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Sample records for presumptive diagnosis based

  1. Health centre versus home presumptive diagnosis of malaria in southern Ghana: implications for home-based care policy.

    Science.gov (United States)

    Dunyo, S K; Afari, E A; Koram, K A; Ahorlu, C K; Abubakar, I; Nkrumah, F K

    2000-01-01

    A study was conducted in 1997 to compare the accuracy of presumptive diagnosis of malaria in children aged 1-9 years performed by caretakers of the children to that of health centre staff in 2 ecological zones in southern Ghana. Similar symptoms were reported in the children at home and at the health centre. In the home setting, symptoms were reported the same day that they occurred, 77.6% of the children with a report of fever were febrile (axillary temperature > or = 37.5 degrees C) and 64.7% of the reports of malaria were parasitologically confirmed. In the health centre, the median duration of symptoms before a child was seen was 3 days (range 1-14 days), 58.5% of the children with a report of fever were febrile and 62.6% of the clinically diagnosed cases were parasitologically confirmed. In the 2 settings almost all the infections were due to Plasmodium falciparum. Parasite density was 3 times higher in the health centre cases compared to the home-diagnosed cases. Early and appropriate treatment of malaria detected in children by caretakers may prevent complications that arise as a result of persistence of symptoms and attainment of high parasitaemic levels.

  2. High diagnostic value of general practitioners' presumptive diagnosis for pyelonephritis, meningitis and pancreatitis.

    Science.gov (United States)

    Sriskandarajah, Srishamanthi; Carter-Storch, Rasmus; Frydkjær-Olsen, Ulrik; Mogensen, Christian Backer

    2016-01-01

    In Denmark, patients referred from the general practitioner (GP) to the emergency department (ED) can be referred with either specific symptoms or with a presumptive diagnosis. The aim of the present study was to evaluate the diagnostic accuracy for various presumptive diagnoses made by the GP in a population acutely referred to an ED. This was a retrospective cohort study of all registered acute referrals for admission to Kolding ED in 2010. Eight presumptive diagnoses were selected for further studies: meningitis, acute coronary syndrome (ACS), pulmonary embolism, pneumonia, pancreatitis, deep venous thrombosis (DVT), pyelonephritis and intestinal obstruction. The presumptive diagnoses were compared with the final diagnosis on discharge. Sensitivity, specificity, predictive values and likelihood ratios were calculated. A total of 8,841 patients were enrolled. The highest and lowest sensitivities were seen for DVT (90%) and meningitis (36%), respectively; and the highest and lowest values for specificity were observed for meningitis (99%) and ACS (30%), respectively. The positive predictive value had a wide range with the lowest value for ACS (9%) and the highest for pneumonia (59%). For pyelonephritis, meningitis and pancreatitis, the likelihood ratio of a positive test was above 10. The likelihood ratio of a negative test was above 0.1 for all diagnoses. Patients referred with the presumptive diagnoses pyelonephritis, meningitis and pancreatitis had a high likelihood of having the disease in question. It is important not to discard any of the included presumptive diagnoses even if the GPs fail to suggest them on admission. none. none.

  3. Case report 471: Hemophilic pseudotumors (presumptive diagnosis) and hemophilic arthropathy of elbow

    International Nuclear Information System (INIS)

    Hermann, G.; Gilbert, M.

    1988-01-01

    A case has been presented of a 72-year-old man on whom an excretory urogram showed the incidental findings of two soft tissue masses in the abdomen containing considerable deposits of calcium. The history was interesting in that the patient was classic hemophiliac with Factor VIII level less than 1%, who first developed symptoms and signs of multiple hemarthroses affecting the knees, ankles, elbows, and shoulders at the age of nine years. Secondary hemophilic arthropathy followed, particularly advanced in the right elbow. Total knee replacements were performed within the last 10 years. A mass within the muscles of the right chest wall, superficial to the ribs, was surgically removed. The abdominal masses in this case were studied with CT and showed considerable calcification with a fibrous wall. Surgical removal of pseudotumors is usually undertaken following diagnosis because the natural history includes continuous enlargement and destruction of the adjacent tissues. Because of the age of the patient and the significant cardiac history, it was considered inappropriate to undertake surgery for the masses in the abdomen which were considered presumptively to be pseudotumors. The clinical, radiological, and pathological aspects of pseudotumor of hemophilia were reviewed. In this case, besides the masses in the abdomen, hemophilic arthropathy of an elbow was illustrated and a soft tissue mass in the right chest wall was demonstrated radiologically and the pathological specimen shown after surgical excision. (orig.)

  4. Case report 471: Hemophilic pseudotumors (presumptive diagnosis) and hemophilic arthropathy of elbow

    Energy Technology Data Exchange (ETDEWEB)

    Hermann, G.; Gilbert, M.

    1988-03-01

    A case has been presented of a 72-year-old man on whom an excretory urogram showed the incidental findings of two soft tissue masses in the abdomen containing considerable deposits of calcium. The history was interesting in that the patient was classic hemophiliac with Factor VIII level less than 1%, who first developed symptoms and signs of multiple hemarthroses affecting the knees, ankles, elbows, and shoulders at the age of nine years. Secondary hemophilic arthropathy followed, particularly advanced in the right elbow. Total knee replacements were performed within the last 10 years. A mass within the muscles of the right chest wall, superficial to the ribs, was surgically removed. The abdominal masses in this case were studied with CT and showed considerable calcification with a fibrous wall. Surgical removal of pseudotumors is usually undertaken following diagnosis because the natural history includes continuous enlargement and destruction of the adjacent tissues. Because of the age of the patient and the significant cardiac history, it was considered inappropriate to undertake surgery for the masses in the abdomen which were considered presumptively to be pseudotumors. The clinical, radiological, and pathological aspects of pseudotumor of hemophilia were reviewed. In this case, besides the masses in the abdomen, hemophilic arthropathy of an elbow was illustrated and a soft tissue mass in the right chest wall was demonstrated radiologically and the pathological specimen shown after surgical excision.

  5. Two Obese Patients with Presumptive Diagnosis of Anaphylactoid Syndrome of Pregnancy Presenting at a Community Hospital.

    Science.gov (United States)

    Kradel, Brian K; Hinson, Scarlett B; Smith, Carr J

    2016-07-01

    Anaphylactoid syndrome of pregnancy (ASP) is a rare but extremely serious complication, with an estimated incidence in North America of 1 in 15 200 deliveries. Despite its rarity, ASP is responsible for approximately 10% of all childbirth-associated deaths in the United States. At present, there is no validated biomarker or specific set of risk factors sufficiently predictive of ASP risk to incorporate into clinical practice. Toward the goal of developing a methodology predictive of an impending ASP event for use by obstetricians, anesthesiologists, and other practitioners participating in infant deliveries, physicians encountering an ASP event have been encouraged to report the occurrence of a case and its biologically plausible risk factors. Herein, we report on 2 patients who presented with a presumptive diagnosis of ASP to the delivery unit of a community hospital. Patient One was a 21-year-old, obese (5'11" tall, 250 lbs., BMI 34.9) white female, 1 pregnancy, no live births (G1P0), estimated gestational age (EGA) 40.2 weeks. Patient Two was a 29-year-old, obese (5'7" tall, 307 lbs., BMI 48.1) Hispanic female, second pregnancy, with 1 previous live birth via C-section (G2P1-0-0-1). Her pregnancy was at gestational age 38 weeks plus 2 days. Patient One had 2 possible risk factors: administration of Pitocin to induce labor and post-coital spotting from recent intercourse. Patient Two suffered premature rupture of the placental membranes. Both Patient One and Patient Two had very high body mass indices (BMIs), at the 97th and 99th percentiles, respectively. In the relatively few cases of anaphylactoid syndrome of pregnancy described to date, this is the first report of a possible association with high BMI.

  6. A PCR-based strategy for simple and rapid identification of rough presumptive Salmonella isolates

    DEFF Research Database (Denmark)

    Hoorfar, Jeffrey; Baggesen, Dorte Lau; Porting, P.H.

    1999-01-01

    The purpose of the present study was to investigate the application of ready-to-go Salmonella PCR tests, based on dry chemistry, for final identification of rough presumptive Salmonella isolates. The results were compared with two different biotyping methods performed at two different laboratories......, which did not result in any DNA band. A total of 32 out of the 36 rough presumptive isolates were positive in the PCR. All but one isolate were also identified as Salmonella by the two biochemical methods. All 80 Salmonella strains were also tested in the two multiplex serogroup tests based on PCR beads....... The sensitivity of the BAX Salmonella PCR test was assessed by testing a total of 80 Salmonella isolates, covering most serogroups, which correctly identified all the Salmonella strains by resulting in one 800-bp band in the sample tubes. The specificity of the PCR was assessed using 20 non-Salmonella strains...

  7. Enhancing TB case detection: experience in offering upfront Xpert MTB/RIF testing to pediatric presumptive TB and DR TB cases for early rapid diagnosis of drug sensitive and drug resistant TB.

    Directory of Open Access Journals (Sweden)

    Neeraj Raizada

    Full Text Available Diagnosis of pulmonary tuberculosis (PTB in children is challenging due to difficulties in obtaining good quality sputum specimens as well as the paucibacillary nature of disease. Globally a large proportion of pediatric tuberculosis (TB cases are diagnosed based only on clinical findings. Xpert MTB/RIF, a highly sensitive and specific rapid tool, offers a promising solution in addressing these challenges. This study presents the results from pediatric groups taking part in a large demonstration study wherein Xpert MTB/RIF testing replaced smear microscopy for all presumptive PTB cases in public health facilities across India.The study covered a population of 8.8 million across 18 programmatic sub-district level tuberculosis units (TU, with one Xpert MTB/RIF platform established at each study TU. Pediatric presumptive PTB cases (both TB and Drug Resistant TB (DR-TB accessing any public health facilities in study area were prospectively enrolled and tested on Xpert MTB/RIF following a standardized diagnostic algorithm.4,600 pediatric presumptive pulmonary TB cases were enrolled. 590 (12.8%, CI 11.8-13.8 pediatric PTB were diagnosed. Overall 10.4% (CI 9.5-11.2 of presumptive PTB cases had positive results by Xpert MTB/RIF, compared with 4.8% (CI 4.2-5.4 who had smear-positive results. Upfront Xpert MTB/RIF testing of presumptive PTB and presumptive DR-TB cases resulted in diagnosis of 79 and 12 rifampicin resistance cases, respectively. Positive predictive value (PPV for rifampicin resistance detection was high (98%, CI 90.1-99.9, with no statistically significant variation with respect to past history of treatment.Upfront access to Xpert MTB/RIF testing in pediatric presumptive PTB cases was associated with a two-fold increase in bacteriologically-confirmed PTB, and increased detection of rifampicin-resistant TB cases under routine operational conditions across India. These results suggest that routine Xpert MTB/RIF testing is a promising

  8. MRI findings in the patients with the presumptive clinical diagnosis of Tolosa-Hunt syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Cakirer, Sinan [Department of Radiology, Neuroradiology Section, Istanbul Sisli Etfal Hospital, 81120 Istanbul (Turkey)

    2003-01-01

    The aim of this study was to present our experience in MRI diagnosis of 23 patients with the clinical findings suggesting Tolosa-Hunt syndrome (THS). Cranial MRI studies of the patients with a clinical history of at least one episode of unilateral or bilateral orbital and periorbital pain, and associated paresis of one or more of third to sixth cranial nerves, were performed on a 1.5-T MRI scanner. Whereas 5 patients had the diagnosis of THS, paracavernous meningiomas in 4 patients, pituitary macroadenomas with cavernous sinus infiltration in 3 patients, Meckel's cave neurinoma in 1 patient, and suprasellar epidermoid in 1 patient were surgically proven MRI findings. Other pathological MRI findings were leptomeningeal metastases in 3 patients, granulomatous pachymeningitis sequelae in 2 patients, and aneurysm with compression on cavernous sinus in 1 patient. Three patients had normal MRI findings. The incidence of radiologically proven diagnosis of THS among the patients with the clinical findings suggesting THS seemed to be low in our study. In conclusion, MRI is the most valuable imaging technique to distinguish THS from other THS-like entities, and permits a precise assessment, management, and therapeutic planning of the underlying pathological conditions. (orig.)

  9. Gastrointestinal stromal tumors as an incidental finding in patients with a presumptive diagnosis of ovarian cancer.

    Science.gov (United States)

    Muñoz, Mario; Ramirez, Pedro T; Echeverri, Carolina; Alvarez, Luis Guillermo; Palomino, Maria Alejandra; Pareja, Luis René

    2012-01-01

    To report the clinical presentation and oncologic outcomes of a series of patients who presented with an abdominal or pelvic mass and were diagnosed with a gastrointestinal stromal tumor (GIST). Data were obtained on all patients who presented with an abdominal or pelvic mass between September 2007 and June 2010 and who were ultimately diagnosed with a GIST. The patients' medical records were reviewed. A literature review was also conducted. Six patients were identified who met the inclusion criteria. All six patients had a tumor in the intestinal tract arising from the small bowel. The mean tumor size was 12 cm (range, 6 to 22 cm). A complete resection was achieved in five of the six patients. There were no intraoperative complications; one patient had a postoperative complication. Two patients were treated with imatinib after surgery. The mean follow-up time was 32 months (range, 0.3 to 40 months). At the last follow-up, five of the six patients were without any evidence of disease. One patient died of an unrelated hepatic encephalopathy. The incidence in our institution is 3%. GISTs are uncommon; however, they should be considered in the differential diagnosis of patients presenting with an abdominal or pelvic mass.

  10. Mucocutaneous Leishmaniasis: clinical markers in presumptive diagnosis Leishmaniose mucosa: marcadores clínicos no diagnóstico presuntivo

    Directory of Open Access Journals (Sweden)

    João Luiz Cioglia Pereira Diniz

    2011-06-01

    Full Text Available Mucocutaneous Leishmaniasis (ML can lead to serious sequela; however, early diagnosis can prevent complications. AIM: To evaluate clinical markers for the early diagnosis of ML. MATERIALS AND METHODS: A series study of 21 cases of ML, which were evaluated through clinical interview, nasal endoscopy, biopsy and the Montenegro test. RESULTS: A skin scar and previous diagnosis of cutaneous leishmaniasis (CL were reported in 8(38% patients, and 13(62% of them denied having had previous CL and had no scar. Nasal/oral symptom onset until the ML diagnosis varied from 5 months to 20 years, mean value of 6 years. In the Montenegro test, the average size of the papule was 14.5 mm, which did not correlate with disease duration (p=0.87. The nose was the most often involved site and the extension of the injured mucosa did not correlate with disease duration. The parasite was found in 2 (9.52% biopsy specimens. CONCLUSIONS: ML diagnosis was late. Finding the parasite in the mucosa, cutaneous scar and/or previous diagnosis of CL were not clinical markers for ML. ML diagnosis must be based on the Montenegro test, chronic nasal and/or oral discharge and histological findings ruling out other granulomatous diseases.A leishmaniose cutâneo-mucosa (LM pode deixar sequelas graves. O diagnóstico precoce evita complicações. OBJETIVO: Avaliar marcadores clínicos para o diagnóstico precoce da LM. MATERIAL E MÉTODO: Estudo de série de 21 casos avaliados com diagnóstico confirmado de LM por meio de entrevista, endoscopia nasal, biópsia e teste cutâneo de Montenegro. RESULTADOS: A cicatriz cutânea ou história de leishmaniose cutânea foram observadas em 8 (38% pacientes e 13(62% negaram terem tido forma cutânea e não tinham cicatriz. O início dos sintomas nasais/orais até a definição do diagnóstico variou de 5 meses a 20 anos, média de 6 anos. No teste de Montenegro, o tamanho médio da pápula foi de 14,5mm e não se correlacionou com a duração da

  11. Presumption of Negligence

    DEFF Research Database (Denmark)

    Guerra, Alice; Luppi, Barbara; Parisi, Francesco

    This paper is about the incentive effects of legal presumptions. We analyze three interrelated effects of legal presumptions in a tort setting: (1) incentives to invest in evidence technology; (2) incentives to invest in care-type precautions; and (3) incentives to mitigate excessive activity lev...

  12. PRESUMPTIVE DIAGNOSIS OF SCHISTOSOMA HAEMATOBIUM ...

    African Journals Online (AJOL)

    boaz

    AFRICAN JOURNAL OF CLINICAL AND EXPERIMENTAL MICROBIOLOGY .... study. Sample Collection and Processing. Two specimen containers were given to each subject and the ... false positive results and 24.3% true negative results.

  13. Performance based fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2002-01-01

    Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

  14. Presumptions respecting mental competence.

    Science.gov (United States)

    Madigan, K V; Checkland, D; Silberfeld, M

    1994-04-01

    This paper addresses the role(s) played by presumptions regarding mental competence in the context of clinical assessment of decision-making capacity. In particular, the issue of whether or not the usual common law presumption of competence is appropriate and applicable in cases of reassessment of persons previously found incompetent is discussed. Arguments can be made for either retaining a presumption of competence or adopting a presumption of incompetence in reassessment cases. In addressing the issue and the arguments, the authors conclude that the question is really a public policy issue which requires legislative resolution. In writing this paper, the authors have drawn on their joint clinical experience at the Baycrest Competency Clinic. Though the authors' jurisdiction is the province of Ontario, their intent is to raise awareness and to prompt consideration of this issue both inside and outside Ontario.

  15. Information Based Fault Diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These ...

  16. 20 CFR 416.933 - How we make a finding of presumptive disability or presumptive blindness.

    Science.gov (United States)

    2010-04-01

    ... disability or presumptive blindness. 416.933 Section 416.933 Employees' Benefits SOCIAL SECURITY... Blindness Presumptive Disability and Blindness § 416.933 How we make a finding of presumptive disability or presumptive blindness. We may make a finding of presumptive disability or presumptive blindness if the...

  17. 20 CFR 404.722 - Rebuttal of a presumption of death.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Rebuttal of a presumption of death. 404.722... DISABILITY INSURANCE (1950- ) Evidence Evidence of Age, Marriage, and Death § 404.722 Rebuttal of a presumption of death. A presumption of death made based on § 404.721(b) can be rebutted by evidence that...

  18. Successful Medical Management of Presumptive Pythium insidiosum Keratitis.

    Science.gov (United States)

    Ramappa, Muralidhar; Nagpal, Ritu; Sharma, Savitri; Chaurasia, Sunita

    2017-04-01

    To describe the previously unreported successful treatment of presumptive Pythium keratitis (PK) with medical therapy alone. A 42-year-old female homemaker presented to us with a 15-day history of pain and redness in the right eye after a trivial injury. Her vision was 20/80 at presentation. Slit-lamp biomicroscopy revealed a central, dense and dry-looking, grayish-white infiltrate reaching mid stroma. The infiltrate had feathery margins and was surrounded by multiple tentacle-like lesions and peripherally expanding pinhead-sized subepithelial lesions. The contralateral eye was essentially normal. Diagnostic corneal scraping on smears revealed broad, aseptate, hyaline filaments with ribbon-like folds; very characteristic of Pythium species. Confocal imaging revealed fungal filaments. Based on corroborative evidence, a diagnosis of presumptive PK was made. She was administered a combination therapy consisting of eye drop linezolid 0.2% 1 hourly, azithromycin 1% 2 hourly, atropine sulfate 1% thrice daily, and oral azithromycin 500 mg once daily for 3 days in a week. After initial worsening in the form of stromal expansion, regression of pinhead-sized lesions was seen with onset of scarring by as early as day 4 of intense medical therapy. The tentacle-like lesions did not worsen. On day 8, significant resolution was noted with scarring, and by the end of 2 weeks, the entire stromal lesion had scarred and complete resolution of expanding tentacles was observed in 3 weeks. Presumptive Pythium keratitis of the patient completely resolved with antibacterial treatment alone. It is pertinent for ophthalmologists to be aware of this new treatment regimen.

  19. 20 CFR 416.931 - The meaning of presumptive disability or presumptive blindness.

    Science.gov (United States)

    2010-04-01

    ... presumptive blindness. 416.931 Section 416.931 Employees' Benefits SOCIAL SECURITY ADMINISTRATION SUPPLEMENTAL SECURITY INCOME FOR THE AGED, BLIND, AND DISABLED Determining Disability and Blindness Presumptive Disability and Blindness § 416.931 The meaning of presumptive disability or presumptive blindness. If you are...

  20. Model-based sensor diagnosis

    International Nuclear Information System (INIS)

    Milgram, J.; Dormoy, J.L.

    1994-09-01

    Running a nuclear power plant involves monitoring data provided by the installation's sensors. Operators and computerized systems then use these data to establish a diagnostic of the plant. However, the instrumentation system is complex, and is not immune to faults and failures. This paper presents a system for detecting sensor failures using a topological description of the installation and a set of component models. This model of the plant implicitly contains relations between sensor data. These relations must always be checked if all the components are functioning correctly. The failure detection task thus consists of checking these constraints. The constraints are extracted in two stages. Firstly, a qualitative model of their existence is built using structural analysis. Secondly, the models are formally handled according to the results of the structural analysis, in order to establish the constraints on the sensor data. This work constitutes an initial step in extending model-based diagnosis, as the information on which it is based is suspect. This work will be followed by surveillance of the detection system. When the instrumentation is assumed to be sound, the unverified constraints indicate errors on the plant model. (authors). 8 refs., 4 figs

  1. Issues in practical model-based diagnosis

    NARCIS (Netherlands)

    Bakker, R.R.; Bakker, R.R.; van den Bempt, P.C.A.; van den Bempt, P.C.A.; Mars, Nicolaas; Out, D.-J.; Out, D.J.; van Soest, D.C.; van Soes, D.C.

    1993-01-01

    The model-based diagnosis project at the University of Twente has been directed at improving the practical usefulness of model-based diagnosis. In cooperation with industrial partners, the research addressed the modeling problem and the efficiency problem in model-based reasoning. Main results of

  2. Auditors' Professional Skepticism: Neutrality versus Presumptive Doubt

    NARCIS (Netherlands)

    Groot, T.L.C.M.; Quadackers, L.M.; Wright, A.

    2014-01-01

    Although skepticism is widely viewed as essential to audit quality, there is a debate about what form is optimal. The two prevailing perspectives that have surfaced are "neutrality" and "presumptive doubt." With neutrality, auditors neither believe nor disbelieve client management. With presumptive

  3. Estudio en niños con diagnóstico presuntivo de toxocariasis en Santa Fe, Argentina Analysis of children with a presumptive diagnosis of toxocariasis in Santa Fe, Argentina

    Directory of Open Access Journals (Sweden)

    Ubaldo O. Martín

    2008-10-01

    the larvae migrate through the capillaries, taking up residence in different tissues. Clinical manifestations are associated with mechanical and/or reaction damage caused by these parasites larvae. Clinical diagnosis is difficult. The method applied in this work is the demonstration of antibodies against the helminth in the blood of children, target host population of this parasitic disease. An ELISA test was performed using T. canis larval excretory-secretory products as antigen. A total of 100 children presumptively diagnosed of toxocariasis that had been derived from different services of the Regional Children’s Hospital for complementary studies, were included in the analysis. The test detected two different populations: infected (59 and non-infected (41. The statistical analysis showed a non significant association between infection and sex (p = 0.279. Infected subjects tended to be older than the non infected (p = 0.009. Eosinophilia was detected in 100% of seropositive children and in 85.2% of the seronegative. There was no significant association between infection and leucocytosis ( = 0.950. The association of these two parameters was significantly higher among infected patients (R = 0.918. Respiratory symptoms and signs were more frequently detected in the positive population (p = 0.05. Dogs tenancy was as frequent among infected as in the non infected homes (p = 0.53. According to these results, prevention, early diagnosis and opportune treatment for toxocariasis should be considered as prioritary health activities in this region.

  4. Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data.

    Science.gov (United States)

    Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong

    2018-02-05

    The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis.

  5. Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

    Science.gov (United States)

    Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong

    2018-01-01

    The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis. PMID:29401730

  6. Knowledge-based diagnosis for aerospace systems

    Science.gov (United States)

    Atkinson, David J.

    1988-01-01

    The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

  7. Presumptive Eligibility for Medicaid and CHIP Coverage

    Data.gov (United States)

    U.S. Department of Health & Human Services — Health care providers and Head Start programs can play a major role in finding and enrolling uninsured children through presumptive eligibility. States can authorize...

  8. Myths, presumptions, and facts about obesity

    DEFF Research Database (Denmark)

    Casazza, Krista; Fontaine, Kevin R; Astrup, Arne

    2013-01-01

    Many beliefs about obesity persist in the absence of supporting scientific evidence (presumptions); some persist despite contradicting evidence (myths). The promulgation of unsupported beliefs may yield poorly informed policy decisions, inaccurate clinical and public health recommendations, and a...

  9. Dengue fever: diagnosis and treatment.

    Science.gov (United States)

    Wiwanitkit, Viroj

    2010-07-01

    Dengue fever is a common tropical infection. This acute febrile illness can be a deadly infection in cases of severe manifestation, causing dengue hemorrhagic shock. In this brief article, I will summarize and discuss the diagnosis and treatment of this disease. For diagnosis of dengue, most tropical doctors make use of presumptive diagnosis; however, the definite diagnosis should be based on immunodiagnosis or viral study. Focusing on treatment, symptomatic and supportive treatment is the main therapeutic approach. The role of antiviral drugs in the treatment of dengue fever has been limited, but is currently widely studied.

  10. 20 CFR 416.934 - Impairments which may warrant a finding of presumptive disability or presumptive blindness.

    Science.gov (United States)

    2010-04-01

    ... Blindness Presumptive Disability and Blindness § 416.934 Impairments which may warrant a finding of presumptive disability or presumptive blindness. We may make findings of presumptive disability and... school, because of mental deficiency or is unable to attend any type of school (or if beyond school age...

  11. Case report 486: Spondyloepiphyseal dysplasia tarda (SDT) (presumptively proved)

    International Nuclear Information System (INIS)

    Brown, D.D.; Childress, M.H.

    1988-01-01

    A 51 year old man with severe degenerative joint disease, short stature, barrel chest deformity, platyspondyly, a narrow pelvis, small iliac bones, dysplastic femoral heads and necks, notching of the patellae and flattening of the femoral intercondylar notches has been described as an example of Spondyloepiphyseal dysplasia tarda SDT. The entity was discussed in detail. The notching of the patellae has not been reported in association with SDT to the authors' knowledge. Characteristic features of SDT allow it to be differentiated from other arthropathies and dysplasias and these distinctions have been emphasized in the discussion. The diagnosis in this case can only be considered presumptively proved. (orig./MG)

  12. Learning-based diagnosis and repair

    NARCIS (Netherlands)

    Roos, Nico

    2017-01-01

    This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus

  13. Approximation Algorithms for Model-Based Diagnosis

    NARCIS (Netherlands)

    Feldman, A.B.

    2010-01-01

    Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation

  14. Cost-Effectiveness Analysis of Test-Based versus Presumptive Treatment of Uncomplicated Malaria in Children under Five Years in an Area of High Transmission in Central Ghana

    DEFF Research Database (Denmark)

    Tawiah, Theresa; Hansen, Kristian Schultz; Baiden, Frank

    2016-01-01

    about household cost incurred on transport, drugs, fees, and special food during a period of one week after the health centre visit as well as days unable to work. A decision model approach was used to calculate the incremental cost-effectiveness ratios (ICERs). Univariate and multivariate sensitivity...... (ACT) in all suspected malaria patients. The use of malaria rapid diagnostic tests (mRDTs) would make it possible for prescribers to diagnose malaria at point-of-care and better target the use of antimalarials. Therefore, a cost-effectiveness analysis was performed on the introduction of m......) or clinical judgement (control) was used to measure the effect of mRDTs on appropriate treatment: ‘a child with a positive reference diagnosis prescribed a course of ACT or a child with a negative reference diagnosis not given an ACT’. Cost data was collected from five purposively selected health centres...

  15. Misleading presumption of a generalized Hartman effect

    International Nuclear Information System (INIS)

    Simanjuntak, Herbert P.; Pereyra, Pedro

    2007-07-01

    We analyze different examples to show that the so-called generalized Hartman effect is an erroneous presumption. The results obtained for electron tunneling and transmission of electromagnetic waves through superlattices and Bragg gratings show clearly the resonant character of the phase time behavior where a generalized Hartman effect is expected. A reinterpretation of the experimental results in double Bragg gratings is proposed. (author)

  16. Microbiological studies of blood specimen from presumptively ...

    African Journals Online (AJOL)

    Three hundred and fifteen blood samples were obtained from presumptively diagnosed typhoid patients who were referred for Widal Serological test at four diagnostic centres. The blood samples were subjected to bacteriological investigations. Salmonella and non-Salmonella organisms isolated were identified according ...

  17. 15 CFR 990.13 - Rebuttable presumption.

    Science.gov (United States)

    2010-01-01

    ...) NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE OIL POLLUTION ACT REGULATIONS... assessment of damages to natural resources made by a Federal, State, or Indian trustee in accordance with this part shall have the force and effect of a rebuttable presumption on behalf of the trustee in any...

  18. Immunodiagnostic confirmation of hydatid disease in patients with a presumptive diagnosis of infection Confirmación inmunodiagnóstica de la hidatidosis en pacientes con diagnóstico presuntivo de la infeccion

    Directory of Open Access Journals (Sweden)

    V. M. Varela-diaz

    1984-04-01

    Full Text Available Information obtained from the routine application of hydatid immunodiagnostic techniques in different clinical situations over a seven-year period is presented. The Immunoelectrophoresis test was used until it was replaced by the simpler, more sensitive and equally specific arc 5 double diffusion (DD5 test. Examination of sera from 1,888 patients with signs and/or symptoms compatible with hydatid disease revealed that the presurgical confirmation of Echinococcus granulosus infection is only obtained by detection of anti-antigen 5 antibodies. The latter were not found in 1,539 presumptive hydatidosis patients whose definitive diagnoses corresponded to other disease conditions. However, false positive latex agglutination test results were obtained in two cases. In all patients whose preoperative serum showed three or more uncharacteristic bands in the absence of anti-antigen 5 antibodies, hydatid cysts were found sur gically. DD5 testing of a fluid sample collected by puncture established its hydatid etiology. Post-operative monitoring of hydatidosis patients demonstrated that persistence of DD5-positivity two years after surgery established the presence of other cysts. Further evidence was obtained in patients with hydatid cysts in intrathoracic, abdominal or other locations associating cyst membrane integrity, antigen release and immunodiagnostic test positivity.Se presenta la información obtenida de la aplicación de las técnicas inmunodiagnósticas para hidatidosis en diferentes situaciones clínicas durante un período de 7 años. Se empleó la prueba de inmunoelectroforesis hasta que se la sustituyó por la prueba de doble difusión arco 5 (DD5, igualmente específica pero de mayor sensibilidad y sencillez. El examen de sueros de 1 888 pacientes con signos y/o sintomas compatibles con la hidatidosis reveló que la confirmación prequirúrgica de la infeccion por Echinococcus granulosus sólo se obtiene mediante la detección de anticuerpos

  19. Organizational Diagnosis in Project-Based Companies

    Directory of Open Access Journals (Sweden)

    Behrouz Zarei

    2014-05-01

    Full Text Available The purpose of this article is to develop a new method for corporate diagnosis (CD. To this end, a method is developed for the diagnosis process of project-based companies. The article presents a case study in a large company where data have been collected through focus groups. Project delay, high project cost, and low profitability are examples of project deficiency in project-based companies. Such issues have made managers pay special attention to find effective solutions to improve them. Prominent factors are inappropriate strategy, structure, system, human resource management, and PMBOK(Project Management Body of Knowledge processes. Thus, CD and analysis is an important task in improvement of corporate performance. The CD model that is developed in this article could be used for project-based companies. The proposed method can be used for CD in any project-based company. This article provides an emphatic application of CD as a prerequisite for restructuring in project-based companies.

  20. A knowledge based system for plant diagnosis

    International Nuclear Information System (INIS)

    Motoda, H.; Yamada, N.; Yoshida, K.

    1984-01-01

    A knowledge based system for plant diagnosis is proposed in which both event-oriented and function-oriented knowledge are used. For the proposed system to be of practical use, these two types of knowledge are represented by mutually nested four frames, i.e. the component, causality, criteriality, and simulator frames, and production rules. The system provides fast inference capability for use as both a production system and a formal reasoning system, with uncertainty of knowledge taken into account in the former. Event-oriented knowledge is used in both diagnosis and guidance and function-oriented knowledge, in diagnosis only. The inference capability required is forward chaining in the former and resolution in the latter. The causality frame guides in the use of event-oriented knowledge, whereas the criteriality frame does so for function-oriented knowledge. Feedback nature of the plant requires the best first search algorithm that uses histories in the resolution process. The inference program is written in Lisp and the plant simulator and the process I/O control programs in Fortran. Fast data transfer between these two languages is realized by enhancing the memory management capability of Lisp to control the numerical data in the global memory. Simulation applications to a BWR plant demonstrated its diagnostic capability

  1. 22 CFR 72.6 - Report of presumptive death.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Report of presumptive death. 72.6 Section 72.6... DEATHS AND ESTATES Reporting Deaths of United States Nationals § 72.6 Report of presumptive death. (a) Local finding. When there is a local finding of presumptive death by a competent local authority, a...

  2. Presumption of Innocence in Criminal Procedure

    Directory of Open Access Journals (Sweden)

    Tatiana Zbanca

    2009-06-01

    Full Text Available Presumption of innocence appears as a rule hardly in modern penal trial. For first timewas noted in legislation from the end of the XVIIIth century (United States of America legislationand Declaration of Human Rights and Citizens in 1789. This constituted a reaction compared toinquisitional report, which practically the one involved into a penal case was presumed alwaysguilty, reverting the obligation of proving own innocence. According to the U.S. Supreme Court,the presumption of the innocence of a criminal defendant is best described as an assumption ofinnocence that is indulged in the absence of contrary evidence. It is not considered evidence of thedefendant's innocence, and it does not require that a mandatory inference favorable to thedefendant be drawn from any facts in evidence.

  3. Presumption of the distribution of the geological structure based on the geological survey and the topographic data in and around the Horonobe area

    International Nuclear Information System (INIS)

    Sakai, Toshihiro; Matsuoka, Toshiyuki

    2015-06-01

    The Horonobe Underground Research Laboratory (URL) Project, a comprehensive research project investigating the deep underground environment in sedimentary rock, is being pursued by the Japan Atomic Energy Agency (JAEA) at Horonobe-cho in Northern Hokkaido, Japan. One of the main goals of the URL project is to establish techniques for investigation, analysis and assessment of the deep geological environment. JAEA constructed the geologic map and the database of geological mapping in Horonobe-cho in 2005 based on the existing literatures and 1/200,000 geologic maps published by Geological Survey of Japan, and then updated the geologic map in 2007 based on the results of various investigations which were conducted around the URL as the surface based investigation phase of the URL project. On the other hand, there are many geological survey data which are derived from natural resources (petroleum, natural gas and coal, etc.) exploration in and around Horonobe-cho. In this report, we update the geologic map and the database of the geological mapping based on these geological survey and topographical analysis data in and around the Horonobe area, and construct a digital geologic map and a digital database of geological mapping as GIS. These data can be expected to improve the precision of modeling and analyzing of geological environment including its long-term evaluation. The digital data is attached on CD-ROM. (J.P.N.)

  4. Randomised primary health center based interventions to improve the diagnosis and treatment of undifferentiated fever and dengue in Vietnam

    NARCIS (Netherlands)

    Phuong, Hoang L.; Nga, Tran T. T.; Giao, Phan T.; Hung, Le Q.; Binh, Tran Q.; Nam, Nguyen V.; Nagelkerke, Nico; de Vries, Peter J.

    2010-01-01

    ABSTRACT: BACKGROUND: Fever is a common reason for attending primary health facilities in Vietnam. Response of health care providers to patients with fever commonly consists of making a presumptive diagnosis and proposing corresponding treatment. In Vietnam, where malaria was brought under control,

  5. Impact of community-based presumptive chloroquine treatment of fever cases on malaria morbidity and mortality in a tribal area in Orissa State, India

    Directory of Open Access Journals (Sweden)

    Sadanandane Candasamy

    2008-05-01

    Full Text Available Abstract Background In the Global Strategy for Malaria Control, one of the basic elements is early detection and prompt treatment of malaria cases, especially in areas where health care facilities are inadequate. Establishing or reviving the existing drug distribution centers (DDC at the peripheral levels of health care can achieve this. The DDCs should be operationally feasible, acceptable by community and technical efficient, particularly in remote hard-core malaria endemic areas. Methods Volunteers from villages were selected for distribution of chloroquine and the selection was made either by villagers or head of the village. The services of the volunteers were absolutely free and voluntary in nature. Chloroquine was provided free of charge to all fever cases. The impact was evaluated based on the changes observed in fever days, fever incidence, parasite incidence and parasite prevalence (proportion of persons harbouring malaria parasite in the community. Comparisons were made between 1st, 2nd and 3rd year of operation in the experimental villages and between the experimental and check areas. Results A total of 411 village volunteers in 378 villages in the experimental community health center with a population of 125,439 treated 88,575 fever cases with a mean annual incidence of 331.8 cases per 1,000 population during the three-year study period. The average morbid days due to fever (AFD was reduced to 1.6 ± 0.1 from 5.9 ± 2.1 in the experimental villages while it remained at 5.0 ± 1.0 in the check villages. There was a significant reduction, (p 0.05. In plain villages that were low endemic, the reductions in AFI and API in experimental villages were statistically significant (p nd and 3rd year when compared with the check area (p 0.0.5. Mortality due to malaria declined by 75% in the experimental villages in the adult age group whereas there was an increasing trend in check villages. Conclusion The study demonstrated that a passive

  6. The presumption of innocence across national borders

    Directory of Open Access Journals (Sweden)

    Lola Shehu

    2018-03-01

    Full Text Available The principle of the presumption of innocence is already an important principle in modern democracies, which have included the principle in their legal systems. Many international instruments also sanction this important principle. The presumption of innocence protects not only the defendant but also the suspect before fi ling charges against him. Human rights are never fully and completely protected. The obligation that state institutions have to respect them does not necessarily mean and in any case guarantee them. For this reason, the material and procedural means envisaged in the legislation of a country are intended to protect the rights of the fundamental rights when the individual has no other way to enjoy them. Violation of fundamental rights can be claimed at every stage of ordinary trial because courts are also obliged to enforce and respect human rights. The practice of the Court in conjunction with Article 6 of the ECHR is basically stated that it has consistently been in the line of the fact that the right to a fair trial occupies an important place in a democratic society in the sense of the European Convention on Human Rights. The right to a fair trial is a very broad right and in any case should be carefully scrutinized by the national courts, analyzing in detail all the facts that, in one form or another, would affect the material or procedural rights of the accused.” (Nowicki, 2003. The right to a fair trial is implemented from the moment of the court’s investment and until the execution of its final decision. The ECHR has emphasized that the principle of the presumption of innocence is considered to be overturned if a judicial decision belonging to a person charged with a criminal offense reflects an opinion that he is guilty before his guilt has been proven by law.

  7. Presumption of Innocence and Public Safety: A Possible Dialogue

    Directory of Open Access Journals (Sweden)

    Ana Aguilar-Garcia

    2014-12-01

    Full Text Available In Mexico, increasing demands for public safety coupled with the need for a more effective criminal justice system resulted in the security and justice constitutional reform of 2008. The outcome was a constitutional framework with provisions based on the highest standards of human rights on the one hand, and on the other, exceptional measures that restrict rights in an attempt to improve public safety. Unfortunately, the crime rate and incidence of unreported crime have changed little. When public safety is demanded, a clear, rational and concrete response is required. Limiting the alternatives to pre-trial detention or increasing penalties is rarely the appropriate response. This paper focuses on pre-trial detention and non-custodial measures supported by the new criminal justice system, how they relate to the principle of the presumption of innocence and the tension between this and the punitive demands for increased imprisonment. In addition, this study discusses a technical solution, found in pre-trial services, which seeks to balance the presumption of innocence and the right to personal liberty with public safety.

  8. Abandoning presumptive antimalarial treatment for febrile children aged less than five years--a case of running before we can walk?

    Directory of Open Access Journals (Sweden)

    Mike English

    2009-01-01

    Full Text Available Current guidelines recommend that all fever episodes in African children be treated presumptively with antimalarial drugs. But declining malarial transmission in parts of sub-Saharan Africa, declining proportions of fevers due to malaria, and the availability of rapid diagnostic tests mean it may be time for this policy to change. This debate examines whether enough evidence exists to support abandoning presumptive treatment and whether African health systems have the capacity to support a shift toward laboratory-confirmed rather than presumptive diagnosis and treatment of malaria in children under five.

  9. Magnetic resonance imaging of presumptive lumbosacral discospondylitis in a dog

    International Nuclear Information System (INIS)

    Kraft, S.L.; Mussman, J.M.; Smith, T.; Biller, D.S.; Hoskinson, J.J.

    1998-01-01

    three-year-old male Boxer dog had hyperesthesia, symmetrical epaxial, gluteal and hind limb muscular atrophy and rear limb ataxia. Neurological deficits included decreased conscious proprioception of the left hind limb, decreased withdrawal and increased patellar reflexes of both hind limbs. The dog had a urinary tract infection with positive culture for Staphylococcus intermedius. On survey radiography of the lumbosacral spine there was active bone proliferation spanning the L7 S1 intervertebral disc space with an epidural filling defect at the ventral aspect of the vertebral canal on epidurography. On magnetic resonance imaging (MRI), findings were similar to those described for human diskospondylitis including altered signal intensity and nonuniform contrast enhancement of the L7-S1 intervertebral disc, adjacent vertebral end plates and epidural and sublumbar soft tissues. Although skeletal radiography is usually sufficient to reach a diagnosis of discospondylitis, MRI of this patient made it possible to reach a presumptive diagnosis of discospondylitis prior to development of definitive radiographic abnormalities

  10. Active fault diagnosis based on stochastic tests

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2008-01-01

    The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output...

  11. The Presumption of Innocence as a Counterfactual Principle

    NARCIS (Netherlands)

    de Jong, F.; van Lent, L.

    This article’s primary aim is to highlight the essentially critical potential of the presumption of innocence, as well as the need for this critical potential to be duly recognized. It is argued that the essential meaning of the presumption of innocence is best understood when approached from what

  12. Failure diagnosis using deep belief learning based health state classification

    International Nuclear Information System (INIS)

    Tamilselvan, Prasanna; Wang, Pingfeng

    2013-01-01

    Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach

  13. Value of polymerase chain reaction in patients with presumptively diagnosed and treated as tuberculous pericardial effusion

    International Nuclear Information System (INIS)

    Rehman, H.; Hafizullah, M.; Shah, S.T.; Khan, S.B.; Hadi, A.; Ahmad, F.; Shah, I.; Gul, A.M.

    2012-01-01

    Objective: To know the sensitivity of polymerase chain reaction (PCR) in pericardial fluid and response to antituberculous treatment (ATT) in PCR positive patients who were presumptively diagnosed and treated as tuberculous pericardial effusion. Methodology: This was a descriptive cross sectional study carried out from June 1, 2009 to 31 May 2010 at Cardiology Department, Lady Reading Hospital, Peshawar. Patients with presumptive diagnosis and receiving treatment for tuberculous pericardial effusion were included. Pericardial fluid sample was aspirated under fluoroscopy for the routine work up. The specimens were subjected to PCR detection of mycobacterium tuberculous DNA. Results: During 12 month study period, a total of 54 patients with large pericardial effusion presented to Cardiology department, Lady Reading Hospital, Peshawar. Of them, 46 patients fulfilled the criteria for presumptive diagnosis of tuberculous pericardial effusion. PCR for mycobacterium tuberculous DNA in pericardial fluid was positive in 45.7%(21). Patients were followed for three months. In PCR positive group, 01 patient while in PCR negative group 3 patients were lost to follow up. Among PCR positive patients 17(85%) while in PCR negative group 11(47.82%) patient responded to ATT both clinically and echo-cardio graphically. We found that patients who were PCR positive responded better to therapy than those who were PCR negative and this finding was statistically significant (p=0.035). Conclusion: PCR, with all its limitations, is potentially a useful diagnostic test in patients with presumptively diagnosed tuberculous pericardial effusion. A PCR positive patient responds better to therapy as compared to PCR negative patient. (author)

  14. New ICPMS based strategies for clinical diagnosis

    International Nuclear Information System (INIS)

    Montes-Bayon, M.; Del Castillo, M.E.; Sanz-Medel, A.

    2009-01-01

    Full text: Glycosylation is the enzymatic process that links saccharides to produce glycans, either free or attached to proteins. This is an enzyme-directed site-specific process, as opposed to the chemical reaction of glycation which is the result of addition of a sugar molecule to a protein or lipid molecule without the controlling action of an enzyme. Both protein modifications, however, can be used as clinical biomarkers for a variety of disorders including chronic alcoholism, diabetes or congenital disorders of glycosylation. The potential of ICPMS as a tool in the diagnosis of such diseases will be illustrated in the presentation. (author)

  15. Analysis of operators' diagnosis tasks based on cognitive process

    International Nuclear Information System (INIS)

    Zhou Yong; Zhang Li

    2012-01-01

    Diagnosis tasks in nuclear power plants characterized as high-dynamic uncertainties are complex reasoning tasks. Diagnosis errors are the main causes for the error of commission. Firstly, based on mental model theory and perception/action cycle theory, a cognitive model for analyzing operators' diagnosis tasks is proposed. Then, the model is used to investigate a trip event which occurred at crystal river nuclear power plant. The application demonstrates typical cognitive bias and mistakes which operators may make when performing diagnosis tasks. They mainly include the strong confirmation tendency, difficulty to produce complete hypothesis sets, group mindset, non-systematic errors in hypothesis testing, and etc. (authors)

  16. Growth characteristics of liquid cultures increase the reliability of presumptive identification of Mycobacterium tuberculosis complex.

    Science.gov (United States)

    Pinhata, Juliana Maira Watanabe; Felippe, Isis Moreira; Gallo, Juliana Failde; Chimara, Erica; Ferrazoli, Lucilaine; de Oliveira, Rosangela Siqueira

    2018-04-23

    We evaluated the microscopic and macroscopic characteristics of mycobacteria growth indicator tube (MGIT) cultures for the presumptive identification of the Mycobacterium tuberculosis complex (MTBC) and assessed the reliability of this strategy for correctly directing isolates to drug susceptibility testing (DST) or species identification. A total of 1526 isolates of mycobacteria received at the Instituto Adolfo Lutz were prospectively subjected to presumptive identification by the observation of growth characteristics along with cord formation detection via microscopy. The presumptive identification showed a sensitivity, specificity and accuracy of 98.8, 92.5 and 97.9 %, respectively. Macroscopic analysis of MTBC isolates that would have been erroneously classified as non-tuberculous mycobacteria based solely on microscopic morphology enabled us to direct them rapidly to DST, representing a substantial gain to patients. In conclusion, the growth characteristics of mycobacteria in MGIT, when considered along with cord formation, increased the reliability of the presumptive identification, which has a great impact on the laboratory budget and turnaround times.

  17. Fault diagnosis based on controller modification

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2015-01-01

    Detection and isolation of parametric faults in closed-loop systems will be considered in this paper. A major problem is that a feedback controller will in general reduce the effects from variations in the systems including parametric faults on the controlled output from the system. Parametric...... faults can be detected and isolated using active methods, where an auxiliary input is applied. Using active methods for the diagnosis of parametric faults in closed-loop systems, the amplitude of the applied auxiliary input need to be increased to be able to detect and isolate the faults in a reasonable......-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify the feedback controller with a minor effect on the closed-loop performance in the fault-free case and at the same time optimize the detection and isolation in a faulty case. Controller modification in connection...

  18. Fault tolerant control based on active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2005-01-01

    An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR......) part. The FTC architecture can be applied for additive faults, parametric faults, and for system structural changes. Only parametric faults will be considered in this paper. The main focus in this paper is on the use of the new approach of active fault diagnosis in connection with FTC. The active fault...... diagnosis approach is based on including an auxiliary input in the system. A fault signature matrix is introduced in connection with AFD, given as the transfer function from the auxiliary input to the residual output. This can be considered as a generalization of the passive fault diagnosis case, where...

  19. MGP site remediation: Working toward presumptive remedies

    International Nuclear Information System (INIS)

    Larsen, B.R.

    1996-01-01

    Manufactured Gas Plants (MGPs) were prevalent in the United States during the 19th and first half of the 20th centuries. MGPs produced large quantities of waste by-products, which varied depending on the process used to manufacture the gas, but most commonly were tars and polynuclear aromatic hydrocarbons. There are an estimated 3,000 to 5,000 abandoned MGP sites across the United States. Because these sites are not concentrated in one geographic location and at least three different manufacturing processes were used, the waste characteristics are very heterogeneous. The question of site remediation becomes how to implement a cost-effective remediation with the variety of cleanup technologies available for these sites. Because of the significant expenditure required for characterization and cleanup of MGP sites, owners and regulatory agencies are beginning to look at standardizing cleanup technologies for these sites. This paper discusses applicable cleanup technologies and the attitude of state regulatory agencies towards the use of presumptive remedies, which can reduce the amount of characterization and detailed analysis necessary for any particular site. Additionally, this paper outlines the process of screening and evaluating candidate technologies, and the progress being made to match the technology to the site

  20. Rule - based Fault Diagnosis Expert System for Wind Turbine

    Directory of Open Access Journals (Sweden)

    Deng Xiao-Wen

    2017-01-01

    Full Text Available Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base.

  1. A Minimalist and Garantistic Conception of the Presumption of Innocence

    Directory of Open Access Journals (Sweden)

    Jordi Ferrer Beltrán

    2018-03-01

    Full Text Available The article aims to address the multiple faces that the presumption of innocence incorporates in modern legal systems from a critical perspective. In this sense, an analytical methodology seeks to demonstrate that some of these faces overlap with other legal rights and institutes, which, far from increasing the guarantees of citizens, leads to confusion and lack of controllability of judicial decisions. Thus, it is defended the conceptual and practical convenience of thinking the presumption of innocence avoiding overlaps with other legal rights or concepts, as standards of proof or burden of proof rules. Hence the reference to a minimalist and guarantistic conception of the presumption of innocence and, and, as will be seen, the defense of the presumption of innocence as a second order rule whose application would make sense in contexts of uncertainty about the satisfaction of the standard of proof.

  2. The Presumption of Innocence as a Counterfactual Principle

    Directory of Open Access Journals (Sweden)

    Ferry de Jong

    2016-01-01

    Full Text Available This article’s primary aim is to highlight the essentially critical potential of the presumption of innocence, as well as the need for this critical potential to be duly recognized. It is argued that the essential meaning of the presumption of innocence is best understood when approached from what is referred to as its counterfactual status. As a first step, the different values and functions that are attributed to the presumption of innocence in contemporary legal literature are discussed, in order to provide an outline of the central ideas it contains or is supposed to contain. Subsequently, the concept of ‘counterfactuality’ is introduced and it is argued that a counterfactual perspective can further clarify the nature of the presumption of innocence. Next, a number of fundamental shifts in society and criminal justice are discussed that affect the presumption of innocence and that lend a large measure of urgency to disclosing its essence and critical potential. The conclusion argues that today’s threats to the presumption of innocence are of a fundamental nature, and that attempts to preserve the principle’s efficacy should focus on the value attached to its counterfactual and critical nature.

  3. Diagnosis of Food Allergy Based on Oral Food Challenge Test

    OpenAIRE

    Komei Ito; Atsuo Urisu

    2009-01-01

    Diagnosis of food allergy should be based on the observation of allergic symptoms after intake of the suspected food. The oral food challenge test (OFC) is the most reliable clinical procedure for diagnosing food allergy. The OFC is also applied for the diagnosis of tolerance of food allergy. The Japanese Society of Pediatric Allergy and Clinical Immunology issued the 'Japanese Pediatric Guideline for Oral Food Challenge Test in Food Allergy 2009' in April 2009, to provide information on a sa...

  4. Disseminated paracoccidioidomycosis diagnosis based on oral lesions

    Directory of Open Access Journals (Sweden)

    Liana Preto Webber

    2014-01-01

    Full Text Available Paracoccidioidomycosis (PCM is a deep mycosis with primary lung manifestations that may present cutaneous and oral lesions. Oral lesions mimic other infectious diseases or even squamous cell carcinoma, clinically and microscopically. Sometimes, the dentist is the first to detect the disease, because lung lesions are asymptomatic, or even misdiagnosed. An unusual case of PCM with 5 months of evolution presenting pulmonary, oral, and cutaneous lesions that was diagnosed by the dentist based on oral lesions is presented and discussed.

  5. Ontology-Based Method for Fault Diagnosis of Loaders.

    Science.gov (United States)

    Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

    2018-02-28

    This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

  6. Process fault diagnosis using knowledge-based systems

    International Nuclear Information System (INIS)

    Sudduth, A.L.

    1991-01-01

    Advancing technology in process plants has led to increased need for computer based process diagnostic systems to assist the operator. One approach to this problem is to use an embedded knowledge based system to interpret measurement signals. Knowledge based systems using only symptom based rules are inadequate for real time diagnosis of dynamic systems; therefore a model based approach is necessary. Though several forms of model based reasoning have been proposed, the use of qualitative causal models incorporating first principles knowledge of process behavior structure, and function appear to have the most promise as a robust modeling methodology. In this paper the structure of a diagnostic system is described which uses model based reasoning and conventional numerical methods to perform process diagnosis. This system is being applied to emergency diesel generator system in nuclear stations

  7. Diagnosis of Food Allergy Based on Oral Food Challenge Test

    Directory of Open Access Journals (Sweden)

    Komei Ito

    2009-01-01

    Full Text Available Diagnosis of food allergy should be based on the observation of allergic symptoms after intake of the suspected food. The oral food challenge test (OFC is the most reliable clinical procedure for diagnosing food allergy. The OFC is also applied for the diagnosis of tolerance of food allergy. The Japanese Society of Pediatric Allergy and Clinical Immunology issued the 'Japanese Pediatric Guideline for Oral Food Challenge Test in Food Allergy 2009' in April 2009, to provide information on a safe and standardized method for administering the OFC. This review focuses on the clinical applications and procedure for the OFC, based on the Japanese OFC guideline.

  8. Bond graph model-based fault diagnosis of hybrid systems

    CERN Document Server

    Borutzky, Wolfgang

    2015-01-01

    This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...

  9. Diagnosis of pregnancy in dairy cows based on the progesterone ...

    African Journals Online (AJOL)

    Diagnosis of pregnancy in dairy cows based on the progesterone content of milk. Part 1. ... best overall classification of dairy cows into pregnant and non-pregnant groups (confirmed by rectal palpation). Progesterone levels ... Teen 'n diskriminante progesteroonwaarde van 5 ng/ml melk het hierdie funksie 98,0% van alle ...

  10. Breath analysis based on micropreconcentrator for early cancer diagnosis

    Science.gov (United States)

    Lee, Sang-Seok

    2018-02-01

    We are developing micropreconcentrators based on micro/nanotechnology to detect trace levels of volatile organic compound (VOC) gases contained in human and canine exhaled breath. The possibility of using exhaled VOC gases as biomarkers for various cancer diagnoses has been previously discussed. For early cancer diagnosis, detection of trace levels of VOC gas is indispensable. Using micropreconcentrators based on MEMS technology or nanotechnology is very promising for detection of VOC gas. A micropreconcentrator based breath analysis technique also has advantages from the viewpoints of cost performance and availability for various cancers diagnosis. In this paper, we introduce design, fabrication and evaluation results of our MEMS and nanotechnology based micropreconcentrators. In the MEMS based device, we propose a flower leaf type Si microstructure, and its shape and configuration are optimized quantitatively by finite element method simulation. The nanotechnology based micropreconcentrator consists of carbon nanotube (CNT) structures. As a result, we achieve ppb level VOC gas detection with our micropreconcentrators and usual gas chromatography system that can detect on the order of ppm VOC in gas samples. In performance evaluation, we also confirm that the CNT based micropreconcentrator shows 115 times better concentration ratio than that of the Si based micropreconcentrator. Moreover, we discuss a commercialization idea for new cancer diagnosis using breath analysis. Future work and preliminary clinical testing in dogs is also discussed.

  11. Heartbeat-based error diagnosis framework for distributed embedded systems

    Science.gov (United States)

    Mishra, Swagat; Khilar, Pabitra Mohan

    2012-01-01

    Distributed Embedded Systems have significant applications in automobile industry as steer-by-wire, fly-by-wire and brake-by-wire systems. In this paper, we provide a general framework for fault detection in a distributed embedded real time system. We use heartbeat monitoring, check pointing and model based redundancy to design a scalable framework that takes care of task scheduling, temperature control and diagnosis of faulty nodes in a distributed embedded system. This helps in diagnosis and shutting down of faulty actuators before the system becomes unsafe. The framework is designed and tested using a new simulation model consisting of virtual nodes working on a message passing system.

  12. Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

    Science.gov (United States)

    Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng

    2018-05-01

    This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.

  13. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

    Directory of Open Access Journals (Sweden)

    Kaijuan Yuan

    2016-01-01

    Full Text Available Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.

  14. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

    Science.gov (United States)

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-07-06

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

  15. Model-based fault diagnosis in PEM fuel cell systems

    Energy Technology Data Exchange (ETDEWEB)

    Escobet, T; de Lira, S; Puig, V; Quevedo, J [Automatic Control Department (ESAII), Universitat Politecnica de Catalunya (UPC), Rambla Sant Nebridi 10, 08222 Terrassa (Spain); Feroldi, D; Riera, J; Serra, M [Institut de Robotica i Informatica Industrial (IRI), Consejo Superior de Investigaciones Cientificas (CSIC), Universitat Politecnica de Catalunya (UPC) Parc Tecnologic de Barcelona, Edifici U, Carrer Llorens i Artigas, 4-6, Planta 2, 08028 Barcelona (Spain)

    2009-07-01

    In this work, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults. (author)

  16. THE PRINCIPLE OF THE PRESUMPTION OF INNOCENCE AND ...

    African Journals Online (AJOL)

    SimenehKA

    The central issue relating to the presumption of innocence and burden of proof in Ethiopia's ... (moral) costs in the application of the substantive law.6 Those moral costs for the acquittal of the ..... (New York: Aspen Law and Business), at 767. ..... Buhagiar,. William (last accessed 26 August 2009).

  17. The Onset of Action of the Presumption of Innocence Principle

    Directory of Open Access Journals (Sweden)

    Igor Y. Murashkin

    2016-04-01

    Full Text Available The author explores the problematic issues of the beginning of the principle of presumption of innocence. Critically evaluate the currently existing position of the origin of the right to protection against unjustified allegations guilty to the crime since the initiation of criminal proceedings. Grounded approach to the beginning of this principle since, when in fact it became prosecute.

  18. Ontology based decision system for breast cancer diagnosis

    Science.gov (United States)

    Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

    2018-04-01

    In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

  19. Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

    Directory of Open Access Journals (Sweden)

    Mingtao Ge

    2017-12-01

    Full Text Available This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT and kernel density estimation classifier (KDEC, which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM classifier and back-propagation (BP neural network classifier.

  20. 75 FR 13051 - Presumptions of Service Connection for Persian Gulf Service

    Science.gov (United States)

    2010-03-18

    ... response to ``RIN 2900-AN24--Presumptions of Service Connection for Persian Gulf Service.'' Copies of...) Cardiovascular signs or symptoms. (12) Abnormal weight loss. (13) Menstrual disorders. (c) Presumptive service...

  1. Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

    Science.gov (United States)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

    2008-03-01

    Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

  2. Community referral for presumptive TB in Nigeria: a comparison of four models of active case finding

    Directory of Open Access Journals (Sweden)

    A. O. Adejumo

    2016-02-01

    Full Text Available Abstract Background Engagement of communities and civil society organizations is a critical part of the Post-2015 End TB Strategy. Since 2007, many models of community referral have been implemented to boost TB case detection in Nigeria. Yet clear insights into the comparative TB yield from particular approaches have been limited. Methods We compared four models of active case finding in three Nigerian states. Data on presumptive TB case referral by community workers (CWs, TB diagnoses among referred clients, active case finding model characteristics, and CWs compensation details for 2012 were obtained from implementers and CWs via interviews and log book review. Self-reported performance data were triangulated against routine surveillance data to assess concordance. Analysis focused on assessing the predictors of presumptive TB referral. Results CWs referred 4–22 % of presumptive TB clients tested, and 4–24 % of the total TB cases detected. The annual median referral per CW ranged widely among the models from 1 to 48 clients, with an overall average of 13.4 referrals per CW. The highest median referrals (48 per CW/yr and mean TB diagnoses (7.1/yr per CW (H =70.850, p < 0.001 was obtained by the model with training supervision, and $80/quarterly payments (Comprehensive Quotas-Oriented model. The model with irregularly supervised, trained, and compensated CWs contributed the least to TB case detection with a median of 13 referrals per CW/yr and mean of 0.53 TB diagnoses per CW/yr. Hours spent weekly on presumptive TB referral made the strongest unique contribution (Beta = 0.514, p < 0.001 to explaining presumptive TB referral after controlling for other variables. Conclusion All community based TB case-finding projects studied referred a relative low number of symptomatic individuals. The study shows that incentivized referral, appropriate selection of CWs, supportive supervision, leveraged treatment support roles, and a

  3. Integrated Knowledge Based Expert System for Disease Diagnosis System

    Science.gov (United States)

    Arbaiy, Nureize; Sulaiman, Shafiza Eliza; Hassan, Norlida; Afizah Afip, Zehan

    2017-08-01

    The role and importance of healthcare systems to improve quality of life and social welfare in a society have been well recognized. Attention should be given to raise awareness and implementing appropriate measures to improve health care. Therefore, a computer based system is developed to serve as an alternative for people to self-diagnose their health status based on given symptoms. This strategy should be emphasized so that people can utilize the information correctly as a reference to enjoy healthier life. Hence, a Web-based Community Center for Healthcare Diagnosis system is developed based on expert system technique. Expert system reasoning technique is employed in the system to enable information about treatment and prevention of the diseases based on given symptoms. At present, three diseases are included which are arthritis, thalassemia and pneumococcal. Sets of rule and fact are managed in the knowledge based system. Web based technology is used as a platform to disseminate the information to users in order for them to optimize the information appropriately. This system will benefit people who wish to increase health awareness and seek expert knowledge on the diseases by performing self-diagnosis for early disease detection.

  4. A hierarchical fuzzy rule-based approach to aphasia diagnosis.

    Science.gov (United States)

    Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

    2007-10-01

    Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.

  5. Imaging diagnosis of Granulocytic Sarcoma in the skull base

    International Nuclear Information System (INIS)

    Zheng Shaoyan; Xie Jiming; Yang Zhiyun; Zhou Zhou; Li Shurong

    2010-01-01

    Objective: To improve the understanding and imaging diagnosis of granulocytic sarcoma in the skull base. Methods: Three cases of granulocytic sarcomas in the skull base are reported. The clinical features and imaging findings were analyzed. Results: The three cases occurred in children with acute myeloid leukemia. Two patients presented with oculomotor paralysis before the diagnosis of leukemia, the third patient with history of leukemia presented with headache. Diffuse infiltration of basal skull bone marrow and extracranial soft tissue masses were shown on MRI. The signal intensities of the masses were similar to that of gray matter on T 1 WI and T 2 WI with marked contrast enhancement. The soft tissue masses were located in the para-sellar region and surrounded the lateral wall of the maxillary sinus in one case. The soft tissue mass of the second case infiltrated the orbital cavity, cavernous sinus and oculomotor nerve. Tumor infiltrating the meninges, cranial nerves and paranasal sinuses was seen in the third patient. Conclusion: Cranial nerve paralysis can be the presenting symptom of basal skull granulocytic sarcoma in children. Granulocytic sarcoma should be considered in the different diagnosis when diffuse abnormal signal intensities in the basal skull bone marrow with solitary or multiple soft tissue masses are shown on MRI. (authors)

  6. Invasive candidiasis: future directions in non-culture based diagnosis.

    Science.gov (United States)

    Posch, Wilfried; Heimdörfer, David; Wilflingseder, Doris; Lass-Flörl, Cornelia

    2017-09-01

    Delayed initial antifungal therapy is associated with high mortality rates caused by invasive candida infections, since accurate detection of the opportunistic pathogenic yeast and its identification display a diagnostic challenge. diagnosis of candida infections relies on time-consuming methods such as blood cultures, serologic and histopathologic examination. to allow for fast detection and characterization of invasive candidiasis, there is a need to improve diagnostic tools. trends in diagnostics switch to non-culture-based methods, which allow specified diagnosis within significantly shorter periods of time in order to provide early and appropriate antifungal treatment. Areas covered: within this review comprise novel pathogen- and host-related testing methods, e.g. multiplex-PCR analyses, T2 magnetic resonance, fungus-specific DNA microarrays, microRNA characterization or analyses of IL-17 as biomarker for early detection of invasive candidiasis. Expert commentary: Early recognition and diagnosis of fungal infections is a key issue for improved patient management. As shown in this review, a broad range of novel molecular based tests for the detection and identification of Candida species is available. However, several assays are in-house assays and lack standardization, clinical validation as well as data on sensitivity and specificity. This underscores the need for the development of faster and more accurate diagnostic tests.

  7. Cost Benefit Analysis of Presumptive Taxation

    OpenAIRE

    Shlomo Yitzhaki

    2007-01-01

    The general idea is the following: any tax authority that respects basic human rights has to impose taxes on a base to avoid random and arbitrary taxation. The tax base should be announced prior to the imposition of the tax and therefore, taxpayers are given an advanced warning concerning the tax base. The advanced warning enables the taxpayers to adjust the tax base to the new circumstances so that they can adjust their behavior to the existence of the tax. This adjustment of the tax base by...

  8. Meckel-Gruber Syndrome: Autopsy Based Approach to Diagnosis

    Directory of Open Access Journals (Sweden)

    Asaranti Kar

    2016-01-01

    Full Text Available Meckel-Gruber syndrome (MGS is a rare lethal congenital malformation affecting 1 in 13,250-140,000 live births. The classical diagnostic triad comprises multicystic dysplastic kidneys, occipital encephalocele, and postaxial polydactyly. It can variably be associated with other malformations such as cleft lip and palate, pulmonary hypoplasia, hepatic fibrosis, and anomalies of central nervous system. A 20 weeks fetus was diagnosed as MGS with classical features along with many other congenital abnormalities such as microcephaly, microphthalmia, hypertelorism, cleft lip and palate, neonatal teeth, and the right side club foot which were detected only after doing autopsy. This case is reported because of its rarity emphasizing the importance of neonatal autopsy in every case of fetal death, especially where the antenatal diagnosis has not been made previously. A systematic approach to accurate diagnosis of MGS based on autopsy will be described here which can allow recurrence risk counseling and proper management in future pregnancies.

  9. PACS-Based Computer-Aided Detection and Diagnosis

    Science.gov (United States)

    Huang, H. K. (Bernie); Liu, Brent J.; Le, Anh HongTu; Documet, Jorge

    The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist's diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

  10. Veterans Affairs: Presumptive Service Connection and Disability Compensation

    Science.gov (United States)

    2011-03-28

    aggravation of disease) and third element (nexus between in-service occurrence/aggravation of disease and current disease) of the prima facie case for...occurring within two years of separation from active duty military service. In the following years, additions to the presumptive list were made by...the change of mission for U.S. forces in Iraq. 4 Veterans Benefits Disability Commission, Honoring the Call to Duty : Veterans’ Disability Benefits in

  11. An expert fitness diagnosis system based on elastic cloud computing.

    Science.gov (United States)

    Tseng, Kevin C; Wu, Chia-Chuan

    2014-01-01

    This paper presents an expert diagnosis system based on cloud computing. It classifies a user's fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user's physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

  12. An Expert Fitness Diagnosis System Based on Elastic Cloud Computing

    Directory of Open Access Journals (Sweden)

    Kevin C. Tseng

    2014-01-01

    Full Text Available This paper presents an expert diagnosis system based on cloud computing. It classifies a user’s fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user’s physiological data, such as age, gender, and body mass index (BMI. In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8% and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

  13. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    Science.gov (United States)

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Model-Based Methods for Fault Diagnosis: Some Guide-Lines

    DEFF Research Database (Denmark)

    Patton, R.J.; Chen, J.; Nielsen, S.B.

    1995-01-01

    This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties.......This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties....

  15. Intelligence system based classification approach for medical disease diagnosis

    Science.gov (United States)

    Sagir, Abdu Masanawa; Sathasivam, Saratha

    2017-08-01

    The prediction of breast cancer in women who have no signs or symptoms of the disease as well as survivability after undergone certain surgery has been a challenging problem for medical researchers. The decision about presence or absence of diseases depends on the physician's intuition, experience and skill for comparing current indicators with previous one than on knowledge rich data hidden in a database. This measure is a very crucial and challenging task. The goal is to predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system. A framework describes methodology for designing and evaluation of classification performances of two discrete ANFIS systems of hybrid learning algorithms least square estimates with Modified Levenberg-Marquardt and Gradient descent algorithms that can be used by physicians to accelerate diagnosis process. The proposed method's performance was evaluated based on training and test datasets with mammographic mass and Haberman's survival Datasets obtained from benchmarked datasets of University of California at Irvine's (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity is examined. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

  16. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

    Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

  17. Cost-effectiveness analysis of rapid diagnostic test, microscopy and syndromic approach in the diagnosis of malaria in Nigeria: implications for scaling-up deployment of ACT

    Directory of Open Access Journals (Sweden)

    Onwujekwe Obinna E

    2009-11-01

    Full Text Available Abstract Background The diagnosis and treatment of malaria is often based on syndromic presentation (presumptive treatment and microscopic examination of blood films. Treatment based on syndromic approach has been found to be costly, and contributes to the development of drug resistance, while microscopic diagnosis of malaria is time-consuming and labour-intensive. Also, there is lack of trained microscopists and reliable equipment especially in rural areas of Nigeria. However, although rapid diagnostic tests (RDTs have improved the ease of appropriate diagnosis of malaria diagnosis, the cost-effectiveness of RDTs in case management of malaria has not been evaluated in Nigeria. The study hence compares the cost-effectiveness of RDT versus syndromic diagnosis and microscopy. Methods A total of 638 patients with fever, clinically diagnosed as malaria (presumptive malaria by health workers, were selected for examination with both RDT and microscopy. Patients positive on RDT received artemisinin-based combination therapy (ACT and febrile patients negative on RDT received an antibiotic treatment. Using a decision tree model for a hypothetical cohort of 100,000 patients, the diagnostic alternatives considered were presumptive treatment (base strategy, RDT and microscopy. Costs were based on a consumer and provider perspective while the outcome measure was deaths averted. Information on costs and malaria epidemiology were locally generated, and along with available data on effectiveness of diagnostic tests, adherence level to drugs for treatment, and drug efficacy levels, cost-effectiveness estimates were computed using TreeAge programme. Results were reported based on costs and effects per strategy, and incremental cost-effectiveness ratios. Results The cost-effectiveness analysis at 43.1% prevalence level showed an incremental cost effectiveness ratio (ICER of 221 per deaths averted between RDT and presumptive treatment, while microscopy is dominated

  18. Knowledge-based fault diagnosis system for refuse collection vehicle

    International Nuclear Information System (INIS)

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-01-01

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle

  19. Knowledge-based fault diagnosis system for refuse collection vehicle

    Energy Technology Data Exchange (ETDEWEB)

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y. [Centre of Advanced Research on Energy, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka (Malaysia)

    2015-05-15

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  20. An Integrated Model-Based Distributed Diagnosis and Prognosis Framework

    Data.gov (United States)

    National Aeronautics and Space Administration — Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation...

  1. Multiplex polymerase chain reaction: Could change diagnosis of ...

    African Journals Online (AJOL)

    acquired infection in critically ill children. The increasing incidence of infections by antibiotic-resistant pathogens adds significantly to the cost of hospital care and to the length of hospital stays. Besides clinical prerequisites for presumptive diagnosis ...

  2. A systems biology-based classifier for hepatocellular carcinoma diagnosis.

    Directory of Open Access Journals (Sweden)

    Yanqiong Zhang

    Full Text Available AIM: The diagnosis of hepatocellular carcinoma (HCC in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71% and area under ROC curve (approximating 1.0, and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.

  3. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  4. Method of fault diagnosis in nuclear power plant base on genetic algorithm and knowledge base

    International Nuclear Information System (INIS)

    Zhou Yangping; Zhao Bingquan

    2000-01-01

    Via using the knowledge base, combining Genetic Algorithm and classical probability and contraposing the characteristic of the fault diagnosis of NPP. The authors put forward a method of fault diagnosis. In the process of fault diagnosis, this method contact the state of NPP with the colony in GA and transform the colony to get the individual that adapts to the condition. On the 950MW full size simulator in Beijing NPP simulation training center, experimentation shows it has comparative adaptability to the imperfection of expert knowledge, illusive signal and other instance

  5. Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology

    Directory of Open Access Journals (Sweden)

    Vollmer Ekkehard

    2008-04-01

    Full Text Available Abstract Background Progress in automated image analysis, virtual microscopy, hospital information systems, and interdisciplinary data exchange require image standards to be applied in tissue-based diagnosis. Aims To describe the theoretical background, practical experiences and comparable solutions in other medical fields to promote image standards applicable for diagnostic pathology. Theory and experiences Images used in tissue-based diagnosis present with pathology – specific characteristics. It seems appropriate to discuss their characteristics and potential standardization in relation to the levels of hierarchy in which they appear. All levels can be divided into legal, medical, and technological properties. Standards applied to the first level include regulations or aims to be fulfilled. In legal properties, they have to regulate features of privacy, image documentation, transmission, and presentation; in medical properties, features of disease – image combination, human – diagnostics, automated information extraction, archive retrieval and access; and in technological properties features of image acquisition, display, formats, transfer speed, safety, and system dynamics. The next lower second level has to implement the prescriptions of the upper one, i.e. describe how they are implemented. Legal aspects should demand secure encryption for privacy of all patient related data, image archives that include all images used for diagnostics for a period of 10 years at minimum, accurate annotations of dates and viewing, and precise hardware and software information. Medical aspects should demand standardized patients' files such as DICOM 3 or HL 7 including history and previous examinations, information of image display hardware and software, of image resolution and fields of view, of relation between sizes of biological objects and image sizes, and of access to archives and retrieval. Technological aspects should deal with image

  6. Fault Features Extraction and Identification based Rolling Bearing Fault Diagnosis

    International Nuclear Information System (INIS)

    Qin, B; Sun, G D; Zhang L Y; Wang J G; HU, J

    2017-01-01

    For the fault classification model based on extreme learning machine (ELM), the diagnosis accuracy and stability of rolling bearing is greatly influenced by a critical parameter, which is the number of nodes in hidden layer of ELM. An adaptive adjustment strategy is proposed based on vibrational mode decomposition, permutation entropy, and nuclear kernel extreme learning machine to determine the tunable parameter. First, the vibration signals are measured and then decomposed into different fault feature models based on variation mode decomposition. Then, fault feature of each model is formed to a high dimensional feature vector set based on permutation entropy. Second, the ELM output function is expressed by the inner product of Gauss kernel function to adaptively determine the number of hidden layer nodes. Finally, the high dimension feature vector set is used as the input to establish the kernel ELM rolling bearing fault classification model, and the classification and identification of different fault states of rolling bearings are carried out. In comparison with the fault classification methods based on support vector machine and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability. (paper)

  7. Diagnosis of Dengue Infection Using Conventional and Biosensor Based Techniques

    Science.gov (United States)

    Parkash, Om; Hanim Shueb, Rafidah

    2015-01-01

    Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed. PMID:26492265

  8. Deterministic versus evidence-based attitude towards clinical diagnosis.

    Science.gov (United States)

    Soltani, Akbar; Moayyeri, Alireza

    2007-08-01

    Generally, two basic classes have been proposed for scientific explanation of events. Deductive reasoning emphasizes on reaching conclusions about a hypothesis based on verification of universal laws pertinent to that hypothesis, while inductive or probabilistic reasoning explains an event by calculation of some probabilities for that event to be related to a given hypothesis. Although both types of reasoning are used in clinical practice, evidence-based medicine stresses on the advantages of the second approach for most instances in medical decision making. While 'probabilistic or evidence-based' reasoning seems to involve more mathematical formulas at the first look, this attitude is more dynamic and less imprisoned by the rigidity of mathematics comparing with 'deterministic or mathematical attitude'. In the field of medical diagnosis, appreciation of uncertainty in clinical encounters and utilization of likelihood ratio as measure of accuracy seem to be the most important characteristics of evidence-based doctors. Other characteristics include use of series of tests for refining probability, changing diagnostic thresholds considering external evidences and nature of the disease, and attention to confidence intervals to estimate uncertainty of research-derived parameters.

  9. Development of a knowledge-based system for loop diagnosis

    International Nuclear Information System (INIS)

    Liao, L.Y.; Tang, H.C.; Chen, S.S.

    1987-01-01

    An accident diagnostic system is developed as an attempt to provide a useful aid for the operators of an experimental loop or a nuclear power plant in the case of emergency condition. Because the current practices in the system diagnosis are not satisfactory, there is an increasing demand on the establishment of various operator decision support systems. The knowledge based system is a new and promising technique which can be used to fulfill this demand. With the capability of automatic reasoning and by incorporating the information about system status, the knowledge based system can simulate the process of human thinking and serve as a good decision support system. This knowledge based decision support system can be helpful for both a fast, violent accident and a slowly developed accident. Specifically, a fast diagnostic report can be provided for a fast and violent accident of which time is the main concern and a complete diagnostic report can be provided for a slowly developed accident of which complexity is the main concern. Such a knowledge based decision support system also provides many other equally important advantages, such as the elimination of human error, the automatic validation of signal readings, the establishment of human error, the automatic validation of signal readings, and the establishment of a simulation environment

  10. High yield of culture-based diagnosis in a TB-endemic setting

    NARCIS (Netherlands)

    Demers, Anne-Marie; Verver, Suzanne; Boulle, Andrew; Warren, Robin; van Helden, Paul; Behr, Marcel A.; Coetzee, David

    2012-01-01

    Background: In most of the world, microbiologic diagnosis of tuberculosis (TB) is limited to microscopy. Recent guidelines recommend culture-based diagnosis where feasible. Methods: In order to evaluate the relative and absolute incremental diagnostic yield of culture-based diagnosis in a

  11. Molecular biology-based diagnosis and therapy for pancreatic cancer

    International Nuclear Information System (INIS)

    Fujita, Hayato; Ohuchida, Kenoki; Mizumoto, Kazuhiro; Tanaka, Masao

    2011-01-01

    Mainly described are author's investigations of the title subject through clinical and basic diagnosis/therapeutic approach. Based on their consideration of carcinogenesis and pathological features of pancreatic cancer (PC), analysis of expression of cancer-related genes in clinically available samples like pancreatic juice and cells biopsied can result in attaining their purposes. Desmoplasia, a pathological feature of PC, possibly induces resistance to therapy and one of strategies is probably its suppression. Targeting stem cells of the mesenchyma as well as those of PC is also a strategy in future. Authors' studies have revealed that quantitation of hTERT (coding teromerase) mRNA levels in PC cells micro-dissected from cytological specimens is an accurate molecular biological diagnostic method applicable clinically. Other cancer-related genes are also useful for the diagnosis and mucin (MUC) family genes are shown to be typical ones for differentiating the precancerous PC, PC and chronic pancreatisis. Efficacy of standard gemcitabine chemotherapy can be individualized with molecular markers concerned to metabolism of the drug like dCK. Radiotherapy/radio-chemotherapy are not so satisfactory for PC treatment now. Authors have found elevated MMP-2 expression and HGF/c-Met signal activation in irradiated PC cells, which can increase the invasive capability; and stimulation of phosphorylation and activation of c-Met/MARK in co-culture of irradiated PC cells with messenchymal cells from PC, which possibly leads to progression of malignancy of PC through their interaction, of which suppression, therefore, can be a new approach to increase the efficacy of radiotherapy. Authors are making effort to introducing adenovirus therapy in clinic; exempli gratia (e.g.), the virus carrying wild type p53, a cancer-suppressive gene, induces apoptosis of PC cells often having its mutated gene. (T.T.)

  12. An intelligent system based on fuzzy probabilities for medical diagnosis – a study in aphasia diagnosis

    Directory of Open Access Journals (Sweden)

    Majid Moshtagh Khorasani

    2009-04-01

    Full Text Available

    • BACKGROUND: Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with  mprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease.
    • METHODS: Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features.
    • RESULTS: Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN  esults as well as author’s earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, espectively, strongly rejecting the null hypothesis.
    • CONCLUSIONS: The technique is applied and compared on both comprehensive and spontaneous speech test data for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features.
    • KEYWORDS: Aphasia, fuzzy probability, fuzzy logic, medical diagnosis, fuzzy rules.

  13. Presumptive risk factors for monkeypox in rural communities in the Democratic Republic of the Congo.

    Directory of Open Access Journals (Sweden)

    Claire A Quiner

    Full Text Available Monkeypox virus (MPXV, a close relative of Variola virus, is a zoonotic virus with an unknown reservoir. Interaction with infected wildlife, bites from peri-domestic animals, and bushmeat hunting are hypothesized routes of infection from wildlife to humans. Using a Risk Questionnaire, performed in monkeypox-affected areas of rural Democratic Republic of the Congo, we describe the lifestyles and demographics associated with presumptive risk factors for MPXV infection. We generated two indices to assess risk: Household Materials Index (HMI, a proxy for socioeconomic status of households and Risk Activity Index (RAI, which describes presumptive risk for animal-to-human transmission of MPXV. Based on participant self-reported activity patterns, we found that people in this population are more likely to visit the forest than a market to fulfill material needs, and that the reported occupation is limited in describing behavior of individuals may participate. Being bitten by rodents in the home was commonly reported, and this was significantly associated with a low HMI. The highest scoring RAI sub-groups were 'hunters' and males aged ≥ 18 years; however, several activities involving MPXV-implicated animals were distributed across all sub-groups. The current analysis may be useful in identifying at-risk groups and help to direct education, outreach and prevention efforts more efficiently.

  14. Calcium-based biomaterials for diagnosis, treatment, and theranostics.

    Science.gov (United States)

    Qi, Chao; Lin, Jing; Fu, Lian-Hua; Huang, Peng

    2018-01-22

    Calcium-based (CaXs) biomaterials including calcium phosphates, calcium carbonates, calcium silicate and calcium fluoride have been widely utilized in the biomedical field owing to their excellent biocompatibility and biodegradability. In recent years, CaXs biomaterials have been strategically integrated with imaging contrast agents and therapeutic agents for various molecular imaging modalities including fluorescence imaging, magnetic resonance imaging, ultrasound imaging or multimodal imaging, as well as for various therapeutic approaches including chemotherapy, gene therapy, hyperthermia therapy, photodynamic therapy, radiation therapy, or combination therapy, even imaging-guided therapy. Compared with other inorganic biomaterials such as silica-, carbon-, and gold-based biomaterials, CaXs biomaterials can dissolve into nontoxic ions and participate in the normal metabolism of organisms. Thus, they offer safer clinical solutions for disease theranostics. This review focuses on the state-of-the-art progress in CaXs biomaterials, which covers from their categories, characteristics and preparation methods to their bioapplications including diagnosis, treatment, and theranostics. Moreover, the current trends and key problems as well as the future prospects and challenges of CaXs biomaterials are also discussed at the end.

  15. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

    Science.gov (United States)

    Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

    2018-03-01

    Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

  16. Space nuclear reactor system diagnosis: Knowledge-based approach

    International Nuclear Information System (INIS)

    Ting, Y.T.D.

    1990-01-01

    SP-100 space nuclear reactor system development is a joint effort by the Department of Energy, the Department of Defense and the National Aeronautics and Space Administration. The system is designed to operate in isolation for many years, and is possibly subject to little or no remote maintenance. This dissertation proposes a knowledge based diagnostic system which, in principle, can diagnose the faults which can either cause reactor shutdown or lead to another serious problem. This framework in general can be applied to the fully specified system if detailed design information becomes available. The set of faults considered herein is identified based on heuristic knowledge about the system operation. The suitable approach to diagnostic problem solving is proposed after investigating the most prevalent methodologies in Artificial Intelligence as well as the causal analysis of the system. Deep causal knowledge modeling based on digraph, fault-tree or logic flowgraph methodology would present a need for some knowledge representation to handle the time dependent system behavior. A proposed qualitative temporal knowledge modeling methodology, using rules with specified time delay among the process variables, has been proposed and is used to develop the diagnostic sufficient rule set. The rule set has been modified by using a time zone approach to have a robust system design. The sufficient rule set is transformed to a sufficient and necessary one by searching the whole knowledge base. Qualitative data analysis is proposed in analyzing the measured data if in a real time situation. An expert system shell - Intelligence Compiler is used to develop the prototype system. Frames are used for the process variables. Forward chaining rules are used in monitoring and backward chaining rules are used in diagnosis

  17. New Diagnosis of AIDS Based on Salmonella enterica subsp. I (enterica Enteritidis (A Meningitis in a Previously Immunocompetent Adult in the United States

    Directory of Open Access Journals (Sweden)

    Andrew C. Elton

    2017-01-01

    Full Text Available Salmonella meningitis is a rare manifestation of meningitis typically presenting in neonates and the elderly. This infection typically associates with foodborne outbreaks in developing nations and AIDS-endemic regions. We report a case of a 19-year-old male presenting with altered mental status after 3-day absence from work at a Wisconsin tourist area. He was febrile, tachycardic, and tachypneic with a GCS of 8. The patient was intubated and a presumptive diagnosis of meningitis was made. Treatment was initiated with ceftriaxone, vancomycin, acyclovir, dexamethasone, and fluid resuscitation. A lumbar puncture showed cloudy CSF with Gram negative rods. He was admitted to the ICU. CSF culture confirmed Salmonella enterica subsp. I (enterica Enteritidis (A. Based on this finding, a 4th-generation HIV antibody/p24 antigen test was sent. When this returned positive, a CD4 count was obtained and showed 3 cells/mm3, confirming AIDS. The patient ultimately received 38 days of ceftriaxone, was placed on elvitegravir, cobicistat, emtricitabine, and tenofovir alafenamide (Genvoya for HIV/AIDS, and was discharged neurologically intact after a 44-day admission.

  18. Diagnosis of asthma: diagnostic testing.

    Science.gov (United States)

    Brigham, Emily P; West, Natalie E

    2015-09-01

    Asthma is a heterogeneous disease, encompassing both atopic and non-atopic phenotypes. Diagnosis of asthma is based on the combined presence of typical symptoms and objective tests of lung function. Objective diagnostic testing consists of 2 components: (1) demonstration of airway obstruction, and (2) documentation of variability in degree of obstruction. A review of current guidelines and literature was performed regarding diagnostic testing for asthma. Spirometry with bronchodilator reversibility testing remains the mainstay of asthma diagnostic testing for children and adults. Repetition of the test over several time points may be necessary to confirm airway obstruction and variability thereof. Repeated peak flow measurement is relatively simple to implement in a clinical and home setting. Bronchial challenge testing is reserved for patients in whom the aforementioned testing has been unrevealing but clinical suspicion remains, though is associated with low specificity. Demonstration of eosinophilic inflammation, via fractional exhaled nitric oxide measurement, or atopy, may be supportive of atopic asthma, though diagnostic utility is limited particularly in nonatopic asthma. All efforts should be made to confirm the diagnosis of asthma in those who are being presumptively treated but have not had objective measurements of variability in the degree of obstruction. Multiple testing modalities are available for objective confirmation of airway obstruction and variability thereof, consistent with a diagnosis of asthma in the appropriate clinical context. Providers should be aware that both these characteristics may be present in other disease states, and may not be specific to a diagnosis of asthma. © 2015 ARS-AAOA, LLC.

  19. All Roads Lead to Fault Diagnosis : Model-Based Reasoning with LYDIA

    NARCIS (Netherlands)

    Feldman, A.B.; Pietersma, J.; Van Gemund, A.J.C.

    2006-01-01

    Model-Based Reasoning (MBR) over qualitative models of complex, real-world systems has proven succesful for automated fault diagnosis, control, and repair. Expressing a system under diagnosis in a formal model and infering a diagnosis given observations are both challenging problems. In this paper

  20. Crack diagnosis of metallic profiles based on structural damage indicators

    International Nuclear Information System (INIS)

    Preisler, A; Schröder, K-U; Steenbock, C

    2015-01-01

    Structural Health Monitoring (SHM) faces several challenges before large-scale industrial application. First of all damage diagnosis has to be reliable. Therefore, common SHM approaches use highly advanced sensor techniques to monitor the whole structure on all possible failures. This results in an enormous amount of data gathered during service. The general effort can be drastically reduced, if the knowledge achieved during the sizing process is used. During sizing, potential failure modes and critical locations, so called hot spots, are already evaluated. A very sensitive SHM system can be developed, when the monitoring effort shifts from the damage to its impact on the structural behaviour and the so called damage indicators. These are the two main components of the SmartSHM approach, which reduces the monitoring effort significantly. Not only the amount of data is minimized, but also reliability and robustness are ensured by the SmartSHM approach.This contribution demonstrates the SmartSHM approach by a cracked four point bending beam. To show general applicability a parametric study considering different profiles (bar, box, I, C, T, L, Z), crack positions and lengths has been performed. Questions of sensitivity and minimum size of the sensor network are discussed based on the results of the parametric study. (paper)

  1. Vitiligo: concise evidence based guidelines on diagnosis and management.

    Science.gov (United States)

    Gawkrodger, David J; Ormerod, Anthony D; Shaw, Lindsay; Mauri-Sole, Inma; Whitton, Maxine E; Watts, M Jane; Anstey, Alex V; Ingham, Jane; Young, Katharine

    2010-08-01

    Vitiligo is a common disease that causes a great degree of psychological distress. In its classical forms it is easily recognised and diagnosed. This review provides an evidence based outline of the management of vitiligo, particularly with the non-specialist in mind. Treatments for vitiligo are generally unsatisfactory. The initial approach to a patient who is thought to have vitiligo is to make a definite diagnosis, offer psychological support, and suggest supportive treatments such as the use of camouflage cosmetics and sunscreens, or in some cases after discussion the option of no treatment. Active therapies open to the non-specialist, after an explanation of potential side effects, include the topical use of potent or highly potent steroids or calcineurin inhibitors for a defined period of time (usually 2 months), following which an assessment is made to establish whether or not there has been a response. Patients whose condition is difficult to diagnose, unresponsive to straightforward treatments, or is causing psychological distress, are usually referred to a dermatologist. Specialist dermatology units have at their disposal phototherapy, either narrow band ultraviolet B or in some cases photochemotherapy, which is the most effective treatment presently available and can be considered for symmetrical types of vitiligo. Depigmenting treatments and possibly surgical approaches may be appropriate for vitiligo in selected cases. There is no evidence that presently available systemic treatments are helpful and safe in vitiligo. There is a need for further research into the causes of vitiligo, and into discovering better treatments.

  2. Theory of sampling and its application in tissue based diagnosis

    Directory of Open Access Journals (Sweden)

    Kayser Gian

    2009-02-01

    Full Text Available Abstract Background A general theory of sampling and its application in tissue based diagnosis is presented. Sampling is defined as extraction of information from certain limited spaces and its transformation into a statement or measure that is valid for the entire (reference space. The procedure should be reproducible in time and space, i.e. give the same results when applied under similar circumstances. Sampling includes two different aspects, the procedure of sample selection and the efficiency of its performance. The practical performance of sample selection focuses on search for localization of specific compartments within the basic space, and search for presence of specific compartments. Methods When a sampling procedure is applied in diagnostic processes two different procedures can be distinguished: I the evaluation of a diagnostic significance of a certain object, which is the probability that the object can be grouped into a certain diagnosis, and II the probability to detect these basic units. Sampling can be performed without or with external knowledge, such as size of searched objects, neighbourhood conditions, spatial distribution of objects, etc. If the sample size is much larger than the object size, the application of a translation invariant transformation results in Kriege's formula, which is widely used in search for ores. Usually, sampling is performed in a series of area (space selections of identical size. The size can be defined in relation to the reference space or according to interspatial relationship. The first method is called random sampling, the second stratified sampling. Results Random sampling does not require knowledge about the reference space, and is used to estimate the number and size of objects. Estimated features include area (volume fraction, numerical, boundary and surface densities. Stratified sampling requires the knowledge of objects (and their features and evaluates spatial features in relation to

  3. Iris features-based heart disease diagnosis by computer vision

    Science.gov (United States)

    Nguchu, Benedictor A.; Li, Li

    2017-07-01

    The study takes advantage of several new breakthroughs in computer vision technology to develop a new mid-irisbiomedical platform that processes iris image for early detection of heart-disease. Guaranteeing early detection of heart disease provides a possibility of having non-surgical treatment as suggested by biomedical researchers and associated institutions. However, our observation discovered that, a clinical practicable solution which could be both sensible and specific for early detection is still lacking. Due to this, the rate of majority vulnerable to death is highly increasing. The delayed diagnostic procedures, inefficiency, and complications of available methods are the other reasons for this catastrophe. Therefore, this research proposes the novel IFB (Iris Features Based) method for diagnosis of premature, and early stage heart disease. The method incorporates computer vision and iridology to obtain a robust, non-contact, nonradioactive, and cost-effective diagnostic tool. The method analyzes abnormal inherent weakness in tissues, change in color and patterns, of a specific region of iris that responds to impulses of heart organ as per Bernard Jensen-iris Chart. The changes in iris infer the presence of degenerative abnormalities in heart organ. These changes are precisely detected and analyzed by IFB method that includes, tensor-based-gradient(TBG), multi orientations gabor filters(GF), textural oriented features(TOF), and speed-up robust features(SURF). Kernel and Multi class oriented support vector machines classifiers are used for classifying normal and pathological iris features. Experimental results demonstrated that the proposed method, not only has better diagnostic performance, but also provides an insight for early detection of other diseases.

  4. Measurement of radiocesium concentration in trees using cumulative gamma radiation dose rate detection systems - A simple presumption for radiocesium concentration in living woods using glass-badge based gamma radiation dose rate detection system

    Energy Technology Data Exchange (ETDEWEB)

    Yoshihara, T.; Hashida, S.N. [Plant Molecular Biology, Laboratory of Environmental Science, Central Research Institute of Electric Power Industry (CRIEPI), 1646 Abiko, Chiba 270-1194 (Japan); Kawachi, N.; Suzui, N.; Yin, Y.G.; Fujimaki, S. [Radiotracer Imaging Gr., Quantum Beam Science Center, Japan Atomic Energy Agency (JAEA), 1233 Watanuki, Takasaki, Gunma 370-1292 (Japan); Nagao, Y.; Yamaguchi, M. [Takasaki Advanced Radiation Research Institute, Japan Atomic Energy Agency (JAEA), 1233 Watanuki, Takasaki, Gunma 370-1292 (Japan)

    2014-07-01

    Radiocesium from the severe accident at the Fukushima Dai-ichi Nuclear Power Plant on 11 March 2011 contaminates large areas. After this, a doubt for forest products, especially of mushroom, is indelible at the areas. Pruned woody parts and litters are containing a considerable amount of radiocesium, and generates a problem at incineration and composting. These mean that more attentive survey for each subject is expected; however, the present survey system is highly laborious/expensive and/or non-effective for this purpose. On the other hand, we can see a glass-badge based gamma radiation dose rate detection system. This system always utilized to detect a personal cumulative radiation dose, and thus, it is not suitable to separate a radiation from a specific object. However, if we can separate a radiation from a specific object and relate it with the own radiocesium concentration, it would enable us to presume the specific concentration with just an easy monitoring but without a destruction of the target nature and a complicated process including sampling, pre-treatment, and detection. Here, we present the concept of the measurement and results of the trials. First, we set glass-badges (type FS, Chiyoda Technol Corp., Japan) on a part of bough (approximately 10 cm in diameter) of Japanese flowering cherry trees (Prunus x yedoensis cv. Somei-Yoshino) with four different settings: A, a direct setting without any shield; B, a setting with an aluminum shield between bough and the glass-badge; C, a setting with a lead shield between bough and the glass-badge; D, a setting with a lead shield covering the glass-badge to shut the radiation from the surrounding but from bough. The deduction between the amount of each setting should separate a specific radiation of the bough from unlimited radiation from the surrounding. Even if the hourly dose rate is not enough to count the difference, a moderate cumulative dose would clear the difference. In fact, results demonstrated a

  5. The Knowledge Base and Acceptability of Prenatal Diagnosis by ...

    African Journals Online (AJOL)

    AJRH Managing Editor

    African Journal of Reproductive Health December 2014; 18(1): 127. ORIGINAL RESEARCH ... unskilled and self-employed positively influenced attitude towards prenatal diagnosis. .... exercise and volume of water intake per day, willingness ...

  6. Nuclear Power Plants Fault Diagnosis Method Based on Data Fusion

    International Nuclear Information System (INIS)

    Xie Chunli; Liu Yongkuo; Xia Hong

    2009-01-01

    The data fusion is a method suit for complex system fault diagnosis such as nuclear power plants, which is multisource information processing technology. This paper uses data fusion information hierarchical thinking and divides nuclear power plants fault diagnosis into three levels. Data level adopts data mining method to handle data and reduction attributes. Feature level uses three parallel neural networks to deal with attributes of data level reduction and the outputs of three networks are as the basic probability assignment of Dempster-Shafer (D-S) evidence theory. The improved D-S evidence theory synthesizes the outputs of neural networks in decision level, which conquer the traditional D-S evidence theory limitation which can't dispose conflict information. The diagnosis method was tested using correlation data of literature. The test results indicate that the data fusion diagnosis system can diagnose nuclear power plants faults accurately and the method has application value. (authors)

  7. 77 FR 12522 - Tentative Eligibility Determinations; Presumptive Eligibility for Psychosis and Other Mental Illness

    Science.gov (United States)

    2012-03-01

    ...; Presumptive Eligibility for Psychosis and Other Mental Illness AGENCY: Department of Veterans Affairs. ACTION... psychosis within specified time periods and for Persian Gulf War veterans who developed a mental illness... eligibility determinations; Presumptive eligibility for psychosis and other mental illness.'' Copies of...

  8. Presumptive Ischemic Brain Infarction in a Dog with Evans’ Syndrome

    Directory of Open Access Journals (Sweden)

    Angelo Pasquale Giannuzzi

    2014-01-01

    Full Text Available A ten-year-old neutered female mixed breed dog was referred for pale mucous membrane and acute onset of right prosencephalic clinical signs. Brain magnetic resonance imaging was suggestive for right middle cerebral artery ischemic stroke. Based on cell blood count, serum biochemistry and serologic tests and flow cytometric detection of anti-platelets and anti-red blood cells antibodies, a diagnosis of immunomediated haemolytic anemia associated with thrombocytopenia of suspected immunomediated origin was done. Immunosuppresive therapy with prednisone was started and the dog clinically recovered. Two months later complete normalization of CBC and serum biochemistry was documented. The dog remained stable for 7 months without therapy; then she relapsed. CBC revealed mild regenerative anemia with spherocytosis and thrombocytopenia. A conclusive Evans’ syndrome diagnosis was done and prednisone and cyclosporine treatment led to normalization of physical and CBC parameters. The dog is still alive at the time the paper submitted. Possible thrombotic etiopathogenetic mechanisms are illustrated in the paper and the authors suggest introducing Evans’ syndrome in the differential diagnosis list for brain ischemic stroke in dogs.

  9. Combustion engine diagnosis model-based condition monitoring of gasoline and diesel engines and their components

    CERN Document Server

    Isermann, Rolf

    2017-01-01

    This book offers first a short introduction to advanced supervision, fault detection and diagnosis methods. It then describes model-based methods of fault detection and diagnosis for the main components of gasoline and diesel engines, such as the intake system, fuel supply, fuel injection, combustion process, turbocharger, exhaust system and exhaust gas aftertreatment. Additionally, model-based fault diagnosis of electrical motors, electric, pneumatic and hydraulic actuators and fault-tolerant systems is treated. In general series production sensors are used. It includes abundant experimental results showing the detection and diagnosis quality of implemented faults. Written for automotive engineers in practice, it is also of interest to graduate students of mechanical and electrical engineering and computer science. The Content Introduction.- I SUPERVISION, FAULT DETECTION AND DIAGNOSIS METHODS.- Supervision, Fault-Detection and Fault-Diagnosis Methods - a short Introduction.- II DIAGNOSIS OF INTERNAL COMBUST...

  10. USE OF PRESUMPTIVE TAXATION IN FACILITATING SMALL BUSINESS TAX COMPLIANCE

    Directory of Open Access Journals (Sweden)

    Victoria IORDACHI

    2016-07-01

    Full Text Available The actuality of this article is determined by the necessity of implementing fiscal simplicity for increasing tax compliance through fiscal education of small business representatives. In many developing and transition countries, micro and small enterprises are the most rapidly growing business segment. Tax compliance attitude within this sector varies significantly because high conformation costs and difficult formalization procedures can determine many small enterprises to operate in the informal economy. Thus tax regulation of small enterprises is crucial in the process of small entrepreneurs fiscal education and tax simplification of SMEs in many countries becomes one of the most efficient instruments. The main research methods were systemic analysis and logic synthesis. The main results obtained in article, as a result of research, are identification, analysis and systematization of foreign countries’ practices in implementing presumptive tax design and elaboration of some recommendations on fiscal simplicity.

  11. Fuzzy Concurrent Object Oriented Expert System for Fault Diagnosis in 8085 Microprocessor Based System Board

    OpenAIRE

    Mr.D. V. Kodavade; Dr. Mrs.S.D.Apte

    2014-01-01

    With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations...

  12. Distributed bearing fault diagnosis based on vibration analysis

    Science.gov (United States)

    Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

    2016-01-01

    Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

  13. Criminalisation of the Muslim community and the fight for the presumption of innocence

    Directory of Open Access Journals (Sweden)

    Iker Barbero González

    2017-04-01

    Full Text Available In parallel to the strategy of neo-Orientalising the Muslim community in Europe, acts of resistance emerge to condemn it. This article considers neo-Orientalisation not only as a strategy for exoticising and/or undermining the community, it demonstrates that it may be understood as an “agonistic Government strategy”. To this end, the paper presents the case of the “Raval 11” and bases its analysis on the interpretation of the resistance by family and activists to the arrests of 11 Pakistanis and Indians on charges of terrorism in Barcelona in 2008 as “acts of citizenship”. New political subjects were engaged: women, young people and children burst onto the scene demanding both freedom and the presumption of innocence for their relatives and dignity for the wider Muslim and migrant community criminalised by the dominant political and media discourses.

  14. Stochastic Change Detection based on an Active Fault Diagnosis Approach

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2007-01-01

    The focus in this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow to obtain a fast change detection/isolation by considering the output or an err...

  15. A Textual Case-Based Mobile Phone Diagnosis Support System ...

    African Journals Online (AJOL)

    In this paper, a Mobile Phone Diagnosis Support System is presented as an extension to jCOLIBRI which accepts a problem and reasons with cases to provide a solution related to a new given problem. Experimental evaluation using some set of problems shows that the developed system predicts the solution that is ...

  16. A Cough-Based Algorithm for Automatic Diagnosis of Pertussis

    Science.gov (United States)

    Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

    2016-01-01

    Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. PMID:27583523

  17. 38 CFR 1.18 - Guidelines for establishing presumptions of service connection for former prisoners of war.

    Science.gov (United States)

    2010-07-01

    ... establishing presumptions of service connection for former prisoners of war. 1.18 Section 1.18 Pensions... Guidelines for establishing presumptions of service connection for former prisoners of war. (a) Purpose. The Secretary of Veterans Affairs will establish presumptions of service connection for former prisoners of war...

  18. Sensor fault diagnosis of aero-engine based on divided flight status.

    Science.gov (United States)

    Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

    2017-11-01

    Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

  19. Sensor fault diagnosis of aero-engine based on divided flight status

    Science.gov (United States)

    Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

    2017-11-01

    Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

  20. Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

    DEFF Research Database (Denmark)

    Huang, Sijia; Chong, Nicole; Lewis, Nathan

    2016-01-01

    diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. Results: The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under.......993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. Conclusions: We have successfully developed a new type of pathway-based model to study...... metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease...

  1. Gear Fault Diagnosis Based on BP Neural Network

    Science.gov (United States)

    Huang, Yongsheng; Huang, Ruoshi

    2018-03-01

    Gear transmission is more complex, widely used in machinery fields, which form of fault has some nonlinear characteristics. This paper uses BP neural network to train the gear of four typical failure modes, and achieves satisfactory results. Tested by using test data, test results have an agreement with the actual results. The results show that the BP neural network can effectively solve the complex state of gear fault in the gear fault diagnosis.

  2. Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance

    Directory of Open Access Journals (Sweden)

    Detang Zeng

    2018-01-01

    Full Text Available It is essential to monitor and to diagnose faults in rotating machinery with a high thrust–weight ratio and complex structure for a variety of industrial applications, for which reliable signal measurements are required. However, the measured values consist of the true values of the parameters, the inertia of measurements, random errors and systematic errors. Such signals cannot reflect the true performance state and the health state of rotating machinery accurately. High-quality, steady-state measurements are necessary for most current diagnostic methods. Unfortunately, it is hard to obtain these kinds of measurements for most rotating machinery. Diagnosis based on transient performance is a useful tool that can potentially solve this problem. A model-based fault diagnosis method for gas turbines based on transient performance is proposed in this paper. The fault diagnosis consists of a dynamic simulation model, a diagnostic scheme, and an optimization algorithm. A high-accuracy, nonlinear, dynamic gas turbine model using a modular modeling method is presented that involves thermophysical properties, a component characteristic chart, and system inertial. The startup process is simulated using this model. The consistency between the simulation results and the field operation data shows the validity of the model and the advantages of transient accumulated deviation. In addition, a diagnostic scheme is designed to fulfill this process. Finally, cuckoo search is selected to solve the optimization problem in fault diagnosis. Comparative diagnostic results for a gas turbine before and after washing indicate the improved effectiveness and accuracy of the proposed method of using data from transient processes, compared with traditional methods using data from the steady state.

  3. MDD diagnosis based on partial-brain functional connection network

    Science.gov (United States)

    Yan, Gaoliang; Hu, Hailong; Zhao, Xiang; Zhang, Lin; Qu, Zehui; Li, Yantao

    2018-04-01

    Artificial intelligence (AI) is a hotspot in computer science research nowadays. To apply AI technology in all industries has been the developing direction for researchers. Major depressive disorder (MDD) is a common disease of serious mental disorders. The World Health Organization (WHO) reports that MDD is projected to become the second most common cause of death and disability by 2020. At present, the way of MDD diagnosis is single. Applying AI technology to MDD diagnosis and pathophysiological research will speed up the MDD research and improve the efficiency of MDD diagnosis. In this study, we select the higher degree of brain network functional connectivity by statistical methods. And our experiments show that the average accuracy of Logistic Regression (LR) classifier using feature filtering reaches 88.48%. Compared with other classification methods, both the efficiency and accuracy of this method are improved, which will greatly improve the process of MDD diagnose. In these experiments, we also define the brain regions associated with MDD, which plays a vital role in MDD pathophysiological research.

  4. Study of fault diagnosis software design for complex system based on fault tree

    International Nuclear Information System (INIS)

    Yuan Run; Li Yazhou; Wang Jianye; Hu Liqin; Wang Jiaqun; Wu Yican

    2012-01-01

    Complex systems always have high-level reliability and safety requirements, and same does their diagnosis work. As a great deal of fault tree models have been acquired during the design and operation phases, a fault diagnosis method which combines fault tree analysis with knowledge-based technology has been proposed. The prototype of fault diagnosis software has been realized and applied to mobile LIDAR system. (authors)

  5. Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

    OpenAIRE

    Yuehai Wang; Yongzheng Yan; Qinyong Wang

    2016-01-01

    Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In ...

  6. Mixed Ehrlichia canis, Hepatozoon canis, and presumptive Anaplasma phagocytophilum infection in a dog.

    Science.gov (United States)

    Mylonakis, Mathio E; Koutinas, Alex F; Baneth, Gad; Polizopoulou, Zoe; Fytianou, Anna

    2004-01-01

    A 5-month-old, female, mongrel dog was admitted to the Clinic of Companion Animal Medicine, Aristotle University of Thessaloniki, Greece, with depression, anorexia, fever, peripheral lymphadenopathy, splenomegaly, oculonasal discharge, nonregenerative anemia, and mild thrombocytopenia. Cytology of Giemsa-stained buffy coat, bone marrow, and lymph node aspiration smears revealed numerous morulae in mononuclear leukocytes and in neutrophils, and Hepatozoon canis gamonts in neutrophils. The dog was seropositive to Ehrlichia canis (immunofluorescence assay [IFA]) and Hepatozoon canis (ELISA) but not to Anaplasma phagocytophilum (IFA). A nested polymerase chain reaction performed on bone marrow aspirates was positive for E canis. This method was not applied for the detection of A phagocytophilum. Treatment with doxycycline and imidocarb dipropionate resulted in both clinical and parasitologic cure. This is the first reported case of a mixed infection with E canis, H canis, and presumptive A phagocytophilum. The findings emphasize the value of cytology in offering a quick and inexpensive diagnosis in mixed tick-borne infections of dogs.

  7. Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

    Science.gov (United States)

    Daigle, Matthew; Roychoudhury, Indranil

    2010-01-01

    We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

  8. Aided diagnosis methods of breast cancer based on machine learning

    Science.gov (United States)

    Zhao, Yue; Wang, Nian; Cui, Xiaoyu

    2017-08-01

    In the field of medicine, quickly and accurately determining whether the patient is malignant or benign is the key to treatment. In this paper, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression were applied to predict the classification of thyroid,Her-2,PR,ER,Ki67,metastasis and lymph nodes in breast cancer, in order to recognize the benign and malignant breast tumors and achieve the purpose of aided diagnosis of breast cancer. The results showed that the highest classification accuracy of LDA was 88.56%, while the classification effect of KNN and Logistic Regression were better than that of LDA, the best accuracy reached 96.30%.

  9. Assisted Diagnosis Research Based on Improved Deep Autoencoder

    Directory of Open Access Journals (Sweden)

    Ke Zhang-Han

    2017-01-01

    Full Text Available Deep Autoencoder has the powerful ability to learn features from large number of unlabeled samples and a small number of labeled samples. In this work, we have improved the network structure of the general deep autoencoder and applied it to the disease auxiliary diagnosis. We have achieved a network by entering the specific indicators and predicting whether suffering from liver disease, the network using real physical examination data for training and verification. Compared with the traditional semi-supervised machine learning algorithm, deep autoencoder will get higher accuracy.

  10. Parent-based diagnosis of ADHD is as accurate as a teacher-based diagnosis of ADHD.

    Science.gov (United States)

    Bied, Adam; Biederman, Joseph; Faraone, Stephen

    2017-04-01

    To review the literature evaluating the psychometric properties of parent and teacher informants relative to a gold-standard ADHD diagnosis in pediatric populations. We included studies that included both a parent and teacher informant, a gold-standard diagnosis, and diagnostic accuracy metrics. Potential confounds were evaluated. We also assessed the 'OR' and the 'AND' rules for combining informant reports. Eight articles met inclusion criteria. The diagnostic accuracy for predicting gold standard ADHD diagnoses did not differ between parents and teachers. Sample size, sample type, participant drop-out, participant age, participant gender, geographic area of the study, and date of study publication were assessed as potential confounds. Parent and teachers both yielded moderate to good diagnostic accuracy for ADHD diagnoses. Parent reports were statistically indistinguishable from those of teachers. The predictive features of the 'OR' and 'AND' rules are useful in evaluating approaches to better integrating information from these informants.

  11. Should the diagnosis of COPD be based on a single spirometry test?

    NARCIS (Netherlands)

    Schermer, T.R.; Robberts, B.; Crockett, A.J.; Thoonen, B.P.; Lucas, A.; Grootens, J.; Smeele, I.J.; Thamrin, C.; Reddel, H.K.

    2016-01-01

    Clinical guidelines indicate that a chronic obstructive pulmonary disease (COPD) diagnosis is made from a single spirometry test. However, long-term stability of diagnosis based on forced expiratory volume in 1 s over forced vital capacity (FEV1/FVC) ratio has not been reported. In primary care

  12. A fuzzy-logic based diagnosis and control of a reactor performing complete autotrophic nitrogen removal

    DEFF Research Database (Denmark)

    Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist

    2013-01-01

    Diagnosis and control modules based on fuzzy set theory were tested for novel bioreactor monitoring and control. Two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation in d...... autotrophic nitrogen removal process. The whole module is evaluated by dynamic simulation....

  13. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    KAUST Repository

    Busbait, Monther I.

    2014-01-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum

  14. Comparative evaluation of six chromogenic media for presumptive yeast identification.

    Science.gov (United States)

    Vecchione, Alessandra; Florio, Walter; Celandroni, Francesco; Barnini, Simona; Lupetti, Antonella; Ghelardi, Emilia

    2017-12-01

    The present study was undertaken to evaluate the discrimination ability of six chromogenic media in presumptive yeast identification. We analysed 108 clinical isolates and reference strains belonging to eight different species: Candida albicans , Candida dubliniensis , Candida tropicalis , Candida krusei , Candida glabrata , Candida parapsilosis , Candida lusitaniae and Trichosporon mucoides . C. albicans , C. tropicalis and C. krusei could be distinguished from one another in all the tested chromogenic media, as predicted by the manufacturers. In addition, C. albicans could be distinguished from C. dubliniensis on BBL CHROMagar Candida, Kima CHROMagar Candida and Brilliance Candida, and C. parapsilosis could be identified on CHROMATIC Candida agar, CHROMOGENIC Candida agar, and Brilliance Candida agar. Brilliance Candida provided the widest discrimination ability, being able to discriminate five out of the seven Candida species tested. Interestingly, C. tropicalis and C. krusei could be already distinguished from each other after 24 hours of incubation. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  15. Fault diagnosis model for power transformers based on information fusion

    Science.gov (United States)

    Dong, Ming; Yan, Zhang; Yang, Li; Judd, Martin D.

    2005-07-01

    Methods used to assess the insulation status of power transformers before they deteriorate to a critical state include dissolved gas analysis (DGA), partial discharge (PD) detection and transfer function techniques, etc. All of these approaches require experience in order to correctly interpret the observations. Artificial intelligence (AI) is increasingly used to improve interpretation of the individual datasets. However, a satisfactory diagnosis may not be obtained if only one technique is used. For example, the exact location of PD cannot be predicted if only DGA is performed. However, using diverse methods may result in different diagnosis solutions, a problem that is addressed in this paper through the introduction of a fuzzy information infusion model. An inference scheme is proposed that yields consistent conclusions and manages the inherent uncertainty in the various methods. With the aid of information fusion, a framework is established that allows different diagnostic tools to be combined in a systematic way. The application of information fusion technique for insulation diagnostics of transformers is proved promising by means of examples.

  16. 2D nanomaterials based electrochemical biosensors for cancer diagnosis.

    Science.gov (United States)

    Wang, Lu; Xiong, Qirong; Xiao, Fei; Duan, Hongwei

    2017-03-15

    Cancer is a leading cause of death in the world. Increasing evidence has demonstrated that early diagnosis holds the key towards effective treatment outcome. Cancer biomarkers are extensively used in oncology for cancer diagnosis and prognosis. Electrochemical sensors play key roles in current laboratory and clinical analysis of diverse chemical and biological targets. Recent development of functional nanomaterials offers new possibilities of improving the performance of electrochemical sensors. In particular, 2D nanomaterials have stimulated intense research due to their unique array of structural and chemical properties. The 2D materials of interest cover broadly across graphene, graphene derivatives (i.e., graphene oxide and reduced graphene oxide), and graphene-like nanomaterials (i.e., 2D layered transition metal dichalcogenides, graphite carbon nitride and boron nitride nanomaterials). In this review, we summarize recent advances in the synthesis of 2D nanomaterials and their applications in electrochemical biosensing of cancer biomarkers (nucleic acids, proteins and some small molecules), and present a personal perspective on the future direction of this area. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform.

    Science.gov (United States)

    Pang, Bin; Tang, Guiji; Tian, Tian; Zhou, Chong

    2018-04-14

    When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time-time (IHTT) transform, by combining a Hilbert time-time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures.

  18. A study on group decision-making based fault multi-symptom-domain consensus diagnosis

    International Nuclear Information System (INIS)

    He Yongyong; Chu Fulei; Zhong Binglin

    2001-01-01

    In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. In this paper, in order to utilize multiple symptom domains to improve the diagnosis accuracy, an idea of fault multi-symptom-domain consensus diagnosis is developed. From the point of view of the group decision-making, two particular multi-symptom-domain diagnosis strategies are proposed. The proposed strategies use BP (Back-Propagation) neural networks as diagnosis models in various symptom domains, and then combine the outputs of these networks by two combination schemes, which are based on Dempster-Shafer evidence theory and fuzzy integral theory, respectively. Finally, a case study pertaining to the fault diagnosis for rotor-bearing systems is given in detail, and the results show that the proposed diagnosis strategies are feasible and more efficient than conventional stacked-vector methods

  19. [Burden of proof in medical cases--presumption of fact and prima facie evidence. II. Presumption of fact and prima facie evidence].

    Science.gov (United States)

    Sliwka, Marcin

    2004-01-01

    The aim of this paper was to present the main rules concerning the burden of proof in polish civil trials, including medical cases. The standard rules were presented with all the important exclusions such as presumption of law and fact or prima facie evidence. The author analyses the effect of these institutions on burden of proof in medical cases. The difference between presumptions of fact and prima facie evidence was analysed and explained. This paper also describes the importance of the res ipsa loquitur rule in United Kingdom and USA. This paper includes numerous High Court sentences on evidential and medical issues.

  20. Current status of lectin-based cancer diagnosis and therapy

    Directory of Open Access Journals (Sweden)

    Fohona S. Coulibaly

    2017-01-01

    Full Text Available Lectins are carbohydrate recognizing proteins originating from diverse origins in nature, including animals, plants, viruses, bacteria and fungus. Due to their exceptional glycan recognition property, they have found many applications in analytical chemistry, biotechnology and surface chemistry. This manuscript explores the current use of lectins for cancer diagnosis and therapy. Moreover, novel drug delivery strategies aiming at improving lectin’s stability, reducing their undesired toxicity and controlling their non-specific binding interactions are discussed. We also explore the nanotechnology application of lectins for cancer targeting and imaging. Although many investigations are being conducted in the field of lectinology, there is still a limited clinical translation of the major findings reported due to lectins stability and toxicity concerns. Therefore, new investigations of safe and effective drug delivery system strategies for lectins are warranted in order to take full advantage of these proteins.

  1. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    KAUST Repository

    Busbait, Monther I.

    2014-05-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.

  2. Computer-aided diagnosis workstation and data base system for chest diagnosis based on multihelical CT images

    International Nuclear Information System (INIS)

    Satoh, H.; Niki, N.; Eguchi, K.; Masuda, H.; Machida, S.; Moriyama, N.

    2006-01-01

    We have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router, Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information. (author)

  3. Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

    Science.gov (United States)

    Huang, Wentao; Sun, Hongjian; Wang, Weijie

    2017-06-03

    Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

  4. Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

    Science.gov (United States)

    Cao, Yan; Sun, Fengru

    2018-01-01

    In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.

  5. Condition based monitoring, diagnosis and maintenance on operating equipments of a hydraulic generator unit

    International Nuclear Information System (INIS)

    Liu, X T; Feng, F Z; Si, A W

    2012-01-01

    According to performance characteristics of operating equipments in a hydraulic generator unit (HGU), the relative techniques on condition monitoring and fault diagnosis (CMFD) are introduced in this paper, especially the key technologies are emphasized, such as equipment monitoring, expert system (ES), intelligent diagnosis and condition based maintenance (CBM). Meanwhile, according to the instructor on CBM proposed by State electric power corporation, based on integrated mode, the main steps on implementation of CBM are discussed in this paper.

  6. Nanotechnology-Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer

    Science.gov (United States)

    2017-08-01

    AWARD NUMBER: W81XWH-15-1-0157 TITLE: Nanotechnology -Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer PRINCIPAL...TITLE AND SUBTITLE Nanotechnology -Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer 5a. CONTRACT NUMBER 5b. GRANT NUMBER...identify novel differentially expressed miRNAs in the body fluids (blood, urine, etc.) for an early detection of PCa. Advances in nanotechnology and

  7. MR imaging-based diagnosis and classification of meniscal tears.

    Science.gov (United States)

    Nguyen, Jie C; De Smet, Arthur A; Graf, Ben K; Rosas, Humberto G

    2014-01-01

    Magnetic resonance (MR) imaging is currently the modality of choice for detecting meniscal injuries and planning subsequent treatment. A thorough understanding of the imaging protocols, normal meniscal anatomy, surrounding anatomic structures, and anatomic variants and pitfalls is critical to ensure diagnostic accuracy and prevent unnecessary surgery. High-spatial-resolution imaging of the meniscus can be performed using fast spin-echo and three-dimensional MR imaging sequences. Normal anatomic structures that can mimic a tear include the meniscal ligament, meniscofemoral ligaments, popliteomeniscal fascicles, and meniscomeniscal ligament. Anatomic variants and pitfalls that can mimic a tear include discoid meniscus, meniscal flounce, a meniscal ossicle, and chondrocalcinosis. When a meniscal tear is identified, accurate description and classification of the tear pattern can guide the referring clinician in patient education and surgical planning. For example, longitudinal tears are often amenable to repair, whereas horizontal and radial tears may require partial meniscectomy. Tear patterns include horizontal, longitudinal, radial, root, complex, displaced, and bucket-handle tears. Occasionally, meniscal tears can be difficult to detect at imaging; however, secondary indirect signs, such as a parameniscal cyst, meniscal extrusion, or linear subchondral bone marrow edema, should increase the radiologist's suspicion for an underlying tear. Awareness of common diagnostic errors can ensure accurate diagnosis of meniscal tears. Online supplemental material is available for this article. ©RSNA, 2014.

  8. Bartter syndrome prenatal diagnosis based on amniotic fluid biochemical analysis.

    Science.gov (United States)

    Garnier, Arnaud; Dreux, Sophie; Vargas-Poussou, Rosa; Oury, Jean-François; Benachi, Alexandra; Deschênes, Georges; Muller, Françoise

    2010-03-01

    Bartter syndrome is an autosomic recessive disease characterized by severe polyuria and sodium renal loss. The responsible genes encode proteins involved in electrolyte tubular reabsorption. Prenatal manifestations, mainly recurrent polyhydramnios because of fetal polyuria, lead to premature delivery. After birth, polyuria leads to life-threatening dehydration. Prenatal genetic diagnosis needs an index case. The aim of this study was to analyze amniotic fluid biochemistry for the prediction of Bartter syndrome. We retrospectively studied 16 amniotic fluids of Bartter syndrome-affected fetuses diagnosed after birth, only six of them being genetically proven. We assayed total proteins, alpha-fetoprotein, and electrolytes and defined a Bartter index corresponding to the multiplication of total protein and of alpha-fetoprotein. Results were compared with two control groups matched for gestational age-non-Bartter polyhydramnios (n = 30) and nonpolyhydramnios (n = 60). In Bartter syndrome, we observed significant differences (p Bartter index (0.16, 0.82, and 1.0, respectively). No statistical difference was observed for electrolytes. In conclusion, Bartter syndrome can be prenatally suspected on amniotic fluid biochemistry (sensitivity 93% and specificity 100%), allowing appropriate management before and after birth.

  9. Fault diagnosis and fault-tolerant control based on adaptive control approach

    CERN Document Server

    Shen, Qikun; Shi, Peng

    2017-01-01

    This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .

  10. A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

    Science.gov (United States)

    Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

    2018-03-01

    In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

  11. Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

    International Nuclear Information System (INIS)

    Zhou Gang; Ge Shengqi; Yang Li

    2010-01-01

    A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

  12. Wavelet Based Diagnosis and Protection of Electric Motors

    OpenAIRE

    Khan, M. Abdesh Shafiel Kafiey; Rahman, M. Azizur

    2010-01-01

    In this chapter, a short review of conventional Fourier transforms and new wavelet based faults diagnostic and protection techniques for electric motors is presented. The new hybrid wavelet packet transform (WPT) and neural network (NN) based faults diagnostic algorithm is developed and implemented for electric motors. The proposed WPT and NN

  13. Functional protease profiling for laboratory based diagnosis of invasive aspergillosis.

    Science.gov (United States)

    Sabbagh, Bassel; Costina, Victor; Buchheidt, Dieter; Reinwald, Mark; Neumaier, Michael; Findeisen, Peter

    2015-07-01

    Invasive aspergillosis (IA) remains difficult to diagnose in immunocompromised patients, because diagnostic criteria according to EORTC/MSG guidelines are often not met and have low sensitivity. Hence there is an urgent need to improve diagnostic procedures by developing novel approaches. In the present study, we present a proof of concept experiment for the monitoring of Aspergillus associated protease activity in serum specimens for diagnostic purpose. Synthetic peptides that are selectively cleaved by proteases secreted from Aspergillus species were selected from our own experiments and published data. These so called reporter peptides (RP, n=5) were added to serum specimens from healthy controls (HC, n=101) and patients with proven (IA, n=9) and possible (PIA, n=144) invasive aspergillosis. Spiked samples were incubated ex vivo under strictly standardized conditions. Proteolytic fragments were analyzed using MALDI-TOF mass spectrometry. Spiked specimens of IA patients had highest concentrations of RP-fragments followed by PIA and HC. The median signal intensity was 116.546 (SD, 53.063) for IA and 5.009 (SD, 8.432) for HC. A cut-off >36.910 was chosen that performed with 100% specificity and sensitivity. Patients with PIA had either values above [53% (76/144)] or below [47% (67/144)] this chosen cut-off. The detection of respective reporter peptide fragments can easily be performed by MALDI TOF mass spectrometry. In this proof of concept study we were able to demonstrate that serum specimens of patients with IA have increased proteolytic activity towards selected reporter peptides. However, the diagnostic value of functional protease profiling has to be validated in further prospective studies. It is likely that a combination of existing and new methods will be required to achieve optimal performance for diagnosis of IA in the future.

  14. An Expert System-Based Approach to Hospitality Company Diagnosis

    OpenAIRE

    Balfe, Andrew; O'Connor, Peter; McDonnell, Ciaran

    1994-01-01

    This paper describes the development of a prototype Expert System-based Analysis and Diagnostic (ESAD) package for the Hotel and Catering Industry. This computerised tool aids the hospitality manager in methodically scrutinising the hotel unit and environment, combining key information with systematic reasoning. The system searches through its extensive knowledge base, investigating complicated relationships. The number of possibilities considered is increased which will broaden the depth and...

  15. Immunodiagnostic confirmation of hydatid disease in patients with a presumptive diagnosis of injection

    International Nuclear Information System (INIS)

    Varela-Diaz, V.M.; Coltorti, E.A.

    1984-01-01

    Information obtained from the routine application of hydatid immunodiagnostic techniques in different clinical situations over a seven-year period is presented. The immunoelectrophoresis test was used and was replaced by the arc 5 double diffusion (DD5) test. Examination of sera from 1.888 patients with signs and/or symptoms comparatible with hydatid disease revealed that the presurgical confirmation of Echinococcus granulosus infection is only obtained by detection of anti-antigen 5 antibodies. In all patients whose preoperative serum showed three or more uncharacteristic bands in the absence of anti-antigen 5 antibodies, hydatid cysts were found surgically. DD5 testing of a fluid sample collected by puncture estabilished its hydatid etiology. Pos-operative monitoring of hydatidosis patients demonstrated that persintence of DD5-positivity two years after surgery established the presence of ther cysts. (M.A.C.) [pt

  16. Immunodiagnostic confirmation of hydatid disease in patients with a presumptive diagnosis of injection

    Energy Technology Data Exchange (ETDEWEB)

    Varela-Diaz, V.M.; Coltorti, E.A.

    Information obtained from the routine application of hydatid immunodiagnostic techniques in different clinical situations over a seven-year period is presented. The immunoelectrophoresis test was used and was replaced by the arc 5 double diffusion (DD5) test. Examination of sera from 1.888 patients with signs and/or symptoms comparatible with hydatid disease revealed that the presurgical confirmation of Echinococcus granulosus infection is only obtained by detection of anti-antigen 5 antibodies. In all patients whose preoperative serum showed three or more uncharacteristic bands in the absence of anti-antigen 5 antibodies, hydatid cysts were found surgically. DD5 testing of a fluid sample collected by puncture estabilished its hydatid etiology. Post-operative monitoring of hydatidosis patients demonstrated that persistence of DD5-positivity two years after surgery established the presence of ther cysts.

  17. Cost-effectiveness of malaria microscopy and rapid diagnostic tests versus presumptive diagnosis

    DEFF Research Database (Denmark)

    Batwala, Vincent; Magnussen, Pascal; Hansen, Kristian Schultz

    2011-01-01

    .9) than in low transmission setting (US$1.78). At a willingness to pay of US$2.8, RDT remained cost effective up to a threshold value of the cost of treatment of US$4.7. CONCLUSION: RDT was cost effective in both low and high transmission settings. With a global campaign to reduce the costs of AL and RDT......ABSTRACT: BACKGROUND: Current Uganda National Malaria treatment guidelines recommend parasitological confirmation either by microscopy or rapid diagnostic test (RDT) before treatment with artemether-lumefantrine (AL). However, the cost-effectiveness of these strategies has not been assessed...... departments were enrolled from March 2010 to February 2011. Of these, a random sample of 1,627 was selected to measure additional socio-economic characteristics. Costing was performed following the standard step-down cost allocation and the ingredients approach. Effectiveness was measured as the number...

  18. SPECT/CT in the Diagnosis of Skull Base Osteomyelitis

    International Nuclear Information System (INIS)

    Damle, Nishikant Avinash; Kumar, Rakesh; Kumar, Praveen; Jaganthan, Sriram; Patnecha, Manish; Bal, Chandrasekhar; Bandopadhyaya, Gurupad; Malhotra, Arun

    2011-01-01

    Skull base osteomyelitis is a potentially fatal disease. We demonstrate here the utility of SPECT/CT in diagnosing this entity, which was not obvious on a planar bone scan. A 99mT c MDP bone scan with SPECT/CT was carried out on a patient with clinically suspected skull base osteomyelitis. Findings were correlated with contrast enhanced CT (CECT) and MRI. Planar images were equivocal, but SPECT/CT showed intense uptake in the body of sphenoid and petrous temporal bone as well as the atlas corresponding to irregular bone destruction on CT and MRI. These findings indicate that SPECT/CT may have an additional role beyond planar imaging in the detection of skull base osteomyelitis.

  19. Five-year risk of HIV diagnosis subsequent to 147 hospital-based indicator diseases

    DEFF Research Database (Denmark)

    Omland, Lars Haukali; Legarth, Rebecca; Ahlström, Magnus Glindvad

    2016-01-01

    . To estimate the risk of HIV diagnosis in the general population without any indicator diseases, we calculated the FYRHD starting at age 25, 35, 45, and 55 years. RESULTS: The risk in the male general population was substantially higher than the female general population, and the risk was lower in the older...... with relevant indicator diseases are nonexistent. METHODS: In a nationwide population-based cohort study encompassing all Danish residents aged 20-60 years during 1994-2013, we estimated the 5-year risk of an HIV diagnosis (FYRHD) after a first-time diagnosis of 147 prespecified potential indicator diseases...

  20. Energy-Based Facial Rejuvenation: Advances in Diagnosis and Treatment.

    Science.gov (United States)

    Britt, Christopher J; Marcus, Benjamin

    2017-01-01

    The market for nonsurgical, energy-based facial rejuvenation techniques has increased exponentially since lasers were first used for skin rejuvenation in 1983. Advances in this area have led to a wide range of products that require the modern facial plastic surgeon to have a large repertoire of knowledge. To serve as a guide for current trends in the development of technology, applications, and outcomes of laser and laser-related technology over the past 5 years. We performed a review of PubMed from January 1, 2011, to March 1, 2016, and focused on randomized clinical trials, meta-analyses, systematic reviews, and clinical practice guidelines including case control, case studies and case reports when necessary, and included 14 articles we deemed landmark articles before 2011. Three broad categories of technology are leading non-energy-based rejuvenation technology: lasers, light therapy, and non-laser-based thermal tightening devices. Laser light therapy has continued to diversify with the use of ablative and nonablative resurfacing technologies, fractionated lasers, and their combined use. Light therapy has developed for use in combination with other technologies or stand alone. Finally, thermally based nonlaser skin-tightening devices, such as radiofrequency (RF) and intense focused ultrasonography (IFUS), are evolving technologies that have changed rapidly over the past 5 years. Improvements in safety and efficacy for energy-based treatment have expanded the patient base considering these therapies viable options. With a wide variety of options, the modern facial plastic surgeon can have a frank discussion with the patient regarding nonsurgical techniques that were never before available. Many of these patients can now derive benefit from treatments requiring significantly less downtime than before while the clinician can augment the treatment to maximize benefit to fit the patient's time schedule.

  1. Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

    Directory of Open Access Journals (Sweden)

    Jose M. Bernal-de-Lázaro

    2016-05-01

    Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

  2. An Event-Based Approach to Distributed Diagnosis of Continuous Systems

    Science.gov (United States)

    Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

    2010-01-01

    Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

  3. Development of a component centered fault monitoring and diagnosis knowledge based system for space power system

    Science.gov (United States)

    Lee, S. C.; Lollar, Louis F.

    1988-01-01

    The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for fault monitoring and diagnosis of space power systems, is discussed. The system architecture, knowledge representation, and fault monitoring and diagnosis strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.

  4. Computer-aided diagnosis workstation and telemedicine network system for chest diagnosis based on multislice CT images

    Science.gov (United States)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

    2009-02-01

    Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and

  5. Grid-based virtual clinic for medical diagnosis tutoring | Yatchou ...

    African Journals Online (AJOL)

    La réalisation visée est un outil collaboratif d\\'enseignement pour les médecins du terrain et les étudiants en médecine au sein d\\'une organisation virtuelle. The emerging grid-based technologies are increasingly adopted to enhance education and provide better learning services. This is characterized all over the world, ...

  6. Knowledge Based Components of Expertise in Medical Diagnosis.

    Science.gov (United States)

    1981-09-01

    the ability to ac- cess and use knowledge that one "has" is situationally dependent (e.g., Melton, 1963)1 Tulving and Pearlstone , 1966). For example...Londons Wiley, 1976. Tulving , E., & Pearlstone , E. Availability versus accessibility of information in memory for words. Journal of Verbal Learning...encounter (c.f.Flexser’and Tulving , 19781 Tulving ., 1976). Expert-based instructional devices (computer assisted instruction or decision support sys

  7. Vibration Based Diagnosis for Planetary Gearboxes Using an Analytical Model

    Directory of Open Access Journals (Sweden)

    Liu Hong

    2016-01-01

    Full Text Available The application of conventional vibration based diagnostic techniques to planetary gearboxes is a challenge because of the complexity of frequency components in the measured spectrum, which is the result of relative motions between the rotary planets and the fixed accelerometer. In practice, since the fault signatures are usually contaminated by noises and vibrations from other mechanical components of gearboxes, the diagnostic efficacy may further deteriorate. Thus, it is essential to develop a novel vibration based scheme to diagnose gear failures for planetary gearboxes. Following a brief literature review, the paper begins with the introduction of an analytical model of planetary gear-sets developed by the authors in previous works, which can predict the distinct behaviors of fault introduced sidebands. This analytical model is easy to implement because the only prerequisite information is the basic geometry of the planetary gear-set. Afterwards, an automated diagnostic scheme is proposed to cope with the challenges associated with the characteristic configuration of planetary gearboxes. The proposed vibration based scheme integrates the analytical model, a denoising algorithm, and frequency domain indicators into one synergistic system for the detection and identification of damaged gear teeth in planetary gearboxes. Its performance is validated with the dynamic simulations and the experimental data from a planetary gearbox test rig.

  8. A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2016-01-01

    Full Text Available The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD technique based local characteristic-scale decomposition (LCD was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN and mean impact value (MIV, ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.

  9. Neuron Types in the Presumptive Primary Somatosensory Cortex of the Florida Manatee (Trichechus manatus latirostris).

    Science.gov (United States)

    Reyes, Laura D; Stimpson, Cheryl D; Gupta, Kanika; Raghanti, Mary Ann; Hof, Patrick R; Reep, Roger L; Sherwood, Chet C

    2015-01-01

    Within afrotherians, sirenians are unusual due to their aquatic lifestyle, large body size and relatively large lissencephalic brain. However, little is known about the neuron type distributions of the cerebral cortex in sirenians within the context of other afrotherians and aquatic mammals. The present study investigated two cortical regions, dorsolateral cortex area 1 (DL1) and cluster cortex area 2 (CL2), in the presumptive primary somatosensory cortex (S1) in Florida manatees (Trichechus manatus latirostris) to characterize cyto- and chemoarchitecture. The mean neuron density for both cortical regions was 35,617 neurons/mm(3) and fell within the 95% prediction intervals relative to brain mass based on a reference group of afrotherians and xenarthrans. Densities of inhibitory interneuron subtypes labeled against calcium-binding proteins and neuropeptide Y were relatively low compared to afrotherians and xenarthrans and also formed a small percentage of the overall population of inhibitory interneurons as revealed by GAD67 immunoreactivity. Nonphosphorylated neurofilament protein-immunoreactive (NPNFP-ir) neurons comprised a mean of 60% of neurons in layer V across DL1 and CL2. DL1 contained a higher percentage of NPNFP-ir neurons than CL2, although CL2 had a higher variety of morphological types. The mean percentage of NPNFP-ir neurons in the two regions of the presumptive S1 were low compared to other afrotherians and xenarthrans but were within the 95% prediction intervals relative to brain mass, and their morphologies were comparable to those found in other afrotherians and xenarthrans. Although this specific pattern of neuron types and densities sets the manatee apart from other afrotherians and xenarthrans, the manatee isocortex does not appear to be explicitly adapted for an aquatic habitat. Many of the features that are shared between manatees and cetaceans are also shared with a diverse array of terrestrial mammals and likely represent highly conserved

  10. Dental enamel defect diagnosis through different technology-based devices.

    Science.gov (United States)

    Kobayashi, Tatiana Yuriko; Vitor, Luciana Lourenço Ribeiro; Carrara, Cleide Felício Carvalho; Silva, Thiago Cruvinel; Rios, Daniela; Machado, Maria Aparecida Andrade Moreira; Oliveira, Thais Marchini

    2018-06-01

    Dental enamel defects (DEDs) are faulty or deficient enamel formations of primary and permanent teeth. Changes during tooth development result in hypoplasia (a quantitative defect) and/or hypomineralisation (a qualitative defect). To compare technology-based diagnostic methods for detecting DEDs. Two-hundred and nine dental surfaces of anterior permanent teeth were selected in patients, 6-11 years of age, with cleft lip with/without cleft palate. First, a conventional clinical examination was conducted according to the modified Developmental Defects of Enamel Index (DDE Index). Dental surfaces were evaluated using an operating microscope and a fluorescence-based device. Interexaminer reproducibility was determined using the kappa test. To compare groups, McNemar's test was used. Cramer's V test was used for comparing the distribution of index codes obtained after classification of all dental surfaces. Cramer's V test revealed statistically significant differences (P < .0001) in the distribution of index codes obtained using the different methods; the coefficients were 0.365 for conventional clinical examination versus fluorescence, 0.961 for conventional clinical examination versus operating microscope and 0.358 for operating microscope versus fluorescence. The sensitivity of the operating microscope and fluorescence method was statistically significant (P = .008 and P < .0001, respectively). Otherwise, the results did not show statistically significant differences in accuracy and specificity for either the operating microscope or the fluorescence methods. This study suggests that the operating microscope performed better than the fluorescence-based device and could be an auxiliary method for the detection of DEDs. © 2017 FDI World Dental Federation.

  11. Diagnosis of pneumothorax using a microwave-based detector

    Science.gov (United States)

    Ling, Geoffrey S. F.; Riechers, Ronald G., Sr.; Pasala, Krishna M.; Blanchard, Jeremy; Nozaki, Masako; Ramage, Anthony; Jackson, William; Rosner, Michael; Garcia-Pinto, Patricia; Yun, Catherine; Butler, Nathan; Riechers, Ronald G., Jr.; Williams, Daniel; Zeidman, Seth M.; Rhee, Peter; Ecklund, James M.; Fitzpatrick, Thomas; Lockhart, Stephen

    2001-08-01

    A novel method for identifying pneumothorax is presented. This method is based on a novel device that uses electromagnetic waves in the microwave radio frequency (RF) region and a modified algorithm previously used for the estimation of the angle of arrival of radar signals. In this study, we employ this radio frequency triage tool (RAFT) to the clinical condition of pneumothorax, which is a collapsed lung. In anesthetized pigs, RAFT can detect changes in the RF signature from a lung that is 20 percent or greater collapsed. These results are compared to chest x-ray. Both studies are equivalent in their ability to detect pneumothorax in pigs.

  12. Validation of hospital register-based diagnosis of Parkinson's disease

    DEFF Research Database (Denmark)

    Wermuth, Lene; Lassen, Christina Funch; Himmerslev, Liselotte

    2012-01-01

    Denmark has a long-standing tradition of maintaining one of the world's largest health science specialized register data bases as the National Hospital Register (NHR). To estimate the prevalence and incidence of diseases, the correctness of the diagnoses recorded is critical. Parkinson's disease...... (PD) is a neurodegenerative disorder and only 75-80% of patients with parkinsonism will have idiopathic PD (iPD). It is necessary to follow patients in order to determine if some of them will develop other neurodegenerative diseases and a one-time-only diagnostic code for iPD reported in the register...

  13. Research of Litchi Diseases Diagnosis Expertsystem Based on Rbr and Cbr

    Science.gov (United States)

    Xu, Bing; Liu, Liqun

    To conquer the bottleneck problems existing in the traditional rule-based reasoning diseases diagnosis system, such as low reasoning efficiency and lack of flexibility, etc.. It researched the integrated case-based reasoning (CBR) and rule-based reasoning (RBR) technology, and put forward a litchi diseases diagnosis expert system (LDDES) with integrated reasoning method. The method use data mining and knowledge obtaining technology to establish knowledge base and case library. It adopt rules to instruct the retrieval and matching for CBR, and use association rule and decision trees algorithm to calculate case similarity.The experiment shows that the method can increase the system's flexibility and reasoning ability, and improve the accuracy of litchi diseases diagnosis.

  14. Diagnosis and Model Based Identification of a Coupling Misalignment

    Directory of Open Access Journals (Sweden)

    P. Pennacchi

    2005-01-01

    Full Text Available This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.

  15. Toward a Mechanism-Based Approach to Pain Diagnosis.

    Science.gov (United States)

    Vardeh, Daniel; Mannion, Richard J; Woolf, Clifford J

    2016-09-01

    The past few decades have witnessed a huge leap forward in our understanding of the mechanistic underpinnings of pain, in normal states where it helps protect from injury, and also in pathological states where pain evolves from a symptom reflecting tissue injury to become the disease itself. However, despite these scientific advances, chronic pain remains extremely challenging to manage clinically. Although the number of potential treatment targets has grown substantially and a strong case has been made for a mechanism-based and individualized approach to pain therapy, arguably clinicians are not much more advanced now than 20 years ago, in their capacity to either diagnose or effectively treat their patients. The gulf between pain research and pain management is as wide as ever. We are still currently unable to apply an evidence-based approach to chronic pain management that reflects mechanistic understanding, and instead, clinical practice remains an empirical and often unsatisfactory journey for patients, whose individual response to treatment cannot be predicted. In this article we take a common and difficult to treat pain condition, chronic low back pain, and use its presentation in clinical practice as a framework to highlight what is known about pathophysiological pain mechanisms and how we could potentially detect these to drive rational treatment choice. We discuss how present methods of assessment and management still fall well short, however, of any mechanism-based or precision medicine approach. Nevertheless, substantial improvements in chronic pain management could be possible if a more strategic and coordinated approach were to evolve, one designed to identify the specific mechanisms driving the presenting pain phenotype. We present an analysis of such an approach, highlighting the major problems in identifying mechanisms in patients, and develop a framework for a pain diagnostic ladder that may prove useful in the future, consisting of successive

  16. Towards a mechanism-based approach to pain diagnosis

    Science.gov (United States)

    Vardeh, Daniel

    2016-01-01

    The last few decades have witnessed a huge leap forward in our understanding of the mechanistic underpinnings of pain, both in normal states where it helps protect from injury, and in pathological states where pain evolves from a symptom reflecting tissue injury to become the disease itself. However, despite these scientific advances, chronic pain remains extremely challenging to manage clinically. While the number of potential treatment targets has grown substantially and a strong case has been made for a mechanism-based and individualized approach to pain therapy, arguably clinicians are not much more advanced now than 20 years ago, in their capacity to either diagnose or effectively treat their patients. The gulf between pain research and pain management is as wide as ever. We are still currently unable to apply an evidence-based approach to chronic pain management that reflects mechanistic understanding, and instead, clinical practice remains an empirical and often unsatisfactory journey for patients, whose individual response to treatment cannot be predicted. Here we take a common and difficult to treat pain condition, chronic low back pain, and use its presentation in clinical practice as a framework to highlight what is known about pathophysiological pain mechanisms and how we could potentially detect these to drive rational treatment choice. We discuss how present methods of assessment and management still fall well short, however, of any mechanism-based or precision-medicine approach. Nevertheless, substantial improvements in chronic pain management could be possible if a more strategic and coordinated approach were to evolve, one designed to identify the specific mechanisms driving the presenting pain phenotype. We present an analysis of such an approach, highlighting the major problems in identifying mechanisms in patients, and develop a framework for a pain diagnostic ladder that may prove useful in the future, consisting of successive identification of

  17. [Active crop canopy sensor-based nitrogen diagnosis for potato].

    Science.gov (United States)

    Yu, Jing; Li, Fei; Qin, Yong-Lin; Fan, Ming-Shou

    2013-11-01

    In the present study, two potato experiments involving different N rates in 2011 were conducted in Wuchuan County and Linxi County, Inner Mongolia. Normalized difference vegetation index (NDVI) was collected by an active GreenSeeker crop canopy sensor to estimate N status of potato. The results show that the NDVI readings were poorly correlated with N nutrient indicators of potato at vegetative Growth stage due to the influence of soil background. With the advance of growth stages, NDVI values were exponentially related to plant N uptake (R2 = 0.665) before tuber bulking stage and were linearly related to plant N concentration (R2 = 0.699) when plant fully covered soil. In conclusion, GreenSeeker active crop sensor is a promising tool to estimate N status for potato plants. The findings from this study may be useful for developing N recommendation method based on active crop canopy sensor.

  18. Diagnosis methods based on noise analysis at Cernavoda NPP, Romania

    International Nuclear Information System (INIS)

    Banica, Constantin; Dobrea, Dumitru

    1999-01-01

    This paper describes a recent noise analysis of the neutronic signals provided by in-core flux detectors (ICFD) and ion chambers (IC). This analysis is part of on-going program developed for Unit 1 of the Cernavoda NPP, Romania, with the following main objectives: - prediction of detector failures based on pattern recognition; - determination of fast excursions from steady states; - detection of abnormal mechanical vibrations in the reactor core. The introduction presents briefly the reactor, the location of ICFD's and IC's. The second section presents the data acquisition systems and their capabilities. The paper continues with a brief presentation of the numerical methods used for analysis (section 3). The most significant results can be found in section 4, while section 5 concludes about useful information that can be obtained from the neutronic signals at high power steady-state operation. (authors)

  19. A knowledge-based diagnosis system for welding machine problem solving

    International Nuclear Information System (INIS)

    Bonnieres, P. de; Boutes, J.L.; Calas, M.A.; Para, S.

    1986-06-01

    This paper presents a knowledge-based diagnosis system which can be a valuable aid in resolving malfunctions and failures encountered using the automatic hot-wire TIG weld cladding process. This knowledge-based system is currently under evaluation by welding operators at the Framatome heavy fabricating facility. Extension to other welding processes is being considered

  20. Computer-Aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.

    Science.gov (United States)

    Bharti, Puja; Mittal, Deepti; Ananthasivan, Rupa

    2016-04-19

    Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis. © The Author(s) 2016.

  1. Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

    Directory of Open Access Journals (Sweden)

    Yu Yuan

    2015-01-01

    Full Text Available The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

  2. Stroke Diagnosis using Microstrip Patch Antennas Based on Microwave Tomography Systems

    Directory of Open Access Journals (Sweden)

    Sakthisudhan K

    2017-03-01

    Full Text Available Microwave tomography (MT based on stroke diagnosis is one of the alternative methods for determinations of the haemorrhagic, ischemic and stroke in brain nervous systems. It is focusing on the brain imaging, continuous monitoring, and preclinical applications. It provides cost effective system and able to use the rural and urban medical clinics that lack the necessary resources in effective stroke diagnosis during emerging applications in road accident and pre-ambulance clinical treatment. In the early works, the design of microstrip patch antennas (MPAs involved the implementation of MT system. Consequently, the MT system presented a few limitations since it required an efficient MPA design with appropriate parameters. Moreover, there were no specific diagnosis modules and body centric features in it. The present research proposes the MPA designs in the forms of diagnosis modules and implements it on the MT system.

  3. Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS

    Directory of Open Access Journals (Sweden)

    Quan Zhou

    2017-07-01

    Full Text Available The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.

  4. Sexuality after a cancer diagnosis: A population-based study.

    Science.gov (United States)

    Jackson, Sarah E; Wardle, Jane; Steptoe, Andrew; Fisher, Abigail

    2016-12-15

    This study explored differences in sexual activity, function, and concerns between cancer survivors and cancer-free controls in a population-based study. The data were from 2982 men and 3708 women who were 50 years old or older and were participating in the English Longitudinal Study of Ageing. Sexual well-being was assessed with the Sexual Relationships and Activities Questionnaire, and cancer diagnoses were self-reported. There were no differences between cancer survivors and controls in levels of sexual activity (76.0% vs 78.5% for men and 58.2% vs 55.5% for women) or sexual function. Men and women with cancer diagnoses were more dissatisfied with their sex lives than controls (age-adjusted percentages: 30.9% vs 19.8% for men [P = .023] and 18.2% vs 11.8% for women [P = .034]), and women with cancer were more concerned about levels of sexual desire (10.2% vs 7.1%; P = .006). Women diagnosed sexual desire (14.8% vs 7.1%; P = .007) and orgasmic experience (17.6% vs 7.1%; P = .042) than controls, but there were no differences in men. Self-reports of sexual activity and functioning in older people with cancer are broadly comparable to age-matched, cancer-free controls. There is a need to identify the causes of sexual dissatisfaction among long-term cancer survivors despite apparently normal levels of sexual activity and function for their age. The development of interventions addressing low sexual desire and problems with sexual functioning in women is also important and may be particularly relevant for cancer survivors after treatment. Cancer 2016;122:3883-3891. © 2016 American Cancer Society. © 2016 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

  5. Sexuality after a cancer diagnosis: A population‐based study

    Science.gov (United States)

    Wardle, Jane; Steptoe, Andrew; Fisher, Abigail

    2016-01-01

    BACKGROUND This study explored differences in sexual activity, function, and concerns between cancer survivors and cancer‐free controls in a population‐based study. METHODS The data were from 2982 men and 3708 women who were 50 years old or older and were participating in the English Longitudinal Study of Ageing. Sexual well‐being was assessed with the Sexual Relationships and Activities Questionnaire, and cancer diagnoses were self‐reported. RESULTS There were no differences between cancer survivors and controls in levels of sexual activity (76.0% vs 78.5% for men and 58.2% vs 55.5% for women) or sexual function. Men and women with cancer diagnoses were more dissatisfied with their sex lives than controls (age‐adjusted percentages: 30.9% vs 19.8% for men [P = .023] and 18.2% vs 11.8% for women [P = .034]), and women with cancer were more concerned about levels of sexual desire (10.2% vs 7.1%; P = .006). Women diagnosed sexual desire (14.8% vs 7.1%; P = .007) and orgasmic experience (17.6% vs 7.1%; P = .042) than controls, but there were no differences in men. CONCLUSIONS Self‐reports of sexual activity and functioning in older people with cancer are broadly comparable to age‐matched, cancer‐free controls. There is a need to identify the causes of sexual dissatisfaction among long‐term cancer survivors despite apparently normal levels of sexual activity and function for their age. The development of interventions addressing low sexual desire and problems with sexual functioning in women is also important and may be particularly relevant for cancer survivors after treatment. Cancer 2016;122:3883–3891. © 2016 American Cancer Society. PMID:27531631

  6. Congenital neutropenia: diagnosis, molecular bases and patient management

    Directory of Open Access Journals (Sweden)

    Chantelot Christine

    2011-05-01

    Full Text Available Abstract The term congenital neutropenia encompasses a family of neutropenic disorders, both permanent and intermittent, severe ( When neutropenia is detected, an attempt should be made to establish the etiology, distinguishing between acquired forms (the most frequent, including post viral neutropenia and auto immune neutropenia and congenital forms that may either be isolated or part of a complex genetic disease. Except for ethnic neutropenia, which is a frequent but mild congenital form, probably with polygenic inheritance, all other forms of congenital neutropenia are extremely rare and have monogenic inheritance, which may be X-linked or autosomal, recessive or dominant. About half the forms of congenital neutropenia with no extra-hematopoetic manifestations and normal adaptive immunity are due to neutrophil elastase (ELANE mutations. Some patients have severe permanent neutropenia and frequent infections early in life, while others have mild intermittent neutropenia. Congenital neutropenia may also be associated with a wide range of organ dysfunctions, as for example in Shwachman-Diamond syndrome (associated with pancreatic insufficiency and glycogen storage disease type Ib (associated with a glycogen storage syndrome. So far, the molecular bases of 12 neutropenic disorders have been identified. Treatment of severe chronic neutropenia should focus on prevention of infections. It includes antimicrobial prophylaxis, generally with trimethoprim-sulfamethoxazole, and also granulocyte-colony-stimulating factor (G-CSF. G-CSF has considerably improved these patients' outlook. It is usually well tolerated, but potential adverse effects include thrombocytopenia, glomerulonephritis, vasculitis and osteoporosis. Long-term treatment with G-CSF, especially at high doses, augments the spontaneous risk of leukemia in patients with congenital neutropenia.

  7. Nuclear power plant monitoring and fault diagnosis methods based on the artificial intelligence technique

    International Nuclear Information System (INIS)

    Yoshikawa, S.; Saiki, A.; Ugolini, D.; Ozawa, K.

    1996-01-01

    The main objective of this paper is to develop an advanced diagnosis system based on the artificial intelligence technique to monitor the operation and to improve the operational safety of nuclear power plants. Three different methods have been elaborated in this study: an artificial neural network local diagnosis (NN ds ) scheme that acting at the component level discriminates between normal and abnormal transients, a model-based diagnostic reasoning mechanism that combines a physical causal network model-based knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge. Although the three methods have been developed and verified independently, they are highly correlated and, when connected together, form a effective and robust diagnosis and monitoring tool. (authors)

  8. Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

    Science.gov (United States)

    Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

    2006-03-01

    Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

  9. 129 microbiological studies of blood specimen from presumptively ...

    African Journals Online (AJOL)

    patient' serum for salmonella antibodies is a rapid tool in the diagnosis of enteric fever, but can afford an indirect ... which various dilutions of patient's serum are mixed with drops of either O or H-antigen of Salm. Typhi .... biochemical characterization and sero-typing are essential for complete identification of salmonella.

  10. UIO-based Fault Diagnosis for Hydraulic Automatic Gauge Control System of Magnesium Sheet Mill

    Directory of Open Access Journals (Sweden)

    Li-Ping FAN

    2014-02-01

    Full Text Available Hydraulic automatic gauge control system of magnesium sheet mill is a complex integrated control system, which including mechanical, hydraulic and electrical comprehensive information. The failure rate of AGC system always is high, and its fault reasons are always complex. Based on analyzing the fault of main components of the automatic gauge control system, unknown input observer is used to realize fault diagnosis and isolation. Simulation results show that the fault diagnosis method based on the unknown input observer for the hydraulic automatic gauge control system of magnesium sheet mill is effective.

  11. Decision tree and PCA-based fault diagnosis of rotating machinery

    Science.gov (United States)

    Sun, Weixiang; Chen, Jin; Li, Jiaqing

    2007-04-01

    After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

  12. NN-Es Fault Diagnosis Method in Nuclear Power Equipment Based on Concept Lattice

    International Nuclear Information System (INIS)

    Liu Yongkuo; Xie Chunli; Xia Hong

    2010-01-01

    In order to improve the fault diagnosis accuracy of nuclear power plant,neural network and expert systems were combined to give full play to their advantages. In this paper, the concept lattice was applied to get the object properties, extracting the core attributes, dispensable attributes and relative necessary attributes from a large number raw data of fault symptoms.Based on these attributes, neural networks with different levels of importance were designed to improve the learning speed and diagnosis accuracy, and the diagnosis results of the neural networks were verified by using rule-based reasoning expert system. To verify the accuracy of this method, some simulation experiments about the typical faults of nuclear power plant were conducted. And the simulation results show that it is feasible to diagnose nuclear power plant faults with the confederation diagnosis methods combined the neural networks based on the concept lattice theory and expert system, with the distinctive features such as the efficiency of neural network learning, less calculation and reliability of diagnosis results and so on. (authors)

  13. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    Directory of Open Access Journals (Sweden)

    Xianfeng Yuan

    2015-01-01

    presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel support vector machine (SVM and Dempster-Shafer (D-S fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

  14. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition.

    Science.gov (United States)

    Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao

    2017-05-25

    Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

  15. Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

    Directory of Open Access Journals (Sweden)

    Hao Sun

    2014-01-01

    Full Text Available The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO is proposed. Geronimo, Hardin, and Massopust (GHM multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

  16. Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

    Directory of Open Access Journals (Sweden)

    Wei-Li Qin

    2016-01-01

    Full Text Available Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

  17. [Pleural effusion: diagnosis and management].

    Science.gov (United States)

    Pastré, J; Roussel, S; Israël Biet, D; Sanchez, O

    2015-04-01

    Pleural effusion management is a common clinical situation associated with numerous pulmonary, pleural or extra-pulmonary diseases. A systematic approach is needed to enable a rapid diagnosis and an appropriate treatment. Pleural fluid analysis is the first step to perform which allows a presumptive diagnosis in most cases. Otherwise, further analysis of the pleural fluid or thoracic imaging or pleural biopsy may be necessary. This review aims at highlighting the important elements of the work-up required by a pleural effusion. Copyright © 2014 Société nationale française de médecine interne (SNFMI). Published by Elsevier SAS. All rights reserved.

  18. Infrared thermography based on artificial intelligence as a screening method for carpal tunnel syndrome diagnosis.

    Science.gov (United States)

    Jesensek Papez, B; Palfy, M; Mertik, M; Turk, Z

    2009-01-01

    This study further evaluated a computer-based infrared thermography (IRT) system, which employs artificial neural networks for the diagnosis of carpal tunnel syndrome (CTS) using a large database of 502 thermal images of the dorsal and palmar side of 132 healthy and 119 pathological hands. It confirmed the hypothesis that the dorsal side of the hand is of greater importance than the palmar side when diagnosing CTS thermographically. Using this method it was possible correctly to classify 72.2% of all hands (healthy and pathological) based on dorsal images and > 80% of hands when only severely affected and healthy hands were considered. Compared with the gold standard electromyographic diagnosis of CTS, IRT cannot be recommended as an adequate diagnostic tool when exact severity level diagnosis is required, however we conclude that IRT could be used as a screening tool for severe cases in populations with high ergonomic risk factors of CTS.

  19. Learning and case-based reasoning for faults diagnosis-aiding in nuclear power plants

    International Nuclear Information System (INIS)

    Nicolini, C.

    1998-01-01

    The aim of this thesis is the design of a faults diagnosis-aiding system in a nuclear facility of the Cea. Actually the existing system allows the optimization of the production processes in regular operating conditions. Meanwhile during accidental events, the alarms, managed by threshold, are bringing no relevant information. To increase the reliability and the safety, the human operator needs a faults diagnosis-aiding system. The developed system, SECAPI, combines problem solving techniques and automatic learning techniques, that allow the diagnosis and the the simulation of various faults happening on nuclear facilities. Its reasoning principle uses case-based and rules-based techniques. SECAPI owns a learning module which reads out knowledge connected with faults. It can then simulate various faults, using the inductive logical computing. SECAPI has been applied on a radioactive tritium treatment operating channel, at the Cea with good results. (A.L.B.)

  20. Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

    International Nuclear Information System (INIS)

    Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

    2006-01-01

    High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

  1. A Clinical Approach to the Diagnosis of Acid-Base Disorders

    OpenAIRE

    Bear, Robert A.

    1986-01-01

    The ability to diagnose and manage acid-base disorders rapidly and effectively is essential to the care of critically ill patients. This article presents an approach to the diagnosis of pure and mixed acid-base disorders, metabolic or respiratory. The approach taken is based on using the law of mass-action equation as it applies to the bicarbonate buffer system (Henderson equation), using sub-classifications for diagnostic purposes of causes of metabolic acidosis and metabolic alkalosis, and ...

  2. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    Directory of Open Access Journals (Sweden)

    Chen Lu

    Full Text Available Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for

  3. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    Science.gov (United States)

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

  4. Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft

    Science.gov (United States)

    Desbazeille, M.; Randall, R. B.; Guillet, F.; El Badaoui, M.; Hoisnard, C.

    2010-07-01

    This work aims at monitoring large diesel engines by analyzing the crankshaft angular speed variations. It focuses on a powerful 20-cylinder diesel engine with crankshaft natural frequencies within the operating speed range. First, the angular speed variations are modeled at the crankshaft free end. This includes modeling both the crankshaft dynamical behavior and the excitation torques. As the engine is very large, the first crankshaft torsional modes are in the low frequency range. A model with the assumption of a flexible crankshaft is required. The excitation torques depend on the in-cylinder pressure curve. The latter is modeled with a phenomenological model. Mechanical and combustion parameters of the model are optimized with the help of actual data. Then, an automated diagnosis based on an artificially intelligent system is proposed. Neural networks are used for pattern recognition of the angular speed waveforms in normal and faulty conditions. Reference patterns required in the training phase are computed with the model, calibrated using a small number of actual measurements. Promising results are obtained. An experimental fuel leakage fault is successfully diagnosed, including detection and localization of the faulty cylinder, as well as the approximation of the fault severity.

  5. Diagnosis of three types of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.; Moshkov, Mikhail

    2016-01-01

    We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound

  6. Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings

    NARCIS (Netherlands)

    Beyene, Tariku Jibat; Eshetu, Amanuel; Abdu, Amina; Wondimu, Etenesh; Beyi, Ashenafi Feyisa; Tufa, Takele Beyene; Ibrahim, Sami

    2017-01-01

    Background: The recent rise in mobile phone use and increased signal coverage has created opportunities for growth of the mobile Health sector in many low resource settings. This pilot study explores the use of a smartphone-based application, VetAfrica-Ethiopia, in assisting diagnosis of cattle

  7. The Analysis of Organizational Diagnosis on Based Six Box Model in Universities

    Science.gov (United States)

    Hamid, Rahimi; Siadat, Sayyed Ali; Reza, Hoveida; Arash, Shahin; Ali, Nasrabadi Hasan; Azizollah, Arbabisarjou

    2011-01-01

    Purpose: The analysis of organizational diagnosis on based six box model at universities. Research method: Research method was descriptive-survey. Statistical population consisted of 1544 faculty members of universities which through random strafed sampling method 218 persons were chosen as the sample. Research Instrument were organizational…

  8. Technical advances in flow cytometry-based diagnosis and monitoring of paroxysmal nocturnal hemoglobinuria

    Science.gov (United States)

    Correia, Rodolfo Patussi; Bento, Laiz Cameirão; Bortolucci, Ana Carolina Apelle; Alexandre, Anderson Marega; Vaz, Andressa da Costa; Schimidell, Daniela; Pedro, Eduardo de Carvalho; Perin, Fabricio Simões; Nozawa, Sonia Tsukasa; Mendes, Cláudio Ernesto Albers; Barroso, Rodrigo de Souza; Bacal, Nydia Strachman

    2016-01-01

    ABSTRACT Objective: To discuss the implementation of technical advances in laboratory diagnosis and monitoring of paroxysmal nocturnal hemoglobinuria for validation of high-sensitivity flow cytometry protocols. Methods: A retrospective study based on analysis of laboratory data from 745 patient samples submitted to flow cytometry for diagnosis and/or monitoring of paroxysmal nocturnal hemoglobinuria. Results: Implementation of technical advances reduced test costs and improved flow cytometry resolution for paroxysmal nocturnal hemoglobinuria clone detection. Conclusion: High-sensitivity flow cytometry allowed more sensitive determination of paroxysmal nocturnal hemoglobinuria clone type and size, particularly in samples with small clones. PMID:27759825

  9. Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2016-01-01

    Full Text Available Single-Stage Extreme Learning Machine (SS-ELM is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

  10. Rolling bearing fault diagnosis based on information fusion using Dempster-Shafer evidence theory

    Science.gov (United States)

    Pei, Di; Yue, Jianhai; Jiao, Jing

    2017-10-01

    This paper presents a fault diagnosis method for rolling bearing based on information fusion. Acceleration sensors are arranged at different position to get bearing vibration data as diagnostic evidence. The Dempster-Shafer (D-S) evidence theory is used to fuse multi-sensor data to improve diagnostic accuracy. The efficiency of the proposed method is demonstrated by the high speed train transmission test bench. The results of experiment show that the proposed method in this paper improves the rolling bearing fault diagnosis accuracy compared with traditional signal analysis methods.

  11. Fault Detection and Diagnosis System in Process industry Based on Big Data and WeChat

    Directory of Open Access Journals (Sweden)

    Sun Zengqiang

    2017-01-01

    Full Text Available The fault detection and diagnosis information in process industry can be received, anytime and anywhere, based on bigdata and WeChat with mobile phone, which got rid of constraints that can only check Distributed Control System (DCS in the central control room or look over in office. Then, fault detection, diagnosis information sharing can be provided, and what’s more, fault detection alarm range, code and inform time can be personalized. The pressure of managers who worked on process industry can be release with the mobile information system.

  12. Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

    International Nuclear Information System (INIS)

    Wang, M; Hu, N Q; Qin, G J

    2011-01-01

    In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

  13. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI

    DEFF Research Database (Denmark)

    Bron, Esther E.; Smits, Marion; van der Flier, Wiesje M.

    2015-01-01

    algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease...... of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume......Abstract Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform...

  14. Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Wang, M; Hu, N Q; Qin, G J, E-mail: hnq@nudt.edu.cn, E-mail: wm198063@yahoo.com.cn [School of Mechatronic Engineering and Automation, National University of Defense Technology, ChangSha, Hunan, 410073 (China)

    2011-07-19

    In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

  15. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  16. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    Science.gov (United States)

    Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  17. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    Directory of Open Access Journals (Sweden)

    Hong Yin

    2014-01-01

    Full Text Available The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

  18. A fault diagnosis system for PV power station based on global partitioned gradually approximation method

    Science.gov (United States)

    Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.

    2016-08-01

    As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and fault diagnosis of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a fault diagnosis algorithm to determine and locate the fault of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the fault weigh factor. The fault threshold is defined to determine the fault, and the shade is considered to reduce the probability of misjudgments. A fault diagnosis system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and fault diagnosis. Through the data from PV plants, the algorithm is verified. The results show that the fault diagnosis results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.

  19. Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

    Science.gov (United States)

    Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun

    2017-11-01

    The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

  20. Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

    Directory of Open Access Journals (Sweden)

    Junjie Lu

    2018-01-01

    Full Text Available Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components’ performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg–Marquardt (LM algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF method, the particle filter (PF method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.

  1. A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

    International Nuclear Information System (INIS)

    Liu, Yong-Kuo; Wu, Guo-Hua; Xie, Chun-Li; Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun

    2016-01-01

    Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

  2. A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yong-Kuo, E-mail: LYK08@126.com [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Wu, Guo-Hua [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Institute of Nuclear Energy Technology, Tsinghua University, Beijing 100084 (China); Xie, Chun-Li [Traffic College, Northeast Forestry University, Harbin, 150040 (China); Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China)

    2016-02-15

    Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

  3. Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis

    Directory of Open Access Journals (Sweden)

    Yi Cao

    2013-06-01

    Full Text Available A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD and the relative crossing information (RCI methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO clustering algorithm, the synthesizing symptom parameters (SSP for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP, and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well.

  4. Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Yunxiao Fu

    2016-06-01

    Full Text Available Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM based correntropy extracted from the Intrinsic Mode Functions (IMFs, which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD and employ Least Square Support Vector Machine (LSSVM to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.

  5. MRI-based decision tree model for diagnosis of biliary atresia.

    Science.gov (United States)

    Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

    2018-02-23

    To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

  6. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Science.gov (United States)

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  7. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-08

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  8. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ke Li

    2016-01-01

    Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  9. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  10. Fault Diagnosis for Compensating Capacitors of Jointless Track Circuit Based on Dynamic Time Warping

    Directory of Open Access Journals (Sweden)

    Wei Dong

    2014-01-01

    Full Text Available Aiming at the problem of online fault diagnosis for compensating capacitors of jointless track circuit, a dynamic time warping (DTW based diagnosis method is proposed in this paper. Different from the existing related works, this method only uses the ground indoor monitoring signals of track circuit to locate the faulty compensating capacitor, not depending on the shunt current of inspection train, which is an indispensable condition for existing methods. So, it can be used for online diagnosis of compensating capacitor, which has not yet been realized by existing methods. To overcome the key problem that track circuit cannot obtain the precise position of the train, the DTW method is used for the first time in this situation to recover the function relationship between receiver’s peak voltage and shunt position. The necessity, thinking, and procedure of the method are described in detail. Besides the classical DTW based method, two improved methods for improving classification quality and reducing computation complexity are proposed. Finally, the diagnosis experiments based on the simulation model of track circuit show the effectiveness of the proposed methods.

  11. The Ethics of Fertility Preservation for Paediatric Cancer Patients: From Offer to Rebuttable Presumption.

    Science.gov (United States)

    McDougall, Rosalind

    2015-11-01

    Given advances in the science of fertility preservation and the link between fertility choices and wellbeing, it is time to reframe our ethical thinking around fertility preservation procedures for children and young people with cancer. The current framing of fertility preservation as a possible offer may no longer be universally appropriate. There is an increasingly pressing need to discuss the ethics of failing to preserve fertility, particularly for patient groups for whom established techniques exist. I argue that the starting point for deliberating about a particular patient should be a rebuttable presumption that fertility preservation ought to be attempted. Consideration of the harms applicable to that specific patient may then override this presumption. I outline the benefits of attempting fertility preservation; these justify a presumption in favour of the treatment. I then discuss the potential harms associated with fertility preservation procedures, which may justify failing to attempt fertility preservation in an individual patient's particular case. Moving from a framework of offer to one of rebuttable presumption in favour of fertility preservation would have significant implications for medical practice, healthcare organizations and the state. © 2015 John Wiley & Sons Ltd.

  12. Effects of surveillance on the rule of law, due process and the presumption of innocence

    NARCIS (Netherlands)

    Galetta, Antonella; de Hert, Paul; Wright, D.; Kreissl, R.

    2015-01-01

    This contribution focuses on the impact of surveillance on the rule of law, due process and the presumption of innocence, key values and principles of a democratic order. It illustrates how they are implemented and enforced in contemporary surveillance societies, while referring to European law and

  13. 47 CFR 51.230 - Presumption of acceptability for deployment of an advanced services loop technology.

    Science.gov (United States)

    2010-10-01

    ... an advanced services loop technology. 51.230 Section 51.230 Telecommunication FEDERAL COMMUNICATIONS... Carriers § 51.230 Presumption of acceptability for deployment of an advanced services loop technology. (a) An advanced services loop technology is presumed acceptable for deployment under any one of the...

  14. 75 FR 61356 - Presumptions of Service Connection for Persian Gulf Service; Correction

    Science.gov (United States)

    2010-10-05

    ... DEPARTMENT OF VETERANS AFFAIRS 38 CFR Part 3 RIN 2900-AN24 Presumptions of Service Connection for Persian Gulf Service; Correction AGENCY: Department of Veterans Affairs. ACTION: Correcting amendment. SUMMARY: The Department of Veterans Affairs (VA) published in the Federal Register of September 29, 2010...

  15. 20 CFR 410.418 - Irrebuttable presumption of total disability due to pneumoconiosis.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Irrebuttable presumption of total disability due to pneumoconiosis. 410.418 Section 410.418 Employees' Benefits SOCIAL SECURITY ADMINISTRATION... of the Pneumoconioses of the International Labour Office, Extended Classification (1968) (which may...

  16. The Principle of the Presumption of Innocence and its Challenges in ...

    African Journals Online (AJOL)

    The administration of the criminal justice system tries to strike a balance between the search for truth and the fairness of the process. To this end, the law should protect individual rights and impose various legal burdens on the state. One such tool is the principle of the presumption of innocence until proven guilty. This is a ...

  17. 78 FR 28140 - Tentative Eligibility Determinations; Presumptive Eligibility for Psychosis and Other Mental Illness

    Science.gov (United States)

    2013-05-14

    ...; Presumptive Eligibility for Psychosis and Other Mental Illness AGENCY: Department of Veterans Affairs. ACTION... time periods and for Persian Gulf War veterans who developed a mental illness other than psychosis... veterans, 38 CFR 17.37, to include veterans with psychosis or mental illness other than psychosis. We are...

  18. 77 FR 42909 - Presumption of Insurable Interest for Same-Sex Domestic Partners

    Science.gov (United States)

    2012-07-20

    ... do not fall within the presumptive classes. The commenter suggested that OPM has merely replaced one... beneficiary's date of birth. * * * * * PART 842--FEDERAL EMPLOYEES RETIREMENT SYSTEM--BASIC ANNUITY 0 3. The.... 842.605 Election of insurable interest rate. * * * * * (e) An insurable interest rate may be elected...

  19. 77 FR 76170 - Presumption of Exposure to Herbicides for Blue Water Navy Vietnam Veterans Not Supported

    Science.gov (United States)

    2012-12-26

    ... during the Vietnam War. After careful review of the IOM report, the Secretary determines that the... served in deep-water naval vessels off the coast of Vietnam during the Vietnam War are referred to as... DEPARTMENT OF VETERANS AFFAIRS Presumption of Exposure to Herbicides for Blue Water Navy Vietnam...

  20. 76 FR 41696 - Presumptive Service Connection for Diseases Associated With Service in the Southwest Asia Theater...

    Science.gov (United States)

    2011-07-15

    ... disorders, among the ``Gulf War Seabees'' and that some also have neural damage as a result of vibration...: Functional Gastrointestinal Disorders AGENCY: Department of Veterans Affairs. ACTION: Final rule. SUMMARY... gastrointestinal disorders (FGIDs) and clarifies that FGIDs fall within the scope of the existing presumptions of...

  1. Diagnosis of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2015-01-01

    We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

  2. Diagnosis of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.

    2015-03-01

    We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

  3. Diagnosis and microecological characteristics of aerobic vaginitis in outpatients based on preformed enzymes

    OpenAIRE

    Wang, Zhi-liang; Fu, Lan-yong; Xiong, Zheng-ai; Qin, Qin; Yu, Teng-hua; Wu, Yu-tong; Hua, Yuan-yuan; Zhang, Yong-hong

    2016-01-01

    Objective: Aerobic vaginitis (AV) is a recently proposed term for genital tract infection in women. The diagnosis of AV is mainly based on descriptive diagnostic criteria proposed by Donders and co-workers. The objective of this study is to report AV prevalence in southwest China using an objective assay kit based on preformed enzymes and also to determine its characteristics. Materials and methods: A total of 1948 outpatients were enrolled and tested by a commercial diagnostic kit to inve...

  4. Diagnosis of three types of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.

    2016-03-24

    We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in networks. For bases containing networks with at most 10 edges, we find sharp coefficients for linear bounds.

  5. Product quality management based on CNC machine fault prognostics and diagnosis

    Science.gov (United States)

    Kozlov, A. M.; Al-jonid, Kh M.; Kozlov, A. A.; Antar, Sh D.

    2018-03-01

    This paper presents a new fault classification model and an integrated approach to fault diagnosis which involves the combination of ideas of Neuro-fuzzy Networks (NF), Dynamic Bayesian Networks (DBN) and Particle Filtering (PF) algorithm on a single platform. In the new model, faults are categorized in two aspects, namely first and second degree faults. First degree faults are instantaneous in nature, and second degree faults are evolutional and appear as a developing phenomenon which starts from the initial stage, goes through the development stage and finally ends at the mature stage. These categories of faults have a lifetime which is inversely proportional to a machine tool's life according to the modified version of Taylor’s equation. For fault diagnosis, this framework consists of two phases: the first one is focusing on fault prognosis, which is done online, and the second one is concerned with fault diagnosis which depends on both off-line and on-line modules. In the first phase, a neuro-fuzzy predictor is used to take a decision on whether to embark Conditional Based Maintenance (CBM) or fault diagnosis based on the severity of a fault. The second phase only comes into action when an evolving fault goes beyond a critical threshold limit called a CBM limit for a command to be issued for fault diagnosis. During this phase, DBN and PF techniques are used as an intelligent fault diagnosis system to determine the severity, time and location of the fault. The feasibility of this approach was tested in a simulation environment using the CNC machine as a case study and the results were studied and analyzed.

  6. Observer agreement in the diagnosis of interstitial lung diseases based on HRCT scans

    International Nuclear Information System (INIS)

    Antunes, Viviane Baptista; Meirelles, Gustavo de Souza Portes; Jasinowodolinski, Dany; Verrastro, Carlos Gustavo Yuji; Torlai, Fabiola Goda

    2010-01-01

    Objective: to determine the interobserver and intraobserver agreement in the diagnosis of interstitial lung diseases (ILDs) based on HRCT scans and the impact of observer expertise, clinical data and confidence level on such agreement. Methods: two thoracic radiologists and two general radiologists independently reviewed the HRCT images of 58 patients with ILDs on two distinct occasions: prior to and after the clinical anamnesis. The radiologists selected up to three diagnostic hypotheses for each patient and defined the confidence level for these hypotheses. One of the thoracic and one of the general radiologists re-evaluated the same images up to three months after the first readings. In the coefficient analyses, the kappa statistic was used. Results: the thoracic and general radiologists, respectively, agreed on at least one diagnosis for each patient in 91.4% and 82.8% of the patients. The thoracic radiologists agreed on the most likely diagnosis in 48.3% (κ = 0.42) and 62.1% (κ = 0.58) of the cases, respectively, prior to and after the clinical anamnesis; likewise, the general radiologists agreed on the most likely diagnosis in 37.9% (κ 0.32) and 36.2% (κ = 0.30) of the cases. For the thoracic radiologist, the intraobserver agreement on the most likely diagnosis was 0.73 and 0.63 prior to and after the clinical anamnesis, respectively. That for the general radiologist was 0.38 and 0.42.The thoracic radiologists presented almost perfect agreement for the diagnostic hypotheses defined with the high confidence level. Conclusions: Interobserver and intraobserver agreement in the diagnosis of ILDs based on HRCT scans ranged from fair to almost perfect and was influenced by radiologist expertise, clinical history and confidence level. (author)

  7. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    Science.gov (United States)

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Non-tuberculous mycobacterial lung disease: diagnosis based on computed tomography of the chest

    Energy Technology Data Exchange (ETDEWEB)

    Kwak, Nakwon; Han, Sung Koo; Yim, Jae-Joon [Seoul National University College of Medicine, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul (Korea, Republic of); Lee, Chang Hyun; Lee, Hyun-Ju [Seoul National University College of Medicine, Department of Radiology, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Kang, Young Ae [Yonsei University College of Medicine, Division of Pulmonology, Department of Internal Medicine, Severance Hospital, Institute of Chest Diseases, Seoul (Korea, Republic of); Lee, Jae Ho [Seoul National University Bundang Hospital, Department of Internal Medicine, Seongnam, Gyeonggi-do (Korea, Republic of)

    2016-12-15

    To elucidate the accuracy and inter-observer agreement of non-tuberculous mycobacterial lung disease (NTM-LD) diagnosis based on chest CT findings. Two chest radiologists and two pulmonologists interpreted chest CTs of 66 patients with NTM-LD, 33 with pulmonary tuberculosis and 33 with non-cystic fibrosis bronchiectasis. These observers selected one of these diagnoses for each case without knowing any clinical information except age and sex. Sensitivity and specificity were calculated according to degree of observer confidence. Inter-observer agreement was assessed using Fleiss' κ values. Multiple logistic regression was performed to elucidate which radiological features led to the correct diagnosis. The sensitivity of NTM-LD diagnosis was 56.4 % (95 % CI 47.9-64.7) and specificity 80.3 % (73.1-86.0). The specificity of NTM-LD diagnosis increased with confidence: 44.4 % (20.5-71.3) for possible, 77.4 % (67.4-85.0) for probable, 95.2 % (87.2-98.2) for definite (P < 0.001) diagnoses. Inter-observer agreement for NTM-LD diagnosis was moderate (κ = 0.453). Tree-in-bud pattern (adjusted odds ratio [aOR] 6.24, P < 0.001), consolidation (aOR 1.92, P = 0.036) and atelectasis (aOR 3.73, P < 0.001) were associated with correct NTM-LD diagnoses, whereas presence of pleural effusion (aOR 0.05, P < 0.001) led to false diagnoses. NTM-LD diagnosis based on chest CT findings is specific but not sensitive. (orig.)

  9. Common variable immunodeficiency in three horses with presumptive bacterial meningitis.

    Science.gov (United States)

    Pellegrini-Masini, Alessandra; Bentz, Amy I; Johns, Imogen C; Parsons, Corrina S; Beech, Jill; Whitlock, Robert H; Flaminio, M Julia B F

    2005-07-01

    Three adult horses were evaluated for signs of musculoskeletal pain, dullness, ataxia, and seizures. A diagnosis of bacterial meningitis was made on the basis of results of CSF analysis. Because primary bacterial meningitis is so rare in adult horses without any history of generalized sepsis or trauma, immune function testing was pursued. Flow cytometric phenotyping of peripheral blood lymphocytes was performed, and proliferation of peripheral blood lymphocytes in response to concanavalin A, phytohemagglutinin, pokeweed mitogen, and lipopolysaccharide was determined. Serum IgA, IgM, and IgG concentrations were measured by means of radial immunodiffusion, and serum concentrations of IgG isotypes were assessed with a capture antibody ELISA. Serum tetanus antibody concentrations were measured before and 1 month after tetanus toxoid administration. Phagocytosis and oxidative burst activity of isolated peripheral blood phagocytes were evaluated by means of simultaneous flow cytometric analysis. Persistent B-cell lymphopenia, hypogammaglobulinemia, and abnormal in vitro responses to mitogens were detected in all 3 horses, and a diagnosis of common variable immunodeficiency was made.

  10. A diagnosis-based clinical decision rule for spinal pain part 2: review of the literature

    Directory of Open Access Journals (Sweden)

    Hurwitz Eric L

    2008-08-01

    Full Text Available Abstract Background Spinal pain is a common and often disabling problem. The research on various treatments for spinal pain has, for the most part, suggested that while several interventions have demonstrated mild to moderate short-term benefit, no single treatment has a major impact on either pain or disability. There is great need for more accurate diagnosis in patients with spinal pain. In a previous paper, the theoretical model of a diagnosis-based clinical decision rule was presented. The approach is designed to provide the clinician with a strategy for arriving at a specific working diagnosis from which treatment decisions can be made. It is based on three questions of diagnosis. In the current paper, the literature on the reliability and validity of the assessment procedures that are included in the diagnosis-based clinical decision rule is presented. Methods The databases of Medline, Cinahl, Embase and MANTIS were searched for studies that evaluated the reliability and validity of clinic-based diagnostic procedures for patients with spinal pain that have relevance for questions 2 (which investigates characteristics of the pain source and 3 (which investigates perpetuating factors of the pain experience. In addition, the reference list of identified papers and authors' libraries were searched. Results A total of 1769 articles were retrieved, of which 138 were deemed relevant. Fifty-one studies related to reliability and 76 related to validity. One study evaluated both reliability and validity. Conclusion Regarding some aspects of the DBCDR, there are a number of studies that allow the clinician to have a reasonable degree of confidence in his or her findings. This is particularly true for centralization signs, neurodynamic signs and psychological perpetuating factors. There are other aspects of the DBCDR in which a lesser degree of confidence is warranted, and in which further research is needed.

  11. Detection of presumptive Bacillus cereus in the Irish dairy farm environment

    Directory of Open Access Journals (Sweden)

    O’Connell A.

    2016-12-01

    Full Text Available The objective of the study was to isolate potential Bacillus cereus sensu lato (B. cereus s.l. from a range of farm environments. Samples of tap water, milking equipment rinse water, milk sediment filter, grass, soil and bulk tank milk were collected from 63 farms. In addition, milk liners were swabbed at the start and the end of milking, and swabs were taken from cows’ teats prior to milking. The samples were plated on mannitol egg yolk polymyxin agar (MYP and presumptive B. cereus s.l. colonies were isolated and stored in nutrient broth with 20% glycerol and frozen at -80 °C. These isolates were then plated on chromogenic medium (BACARA and colonies identified as presumptive B. cereus s.l. on this medium were subjected to 16S ribosomal RNA (rRNA sequencing. Of the 507 isolates presumed to be B. cereus s.l. on the basis of growth on MYP, only 177 showed growth typical of B. cereus s.l. on BACARA agar. The use of 16S rRNA sequencing to identify isolates that grew on BACARA confirmed that the majority of isolates belonged to B. cereus s.l. A total of 81 of the 98 isolates sequenced were tentatively identified as presumptive B. cereus s.l. Pulsed-field gel electrophoresis was carried out on milk and soil isolates from seven farms that were identified as having presumptive B. cereus s.l. No pulsotype was shared by isolates from soil and milk on the same farm. Presumptive B. cereus s.l. was widely distributed within the dairy farm environment.

  12. Surface plasmon resonance based biosensor: A new platform for rapid diagnosis of livestock diseases

    Directory of Open Access Journals (Sweden)

    Pravas Ranjan Sahoo

    2016-12-01

    Full Text Available Surface plasmon resonance (SPR based biosensors are the most advanced and developed optical label-free biosensor technique used for powerful detection with vast applications in environmental protection, biotechnology, medical diagnostics, drug screening, food safety, and security as well in livestock sector. The livestock sector which contributes the largest economy of India, harbors many bacterial, viral, and fungal diseases impacting a great loss to the production and productive potential which is a major concern in both small and large ruminants. Hence, an accurate, sensitive, and rapid diagnosis is required for prevention of these above-mentioned diseases. SPR based biosensor assay may fulfill the above characteristics which lead to a greater platform for rapid diagnosis of different livestock diseases. Hence, this review may give a detail idea about the principle, recent development of SPR based biosensor techniques and its application in livestock sector.

  13. FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Pelin GORGEL

    2013-01-01

    Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

  14. A Case-Based Study with Radiologists Performing Diagnosis Tasks in Virtual Reality.

    Science.gov (United States)

    Venson, José Eduardo; Albiero Berni, Jean Carlo; Edmilson da Silva Maia, Carlos; Marques da Silva, Ana Maria; Cordeiro d'Ornellas, Marcos; Maciel, Anderson

    2017-01-01

    In radiology diagnosis, medical images are most often visualized slice by slice. At the same time, the visualization based on 3D volumetric rendering of the data is considered useful and has increased its field of application. In this work, we present a case-based study with 16 medical specialists to assess the diagnostic effectiveness of a Virtual Reality interface in fracture identification over 3D volumetric reconstructions. We developed a VR volume viewer compatible with both the Oculus Rift and handheld-based head mounted displays (HMDs). We then performed user experiments to validate the approach in a diagnosis environment. In addition, we assessed the subjects' perception of the 3D reconstruction quality, ease of interaction and ergonomics, and also the users opinion on how VR applications can be useful in healthcare. Among other results, we have found a high level of effectiveness of the VR interface in identifying superficial fractures on head CTs.

  15. A novel diagnosis method for a Hall plates-based rotary encoder with a magnetic concentrator.

    Science.gov (United States)

    Meng, Bumin; Wang, Yaonan; Sun, Wei; Yuan, Xiaofang

    2014-07-31

    In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly.

  16. Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Xiaoming Xu

    2017-01-01

    Full Text Available In traditional principle component analysis (PCA, because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

  17. Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine

    Science.gov (United States)

    Antoni, Jérôme; Griffaton, Julien; André, Hugo; Avendaño-Valencia, Luis David; Bonnardot, Frédéric; Cardona-Morales, Oscar; Castellanos-Dominguez, German; Daga, Alessandro Paolo; Leclère, Quentin; Vicuña, Cristián Molina; Acuña, David Quezada; Ompusunggu, Agusmian Partogi; Sierra-Alonso, Edgar F.

    2017-12-01

    This paper presents the content and outcomes of the Safran contest organized during the International Conference Surveillance 8, October 20-21, 2015, at the Roanne Institute of Technology, France. The contest dealt with the diagnosis of a civil aircraft engine based on vibration data measured in a transient operating mode and provided by Safran. Based on two independent exercises, the contest offered the possibility to benchmark current diagnostic methods on real data supplemented with several challenges. Outcomes of seven competing teams are reported and discussed. The object of the paper is twofold. It first aims at giving a picture of the current state-of-the-art in vibration-based diagnosis of rolling-element bearings in nonstationary operating conditions. Second, it aims at providing the scientific community with a benchmark and some baseline solutions. In this respect, the data used in the contest are made available as supplementary material.

  18. A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine(SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

  19. Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

    Science.gov (United States)

    Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

    2017-06-01

    This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

  20. The Usefulness of Clinical-Practice-Based Laboratory Data in Facilitating the Diagnosis of Dengue Illness

    Directory of Open Access Journals (Sweden)

    Jien-Wei Liu

    2013-01-01

    Full Text Available Alertness to dengue and making a timely diagnosis is extremely important in the treatment of dengue and containment of dengue epidemics. We evaluated the complementary role of clinical-practice-based laboratory data in facilitating suspicion/diagnosis of dengue. One hundred overall dengue (57 dengue fever [DF] and 43 dengue hemorrhagic fever [DHF] cases and another 100 nondengue cases (78 viral infections other than dengue, 6 bacterial sepsis, and 16 miscellaneous diseases were analyzed. We separately compared individual laboratory variables (platelet count [PC] , prothrombin time [PT], activated partial thromboplastin time [APTT], alanine aminotransferase [ALT], and aspartate aminotransferase [AST] and varied combined variables of DF and/or DHF cases with the corresponding ones of nondengue cases. The sensitivity, specificity, accuracy, positive predictive value (PPV, and negative predictive value (NPV in the diagnosis of DF and/or DHF were measured based on these laboratory variables. While trade-off between sensitivity and specificity, and/or suboptimal PPV/NPV was found at measurements using these variables, prolonged APTT + normal PT + PC < 100 × 109 cells/L had a favorable sensitivity, specificity, PPV, and NPV in diagnosis of DF and/or DHF. In conclusion, these data suggested that prolonged APTT + normal PT + PC < 100 × 109 cells/L is useful in evaluating the likelihood of DF and/or DHF.

  1. The PCR-Based Diagnosis of Central Nervous System Tuberculosis: Up to Date

    Directory of Open Access Journals (Sweden)

    Teruyuki Takahashi

    2012-01-01

    Full Text Available Central nervous system (CNS tuberculosis, particularly tuberculous meningitis (TBM, is the severest form of Mycobacterium tuberculosis (M.Tb infection, causing death or severe neurological defects in more than half of those affected, in spite of recent advancements in available anti-tuberculosis treatment. The definitive diagnosis of CNS tuberculosis depends upon the detection of M.Tb bacilli in the cerebrospinal fluid (CSF. At present, the diagnosis of CNS tuberculosis remains a complex issue because the most widely used conventional “gold standard” based on bacteriological detection methods, such as direct smear and culture identification, cannot rapidly detect M.Tb in CSF specimens with sufficient sensitivity in the acute phase of TBM. Recently, instead of the conventional “gold standard”, the various molecular-based methods including nucleic acid amplification (NAA assay technique, particularly polymerase chain reaction (PCR assay, has emerged as a promising new method for the diagnosis of CNS tuberculosis because of its rapidity, sensitivity and specificity. In addition, the innovation of nested PCR assay technique is worthy of note given its contribution to improve the diagnosis of CNS tuberculosis. In this review, an overview of recent progress of the NAA methods, mainly highlighting the PCR assay technique, was presented.

  2. Landmark-based deep multi-instance learning for brain disease diagnosis.

    Science.gov (United States)

    Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

    2018-01-01

    In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. A fault diagnosis and operation advising cooperative expert system based on multi-agent technology

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, W.; Bai, X.; Ding, J.; Fang, Z.; Li, Z. [China Electric Power Research Inst., Haidian District, Beijing (China)

    2006-07-01

    Power systems are becoming more and more complex. In addition, the amount of real-time alarm messages from the supervisory control and data acquisition, energy management systems and wide area measurement systems about switchgear and protection are also increasing to a point far beyond the operator's capacity to digest the information. Research and development of a fault diagnosis system is necessary for the timely identification of fault or malfunctioning devices and for realizing the automation functions of dynamic supervisory control system. The prevailing fault diagnosis approaches in power systems include the expert system, artificial neural network, and fault diagnosis based on optimal theory. This paper discussed the advantages and disadvantages of each of these approaches for diagnosing faults. The paper also proposed a new fault diagnosis and operational processing approach based on a cooperative expert system combined with a multi-agent architecture. For solving complex and correlative faults, the cooperative expert system can overcome the deficiency of a single expert system. It can be used not only for diagnosing complex faults in real time but also in providing timely operational advice. The proposed system has been used successfully in a district power grid in China's Shangdong province for a year. 9 refs., 4 figs.

  4. The standard diagnosis, treatment, and follow-up of gastrointestinal stromal tumors based on guidelines.

    Science.gov (United States)

    Nishida, Toshirou; Blay, Jean-Yves; Hirota, Seiichi; Kitagawa, Yuko; Kang, Yoon-Koo

    2016-01-01

    Although gastrointestinal stromal tumors (GISTs) are a rare type of cancer, they are the commonest sarcoma in the gastrointestinal tract. Molecularly targeted therapy, such as imatinib therapy, has revolutionized the treatment of advanced GIST and facilitates scientific research on GIST. Nevertheless, surgery remains a mainstay of treatment to obtain a permanent cure for GIST even in the era of targeted therapy. Many GIST guidelines have been published to guide the diagnosis and treatment of the disease. We review current versions of GIST guidelines published by the National Comprehensive Cancer Network, by the European Society for Medical Oncology, and in Japan. All clinical practice guidelines for GIST include recommendations based on evidence as well as on expert consensus. Most of the content is very similar, as represented by the following examples: GIST is a heterogeneous disease that may have mutations in KIT, PDGFRA, HRAS, NRAS, BRAF, NF1, or the succinate dehydrogenase complex, and these subsets of tumors have several distinctive features. Although there are some minor differences among the guidelines--for example, in the dose of imatinib recommended for exon 9-mutated GIST or the efficacy of antigen retrieval via immunohistochemistry--their common objectives regarding diagnosis and treatment are not only to improve the diagnosis of GIST and the prognosis of patients but also to control medical costs. This review describes the current standard diagnosis, treatment, and follow-up of GISTs based on the recommendations of several guidelines and expert consensus.

  5. Neural network based expert system for fault diagnosis of particle accelerators

    International Nuclear Information System (INIS)

    Dewidar, M.M.

    1997-01-01

    Particle accelerators are generators that produce beams of charged particles, acquiring different energies, depending on the accelerator type. The MGC-20 cyclotron is a cyclic particle accelerator used for accelerating protons, deuterons, alpha particles, and helium-3 to different energies. Its applications include isotope production, nuclear reaction, and mass spectroscopy studies. It is a complicated machine, it consists of five main parts, the ion source, the deflector, the beam transport system, the concentric and harmonic coils, and the radio frequency system. The diagnosis of this device is a very complex task. it depends on the conditions of 27 indicators of the control panel of the device. The accurate diagnosis can lead to a high system reliability and save maintenance costs. so an expert system for the cyclotron fault diagnosis is necessary to be built. In this thesis , a hybrid expert system was developed for the fault diagnosis of the MGC-20 cyclotron. Two intelligent techniques, multilayer feed forward back propagation neural network and the rule based expert system, are integrated as a pre-processor loosely coupled model to build the proposed hybrid expert system. The architecture of the developed hybrid expert system consists of two levels. The first level is two feed forward back propagation neural networks, used for isolating the faulty part of the cyclotron. The second level is the rule based expert system, used for troubleshooting the faults inside the isolated faulty part. 4-6 tabs., 4-5 figs., 36 refs

  6. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    Science.gov (United States)

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

    Science.gov (United States)

    Jrad, Nisrine; Grall-Maës, Edith; Beauseroy, Pierre

    2009-01-01

    Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers. PMID:19584932

  8. An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming.

    Science.gov (United States)

    Zhang, Li; Chen, Jiasheng; Gao, Chunming; Liu, Chuanmiao; Xu, Kuihua

    2018-03-16

    Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics. In this study, the prediction of HCC was made based on these six biomarkers (195 HCC patients and 215 non-HCC controls) by setting up optimal joint models with GEP. The GEP model discriminated 353 out of 410 subjects, representing a determination coefficient of 86.28% (283/328) and 85.37% (70/82) for training and test sets, respectively. Compared to the results from the support vector machine, the artificial neural network, and the multilayer perceptron, GEP showed a better outcome. The results suggested that GEP modeling was a promising and excellent tool in diagnosis of hepatocellular carcinoma, and it could be widely used in HCC auxiliary diagnosis. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma.

  9. Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

    International Nuclear Information System (INIS)

    Ben Rabah, N; Saddem, R; Carre-Menetrier, V; Ben Hmida, F; Tagina, M

    2017-01-01

    Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach. (paper)

  10. A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

    Directory of Open Access Journals (Sweden)

    Gang Ma

    2015-01-01

    Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

  11. Taxing bads by taxing goods. Towards efficient pollution control with presumptive charges

    International Nuclear Information System (INIS)

    Eskeland, G.S.; Devarajan, S.

    1995-01-01

    A strong case is made for relying on a mix of indirect pollution control instruments - those which tax or regulate activities associated with emissions - rather than taxing the emissions themselves. They show that indirect instruments that reduce the scale of output (such as a tax on output or on polluting inputs) can be important complementary measures to emissions standards that reduce the level of emissions per unit of output. In this way, the effects of an optimal emission fee can be mimicked fairly well. The optimal mix of indirect instruments, however, requires knowledge of the 'cleaner' technologies (the ease with which emissions per unit of output can be reduced) as well as the sensitivity of demand to prices (the ease with which the scale of output can be reduced). This contrasts with the optimal emission fee, which relies only on information about emissions. The authors present empirically-based case studies to illustrate the consequences of employing a combination of presumptive charges and emissions standards. A recurring theme throughout their contribution is that the taxation of fuel use, due to the interaction between fuel use and emissions, can serve as a powerful indirect instrument to supplement pollution standards in controlling air pollution. In the case of automobiles, for example, they show that failing to employ gasoline taxes (which ensure that emissions are cut through not only cleaner cars but also fewer trips) in Mexico City would significantly harm welfare, even when regulatory standards (catalytic converters) are in place. In the case of point-source pollution, they calculate that significant potential exists for altering the fuel mix of industries in Indonesia and Chile by taxing 'dirtier' fuels. Furthermore, they show that, in the case of Indonesia, the general-equilibrium consequences of such a change in the tax structure are similar, though somewhat dampened, compared to what is indicated by partial-equilibrium models

  12. A Fault Diagnosis Model of Surface to Air Missile Equipment Based on Wavelet Transformation and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhheng Ni

    2016-01-01

    Full Text Available At present, the fault signals of surface to air missile equipment are hard to collect and the accuracy of fault diagnosis is very low. To solve the above problems, based on the superiority of wavelet transformation on processing non-stationary signals and the advantage of SVM on pattern classification, this paper proposes a fault diagnosis model and takes the typical analog circuit diagnosis of one power distribution system as an example to verify the fault diagnosis model based on Wavelet Transformation and SVM. The simulation results show that the model is able to achieve fault diagnosis based on a small amount of training samples, which improves the accuracy of fault diagnosis.

  13. Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

    Science.gov (United States)

    Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

    2018-04-20

    Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

  14. Elastofibroma dorsi: MRI diagnosis in a young girl

    Energy Technology Data Exchange (ETDEWEB)

    Devaney, D. [Dept. of Histopathology, Hospital for Sick Children, London (United Kingdom); Livesley, P. [Dept. of Orthopaedics, Hospital for Sick Children, London (United Kingdom); Shaw, D. [Dept. of Paediatric Radiology, Hospital for Sick Children, London (United Kingdom)

    1995-06-01

    With indications for computerised imaging expanding, elastofibroma dorsi will probably be seen more frequently. This report describes an elastofibroma presenting in an 11-year-old girl and its appearance by magnetic resonance imaging. Presumptive diagnosis by magnetic resonance imaging may prevent unnecessary radical surgery. (orig.)

  15. Elastofibroma dorsi: MRI diagnosis in a young girl

    International Nuclear Information System (INIS)

    Devaney, D.; Livesley, P.; Shaw, D.

    1995-01-01

    With indications for computerised imaging expanding, elastofibroma dorsi will probably be seen more frequently. This report describes an elastofibroma presenting in an 11-year-old girl and its appearance by magnetic resonance imaging. Presumptive diagnosis by magnetic resonance imaging may prevent unnecessary radical surgery. (orig.)

  16. A Method of Rotating Machinery Fault Diagnosis Based on the Close Degree of Information Entropy

    Institute of Scientific and Technical Information of China (English)

    GENG Jun-bao; HUANG Shu-hong; JIN Jia-shan; CHEN Fei; LIU Wei

    2006-01-01

    This paper presents a method of rotating machinery fault diagnosis based on the close degree of information entropy. In the view of the information entropy, we introduce four information entropy features of the rotating machinery, which describe the vibration condition of the machinery. The four features are, respectively, denominated as singular spectrum entropy, power spectrum entropy, wavelet space state feature entropy and wavelet power spectrum entropy. The value scopes of the four information entropy features of the rotating machinery in some typical fault conditions are gained by experiments, which can be acted as the standard features of fault diagnosis. According to the principle of the shorter distance between the more similar models, the decision-making method based on the close degree of information entropy is put forward to deal with the recognition of fault patterns. We demonstrate the effectiveness of this approach in an instance involving the fault pattern recognition of some rotating machinery.

  17. Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhou Qing

    2017-01-01

    Full Text Available Sensorless Drive Diagnosis can be used to assess the process data without the need for additional cost-intensive sensor technology, and you can understand the synchronous motor and connecting parts of the damaged state. Considering the number of features involved in the process data, it is necessary to perform feature selection and reduce the data dimension in the process of fault detection. In this paper, the MOEA / D algorithm based on multi-objective optimization is used to obtain the weight vector of all the features in the original data set. It is more suitable to classify or make decisions based on these features. In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive, which makes the fault detection and elimination process more targeted.

  18. A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

    Energy Technology Data Exchange (ETDEWEB)

    Round, A R [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Wilkinson, S J [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Hall, C J [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Rogers, K D [Department of Materials and Medical Sciences, Cranfield University, Swindon, SN6 8LA (United Kingdom); Glatter, O [Department of Chemistry, University of Graz (Austria); Wess, T [School of Optometry and Vision Sciences, Cardiff University, Cardiff CF10 3NB, Wales (United Kingdom); Ellis, I O [Nottingham City Hospital, Nottingham (United Kingdom)

    2005-09-07

    Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.

  19. A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

    International Nuclear Information System (INIS)

    Round, A R; Wilkinson, S J; Hall, C J; Rogers, K D; Glatter, O; Wess, T; Ellis, I O

    2005-01-01

    Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique

  20. Fault diagnosis for tilting-pad journal bearing based on SVD and LMD

    Directory of Open Access Journals (Sweden)

    Zhang Xiaotao

    2016-01-01

    Full Text Available Aiming at fault diagnosis for tilting-pad journal bearing with fluid support developed recently, a new method based on singular value decomposition (SVD and local mean decomposition (LMD is proposed. First, the phase space reconstruction of Hankel matrix and SVD method are used as pre-filter process unit to reduce the random noises in the original signal. Then the purified signal is decomposed by LMD into a series of production functions (PFs. Based on PFs, time frequency map and marginal spectrum can be obtained for fault diagnosis. Finally, this method is applied to numerical simulation and practical experiment data. The results show that the proposed method can effectively detect fault features of tilting-pad journal bearing.

  1. A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

    Science.gov (United States)

    Round, A. R.; Wilkinson, S. J.; Hall, C. J.; Rogers, K. D.; Glatter, O.; Wess, T.; Ellis, I. O.

    2005-09-01

    Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.

  2. Application of energies of optimal frequency bands for fault diagnosis based on modified distance function

    Energy Technology Data Exchange (ETDEWEB)

    Zamanian, Amir Hosein [Southern Methodist University, Dallas (United States); Ohadi, Abdolreza [Amirkabir University of Technology (Tehran Polytechnic), Tehran (Iran, Islamic Republic of)

    2017-06-15

    Low-dimensional relevant feature sets are ideal to avoid extra data mining for classification. The current work investigates the feasibility of utilizing energies of vibration signals in optimal frequency bands as features for machine fault diagnosis application. Energies in different frequency bands were derived based on Parseval's theorem. The optimal feature sets were extracted by optimization of the related frequency bands using genetic algorithm and a Modified distance function (MDF). The frequency bands and the number of bands were optimized based on the MDF. The MDF is designed to a) maximize the distance between centers of classes, b) minimize the dispersion of features in each class separately, and c) minimize dimension of extracted feature sets. The experimental signals in two different gearboxes were used to demonstrate the efficiency of the presented technique. The results show the effectiveness of the presented technique in gear fault diagnosis application.

  3. Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2018-01-01

    Full Text Available Conflict management in Dempster-Shafer theory (D-S theory is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.

  4. HIV-infected presumptive tuberculosis patients without tuberculosis: How many are eligible for antiretroviral therapy in Karnataka, India?

    Directory of Open Access Journals (Sweden)

    Ajay M.V. Kumar

    2017-03-01

    Full Text Available For certain subgroups within people living with the human immunodeficiency virus (HIV [active tuberculosis (TB, pregnant women, children <5 years old, and serodiscordant couples], the World Health Organization recommends antiretroviral therapy (ART irrespective of CD4 count. Another subgroup which has received increased attention is “HIV-infected presumptive TB patients without TB”. In this study, we assess the proportion of HIV-infected presumptive TB patients eligible for ART in Karnataka State (population 60 million, India. This was a cross-sectional analysis of data of HIV-infected presumptive TB patients diagnosed in May 2015 abstracted from national TB and HIV program records. Of 42,585 presumptive TB patients, 28,964 (68% were tested for HIV and 2262 (8% were HIV positive. Of the latter, 377 (17% had active TB. Of 1885 “presumptive TB patients without active TB”, 1100 (58% were already receiving ART. Of the remaining 785 who were not receiving ART, 617 (79% were assessed for ART eligibility and of those, 548 (89% were eligible for ART. About 90% of “HIV-infected presumptive TB patients without TB” were eligible for ART. This evidence supports a public health approach of starting all “HIV-infected presumptive TB patients without TB” on ART irrespective of CD4 count in line with global thinking about ‘test and treat’.

  5. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Muhammad Sohaib

    2017-12-01

    Full Text Available Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE-based deep neural networks (DNNs to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs and backpropagation neural networks (BPNNs.

  6. A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

    Science.gov (United States)

    Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

    2015-08-01

    Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

  7. Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Shu-zhi Gao

    2013-01-01

    Full Text Available Polyvinyl chloride (PVC polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay. Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks. The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively. Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set. Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method. The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system.

  8. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

    Science.gov (United States)

    Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon

    2017-12-11

    Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

  9. A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2011-01-01

    Full Text Available Abstract Background Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective. Methods In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples. Results Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs. In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer. Conclusions A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

  10. Direct costs of emergency medical care: a diagnosis-based case-mix classification system.

    Science.gov (United States)

    Baraff, L J; Cameron, J M; Sekhon, R

    1991-01-01

    To develop a diagnosis-based case mix classification system for emergency department patient visits based on direct costs of care designed for an outpatient setting. Prospective provider time study with collection of financial data from each hospital's accounts receivable system and medical information, including discharge diagnosis, from hospital medical records. Three community hospital EDs in Los Angeles County during selected times in 1984. Only direct costs of care were included: health care provider time, ED management and clerical personnel excluding registration, nonlabor ED expense including supplies, and ancillary hospital services. Indirect costs for hospitals and physicians, including depreciation and amortization, debt service, utilities, malpractice insurance, administration, billing, registration, and medical records were not included. Costs were derived by valuing provider time based on a formula using annual income or salary and fringe benefits, productivity and direct care factors, and using hospital direct cost to charge ratios. Physician costs were based on a national study of emergency physician income and excluded practice costs. Patients were classified into one of 216 emergency department groups (EDGs) on the basis of the discharge diagnosis, patient disposition, age, and the presence of a limited number of physician procedures. Total mean direct costs ranged from $23 for follow-up visit to $936 for trauma, admitted, with critical care procedure. The mean total direct costs for the 16,771 nonadmitted patients was $69. Of this, 34% was for ED costs, 45% was for ancillary service costs, and 21% was for physician costs. The mean total direct costs for the 1,955 admitted patients was $259. Of this, 23% was for ED costs, 63% was for ancillary service costs, and 14% was for physician costs. Laboratory and radiographic services accounted for approximately 85% of all ancillary service costs and 38% of total direct costs for nonadmitted patients

  11. Explore the Possibility of Early Clinical Diagnosis of Endocrine Ophthalmopathy Based on Eye Symptoms of Hyperthyroidism

    OpenAIRE

    V. G. Likhvantseva; E. A. Rudenko; S. G. Kapkova; V. A. Vygodin

    2016-01-01

    Purpose: to study the possibility of early clinical diagnosis of endocrine ophthalmopathy based on ocular symptoms of hyperthyroidism. Patients and methods: we analyzed the prevalence of ocular symptoms of hyperthyroidism in 139 patients (278 orbits) with newly diagnosed endocrine ophthalmopathy (group 1), developed on the background of diffuse toxic goiter. The comparison group consisted of 80 patients (160 orbits) with newly diagnosed diffuse toxic goiter with no radiographic evidence of en...

  12. Research on fault diagnosis of nuclear power plants based on genetic algorithms and fuzzy logic

    International Nuclear Information System (INIS)

    Zhou Yangping; Zhao Bingquan

    2001-01-01

    Based on genetic algorithms and fuzzy logic and using expert knowledge, mini-knowledge tree model and standard signals from simulator, a new fuzzy-genetic method is developed to fault diagnosis in nuclear power plants. A new replacement method of genetic algorithms is adopted. Fuzzy logic is used to calculate the fitness of the strings in genetic algorithms. Experiments on the simulator show it can deal with the uncertainty and the fuzzy factor

  13. Control and fault diagnosis based sliding mode observer of a multicellular converter: Hybrid approach

    KAUST Repository

    Benzineb, Omar

    2013-01-01

    In this article, the diagnosis of a three cell converter is developed. The hybrid nature of the system represented by the presence of continuous and discrete dynamics is taken into account in the control design. The idea is based on using a hybrid control and an observer-type sliding mode to generate residuals from the observation errors of the system. The simulation results are presented at the end to illustrate the performance of the proposed approach. © 2013 FEI STU.

  14. Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

    Science.gov (United States)

    Zhang, Han

    Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

  15. Knowledge-based and integrated monitoring and diagnosis in autonomous power systems

    Science.gov (United States)

    Momoh, J. A.; Zhang, Z. Z.

    1990-01-01

    A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.

  16. A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine

    Directory of Open Access Journals (Sweden)

    Xiao-hui He

    2016-01-01

    Full Text Available To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM. Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form representation of the distribution underlying the training data, and it is very convenient for modeling high-dimensional real-valued data. Experiments on 10 different data sets verify the performance of the proposed method. The superiority of Gaussian RBM classifier is also confirmed by comparing with other classifiers, such as extreme learning machine, support vector machine, and deep belief network. The robustness of the proposed method is also studied in this paper. It can be concluded that the proposed method can realize the bearing fault diagnosis accurately and effectively.

  17. Fault diagnosis of an intelligent hydraulic pump based on a nonlinear unknown input observer

    Directory of Open Access Journals (Sweden)

    Zhonghai MA

    2018-02-01

    Full Text Available Hydraulic piston pumps are commonly used in aircraft. In order to improve the viability of aircraft and energy efficiency, intelligent variable pressure pump systems have been used in aircraft hydraulic systems more and more widely. Efficient fault diagnosis plays an important role in improving the reliability and performance of hydraulic systems. In this paper, a fault diagnosis method of an intelligent hydraulic pump system (IHPS based on a nonlinear unknown input observer (NUIO is proposed. Different from factors of a full-order Luenberger-type unknown input observer, nonlinear factors of the IHPS are considered in the NUIO. Firstly, a new type of intelligent pump is presented, the mathematical model of which is established to describe the IHPS. Taking into account the real-time requirements of the IHPS and the special structure of the pump, the mechanism of the intelligent pump and failure modes are analyzed and two typical failure modes are obtained. Furthermore, a NUIO of the IHPS is performed based on the output pressure and swashplate angle signals. With the residual error signals produced by the NUIO, online intelligent pump failure occurring in real-time can be detected. Lastly, through analysis and simulation, it is confirmed that this diagnostic method could accurately diagnose and isolate those typical failure modes of the nonlinear IHPS. The method proposed in this paper is of great significance in improving the reliability of the IHPS. Keywords: Fault diagnosis, Hydraulic piston pump, Model-based, Nonlinear unknown input observer (NUIO, Residual error

  18. Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters

    Science.gov (United States)

    Lu, Yunsong; Wang, Fuli; Jia, Mingxing; Qi, Yuanchen

    2016-12-01

    This paper concerns fault diagnosis of centrifugal compressor based on thermal parameters. An improved qualitative simulation (QSIM) based fault diagnosis method is proposed to diagnose the faults of centrifugal compressor in a gas-steam combined-cycle power plant (CCPP). The qualitative models under normal and two faulty conditions have been built through the analysis of the principle of centrifugal compressor. To solve the problem of qualitative description of the observations of system variables, a qualitative trend extraction algorithm is applied to extract the trends of the observations. For qualitative states matching, a sliding window based matching strategy which consists of variables operating ranges constraints and qualitative constraints is proposed. The matching results are used to determine which QSIM model is more consistent with the running state of system. The correct diagnosis of two typical faults: seal leakage and valve stuck in the centrifugal compressor has validated the targeted performance of the proposed method, showing the advantages of fault roots containing in thermal parameters.

  19. Computed tomography in the diagnosis of steroidal hepatopathy in a dog: case report

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, D.C; Costa, L.A.V.S.; Lopes, B.F.; Lanis, A.B.; Borlini, D.C.; Costa, F.S., E-mail: danielcapucho@gmail.co [Universidade Federal do Espirito Santo (UFES), Vitroria, ES (Brazil). Dept. de Medicina Veterinaria; Maia Junior, J.A. [Centro de Escolas de Formacao de Tecnicos em Radiologia, Vila Velha, ES (Brazil)

    2011-02-15

    It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyper attenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU). The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy. (author)

  20. Computed tomography in the diagnosis of steroidal hepatopathy in a dog: case report

    International Nuclear Information System (INIS)

    Oliveira, D.C; Costa, L.A.V.S.; Lopes, B.F.; Lanis, A.B.; Borlini, D.C.; Costa, F.S.

    2011-01-01

    It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyper attenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU). The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy. (author)

  1. Computed tomography in the diagnosis of steroidal hepatopathy in a dog: case report

    Directory of Open Access Journals (Sweden)

    D.C Oliveira

    2011-02-01

    Full Text Available It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyperattenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU. The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy

  2. Clinical Assessment of a Nocardia PCR-Based Assay for Diagnosis of Nocardiosis.

    Science.gov (United States)

    Rouzaud, Claire; Rodriguez-Nava, Véronica; Catherinot, Emilie; Méchaï, Frédéric; Bergeron, Emmanuelle; Farfour, Eric; Scemla, Anne; Poirée, Sylvain; Delavaud, Christophe; Mathieu, Daniel; Durupt, Stéphane; Larosa, Fabrice; Lengelé, Jean-Philippe; Christophe, Jean-Louis; Suarez, Felipe; Lortholary, Olivier; Lebeaux, David

    2018-06-01

    The diagnosis of nocardiosis, a severe opportunistic infection, is challenging. We assessed the specificity and sensitivity of a 16S rRNA Nocardia PCR-based assay performed on clinical samples. In this multicenter study (January 2014 to April 2015), patients who were admitted to three hospitals and had an underlying condition favoring nocardiosis, clinical and radiological signs consistent with nocardiosis, and a Nocardia PCR assay result for a clinical sample were included. Patients were classified as negative control (NC) (negative Nocardia culture results and proven alternative diagnosis or improvement at 6 months without anti- Nocardia treatment), positive control (PC) (positive Nocardia culture results), or probable nocardiosis (positive Nocardia PCR results, negative Nocardia culture results, and no alternative diagnosis). Sixty-eight patients were included; 47 were classified as NC, 8 as PC, and 13 as probable nocardiosis. PCR results were negative for 35/47 NC patients (74%). For the 12 NC patients with positive PCR results, the PCR assay had been performed with respiratory samples. These NC patients had chronic bronchopulmonary disease more frequently than did the NC patients with negative PCR results (8/12 patients [67%] versus 11/35 patients [31%]; P = 0.044). PCR results were positive for 7/8 PC patients (88%). There were 13 cases of probable nocardiosis, diagnosed solely using the PCR results; 9 of those patients (69%) had lung involvement (consolidation or nodule). Nocardia PCR testing had a specificity of 74% and a sensitivity of 88% for the diagnosis of nocardiosis. Nocardia PCR testing may be helpful for the diagnosis of nocardiosis in immunocompromised patients but interpretation of PCR results from respiratory samples is difficult, because the PCR assay may also detect colonization. Copyright © 2018 American Society for Microbiology.

  3. The evidence base regarding the experiences of and attitudes to preimplantation genetic diagnosis in prospective parents.

    Science.gov (United States)

    Cunningham, Jenny; Goldsmith, Lesley; Skirton, Heather

    2015-02-01

    Preimplantation genetic diagnosis was developed as an alternative to prenatal diagnosis for couples with a family history of genetic disease. After in vitro fertilization, the embryos can be analysed to ensure that only healthy embryos are transferred to the uterus. Past studies have suggested that couples who wish to avoid having a child with an inherited genetic condition look favourably on preimplantation genetic diagnosis as it prevents the need for termination of pregnancy following prenatal diagnosis of an affected fetus. However, it is important to understand the experiences of couples who have used or consider using this technique. To ascertain the current evidence base on this topic, we conducted a mixed methods systematic review. Four databases were searched for relevant peer-reviewed papers published between 2000 and 2013. Of 453 papers, nine satisfied the inclusion criteria and were assessed for quality. Results of nine papers were analysed and synthesised using a narrative approach. Three main themes emerged: (1) motivating factors; (2) emotional labour; (3) choices and uncertainty. The review has identified an emotional and difficult journey for couples pursuing preimplantation genetic diagnosis. While use of the technique gives hope to families who wish to prevent transmission of a genetic disease this is not without hard decision-making and periods of uncertainty. Lack of information was perceived as a barrier to access this reproductive option. Recommendations include: training and education in genetics for midwives who are the first point of contact for pregnant women; clinics to use a decision-making tool to emphasise the uncertainty involved in PGD and improved communication and psychological support to couples. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery.

    Science.gov (United States)

    Xue, Xiaoming; Zhou, Jianzhong

    2017-01-01

    To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Diagnosis of clinical samples spotted on FTA cards using PCR-based methods.

    Science.gov (United States)

    Jamjoom, Manal; Sultan, Amal H

    2009-04-01

    The broad clinical presentation of Leishmaniasis makes the diagnosis of current and past cases of this disease rather difficult. Differential diagnosis is important because diseases caused by other aetiologies and a clinical spectrum similar to that of leishmaniasis (e.g. leprosy, skin cancers and tuberculosis for CL; malaria and schistosomiasis for VL) are often present in endemic areas of endemicity. Presently, a variety of methods have been developed and tested to aid the identification and diagnosis of Leishmania. The advent of the PCR technology has opened new channels for the diagnosis of leishmaniasis in a variety of clinical materials. PCR is a simple, rapid procedure that has been adapted for diagnosis of leishmaniasis. A range of tools is currently available for the diagnosis and identification of leishmaniasis and Leishmania species, respectively. However, none of these diagnostic tools are examined and tested using samples spotted on FTA cards. Three different PCR-based approaches were examined including: kDNA minicircle, Leishmania 18S rRNA gene and PCR-RFLP of Intergenic region of ribosomal protein. PCR primers were designed that sit within the coding sequences of genes (relatively well conserved) but which amplify across the intervening intergenic sequence (relatively variable). These were used in PCR-RFLP on reference isolates of 10 of the most important Leishmania species: L. donovani, L. infantum, L. major & L. tropica. Digestion of PCR products with restriction enzymes produced species-specific restriction patterns allowed discrimination of reference isolates. The kDNA minicircle primers are highly sensitive in diagnosis of both bone marrow and skin smears from FTA cards. Leishmania 18S rRNA gene conserved region is sensitive in identification of bone marrow smear but less sensitive in diagnosing skin smears. The intergenic nested PCR-RFLP using P5 & P6 as well as P1 & P2 newly designed primers showed high level of reproducibility and sensitivity

  6. Diagnosis of Giardia infections by PCR-based methods in children of an endemic area

    Directory of Open Access Journals (Sweden)

    EB David

    2011-01-01

    Full Text Available The present study was designed to estimate the prevalence of Giardia infection in preschool- and school-aged children living in an endemic area. Fecal samples from 573 children were processed by zinc sulfate centrifugal flotation, centrifugal sedimentation (using a commercial device for fecal concentration - TF-Test kit® and polymerase chain reaction (PCR-based methods. Of the stool samples assessed, 277 (48.3% were positive for intestinal parasites and/or commensal protozoa. Centrifugal flotation presented the highest diagnostic sensitivity for Giardia infections. The kappa index revealed that both coproparasitological techniques closely agreed on the Giardia diagnosis (86% versus satisfactory (72% and poor (35% concordances for commensal protozoan and helminth infections, respectively. Concerning Giardia molecular diagnosis, from the 71 microscopy-positive samples, specific amplification of gdh and tpi fragments was noted in 68 (95.7% and 64 (90% samples, respectively. Amplification of gdh and tpi genes was observed, respectively, in 95.7% and 90% of microscopy-positive Giardia samples. For 144 microscopy-negative samples, gdh and tpi gene amplification products were obtained from 8.3% and 35.9% samples, respectively. The agreement between these genes was about 40%. The centrifuge-flotation based method was the most suitable means of Giardia diagnosis assessed in the present study by combining accuracy and low cost.

  7. Fault Diagnosis of an Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers

    DEFF Research Database (Denmark)

    Sardi, Hector Eloy Sanchez; Escobet, Teressa; Puig, Vicenc

    2015-01-01

    This paper proposes a model-based fault diagnosis (FD) approach for wind turbines and its application to a realistic wind turbine FD benchmark. The proposed FD approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown...... turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from the structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed FD approach has been validated on a 5-MW wind...

  8. Research on bearing fault diagnosis of large machinery based on mathematical morphology

    Science.gov (United States)

    Wang, Yu

    2018-04-01

    To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

  9. Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Lingli Jiang

    2011-01-01

    Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

  10. Verification test for on-line diagnosis algorithm based on noise analysis

    International Nuclear Information System (INIS)

    Tamaoki, T.; Naito, N.; Tsunoda, T.; Sato, M.; Kameda, A.

    1980-01-01

    An on-line diagnosis algorithm was developed and its verification test was performed using a minicomputer. This algorithm identifies the plant state by analyzing various system noise patterns, such as power spectral densities, coherence functions etc., in three procedure steps. Each obtained noise pattern is examined by using the distances from its reference patterns prepared for various plant states. Then, the plant state is identified by synthesizing each result with an evaluation weight. This weight is determined automatically from the reference noise patterns prior to on-line diagnosis. The test was performed with 50 MW (th) Steam Generator noise data recorded under various controller parameter values. The algorithm performance was evaluated based on a newly devised index. The results obtained with one kind of weight showed the algorithm efficiency under the proper selection of noise patterns. Results for another kind of weight showed the robustness of the algorithm to this selection. (orig.)

  11. A Privacy-Preserving Intelligent Medical Diagnosis System Based on Oblivious Keyword Search

    Directory of Open Access Journals (Sweden)

    Zhaowen Lin

    2017-01-01

    Full Text Available One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS, which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient.

  12. Research on the Diagnosis of Rotor Coupling Fault Based on Wavelet Packet and Local Fisher Discriminant

    Directory of Open Access Journals (Sweden)

    Guangbin Wang

    2014-09-01

    Full Text Available this article is for the coupling fault diagnosis of rotor system, and does in-depth analysis of the rotor unbalance and misalignment, and the fault formed by the coupling of these two. Through research, Rotor Coupling was found filled with rich features. In this paper, Wavelet packet de- noising ideas being introduced to the local Fisher discriminant analysis (LFDA, a new method of fault diagnosis based on Wavelet Packet and Local Fisher Discriminant is proposed. The technology of information fusion is applied to the data processing with coupling faults. By comparing and analyzing the algorithms effect of LE, LPP, FDA, LFDA and IOLFA through experiment, it shows that LE and LPP are unable to identify the fault, while FDA, LFDA has better identification, and Wavelet Packet and Local Fisher discriminant has the best effect.

  13. Bloodstains on Leather: Examination of False Negatives in Presumptive Test and Human Hemoglobin Test.

    Science.gov (United States)

    Castelló, Ana; Francès, Francesc; Verdú, Fernando

    2017-09-01

    Presumptive tests for blood are very simple and sensitive tests used in the search for evidence. They also provide initial information on the nature of stains. A second test can confirm their nature. However, these tests can present false-negative results for different reasons. Some of those reasons have been studied, while others, those caused by the substrate material that contains the stain, are less well known. This work studies the effect of one component of a leather substrate-quebracho extract-on presumptive and human hemoglobin blood tests. Assays were performed using samples of blood dilutions contaminated with quebracho extract and others formed on a substrate containing the contaminant. Results show an undoubted interference that causes false negatives and even visible to the naked eye stains and also indicate that some tests (phenolphthalein) are more affected than others. Examiners should be taken into account when working on this kind of substrates. © 2017 American Academy of Forensic Sciences.

  14. Naming and Shaming in Financial Market Regulations: A Violation of the Presumption of Innocence?

    Directory of Open Access Journals (Sweden)

    Juliette J.W. Pfaeltzer

    2014-01-01

    Full Text Available Naming and shaming in the financial markets has become a well-known enforcement tool by national supervisors both within and outside the EU. The Netherlands is one of the Member States which permits the publication of offences and administrative sanctions including the name of the offender. However, such publication practice might raise some concerns in the light of certain fundamental human rights. For instance, does naming and shaming violate the presumption of innocence? This article tries to answer this question by evaluating the Dutch publication regime under the Financial Supervision Act. Are the legal safeguards as provided under this Act sufficiently adequate to prevent an infringement of the presumption of innocence?

  15. Novel Agent Based-approach for Industrial Diagnosis: A Combined use Between Case-based Reasoning and Similarity Measure

    Directory of Open Access Journals (Sweden)

    Fatima Zohra Benkaddour

    2016-12-01

    Full Text Available In spunlace nonwovens industry, the maintenance task is very complex, it requires experts and operators collaboration. In this paper, we propose a new approach integrating an agent- based modelling with case-based reasoning that utilizes similarity measures and preferences module. The main purpose of our study is to compare and evaluate the most suitable similarity measure for our case. Furthermore, operators that are usually geographically dispersed, have to collaborate and negotiate to achieve mutual agreements, especially when their proposals (diagnosis lead to a conflicting situation. The experimentation shows that the suggested agent-based approach is very interesting and efficient for operators and experts who collaborate in INOTIS enterprise.

  16. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    Science.gov (United States)

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

  17. Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

    Science.gov (United States)

    Wu, Yixiao; Yang, Ran; Jia, Sen; Li, Zhanjun; Zhou, Zhiyang; Lou, Ting

    2014-01-01

    This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

  18. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.

    Science.gov (United States)

    Liu, Han; Zhou, Jianzhong; Zheng, Yang; Jiang, Wei; Zhang, Yuncheng

    2018-04-19

    As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  19. A case-oriented web-based training system for breast cancer diagnosis.

    Science.gov (United States)

    Huang, Qinghua; Huang, Xianhai; Liu, Longzhong; Lin, Yidi; Long, Xingzhang; Li, Xuelong

    2018-03-01

    Breast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor. We introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists. This online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value  .05). The online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

  20. European evidence-based recommendations for diagnosis and treatment of paediatric antiphospholipid syndrome: the SHARE initiative.

    Science.gov (United States)

    Groot, Noortje; de Graeff, Nienke; Avcin, Tadej; Bader-Meunier, Brigitte; Dolezalova, Pavla; Feldman, Brian; Kenet, Gili; Koné-Paut, Isabelle; Lahdenne, Pekka; Marks, Stephen D; McCann, Liza; Pilkington, Clarissa A; Ravelli, Angelo; van Royen-Kerkhof, Annet; Uziel, Yosef; Vastert, Sebastiaan J; Wulffraat, Nico M; Ozen, Seza; Brogan, Paul; Kamphuis, Sylvia; Beresford, Michael W

    2017-10-01

    Antiphospholipid syndrome (APS) is rare in children, and evidence-based guidelines are sparse. Consequently, management is mostly based on observational studies and physician's experience, and treatment regimens differ widely. The Single Hub and Access point for paediatric Rheumatology in Europe (SHARE) initiative was launched to develop diagnostic and management regimens for children and young adults with rheumatic diseases. Here, we developed evidence-based recommendations for diagnosis and treatment of paediatric APS. Evidence-based recommendations were developed using the European League Against Rheumatism standard operating procedure. Following a detailed systematic review of the literature, a committee of paediatric rheumatologists and representation of paediatric haematology with expertise in paediatric APS developed recommendations. The literature review yielded 1473 articles, of which 15 were valid and relevant. In total, four recommendations for diagnosis and eight for treatment of paediatric APS (including paediatric Catastrophic Antiphospholipid Syndrome) were accepted. Additionally, two recommendations for children born to mothers with APS were accepted. It was agreed that new classification criteria for paediatric APS are necessary, and APS in association with childhood-onset systemic lupus erythematosus should be identified by performing antiphospholipid antibody screening. Treatment recommendations included prevention of thrombotic events, and treatment recommendations for venous and/or arterial thrombotic events. Notably, due to the paucity of studies on paediatric APS, level of evidence and strength of the recommendations is relatively low. The SHARE initiative provides international, evidence-based recommendations for diagnosis and treatment for paediatric APS, facilitating improvement and uniformity of care. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use

  1. Utilizing DMAIC six sigma and evidence-based medicine to streamline diagnosis in chest pain.

    Science.gov (United States)

    Kumar, Sameer; Thomas, Kory M

    2010-01-01

    The purpose of this study was to quantify the difference between the current process flow model for a typical patient workup for chest pain and development of a new process flow model that incorporates DMAIC (define, measure, analyze, improve, control) Six Sigma and evidence-based medicine in a best practices model for diagnosis and treatment. The first stage, DMAIC Six Sigma, is used to highlight areas of variability and unnecessary tests in the current process flow for a patient presenting to the emergency department or physician's clinic with chest pain (also known as angina). The next stage, patient process flow, utilizes DMAIC results in the development of a simulated model that represents real-world variability in the diagnosis and treatment of a patient presenting with angina. The third and final stage is used to analyze the evidence-based output and quantify the factors that drive physician diagnosis accuracy and treatment, as well as review the potential for a broad national evidence-based database. Because of the collective expertise captured within the computer-oriented evidence-based model, the study has introduced an innovative approach to health care delivery by bringing expert-level care to any physician triaging a patient for chest pain anywhere in the world. Similar models can be created for other ailments as well, such as headache, gastrointestinal upset, and back pain. This updated way of looking at diagnosing patients stemming from an evidence-based best practice decision support model may improve workflow processes and cost savings across the health care continuum.

  2. An Event-based Distributed Diagnosis Framework using Structural Model Decomposition

    Data.gov (United States)

    National Aeronautics and Space Administration — Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis...

  3. Diagnosis-based and external cause-based criteria to identify adverse drug reactions in hospital ICD-coded data: application to an Australia population-based study

    Directory of Open Access Journals (Sweden)

    Wei Du

    2017-04-01

    Full Text Available Objectives: External cause International Classification of Diseases (ICD codes are commonly used to ascertain adverse drug reactions (ADRs related to hospitalisation. We quantified ascertainment of ADR-related hospitalisation using external cause codes and additional ICD-based hospital diagnosis codes. Methods: We reviewed the scientific literature to identify different ICD-based criteria for ADR-related hospitalisations, developed algorithms to capture ADRs based on candidate hospital ICD-10 diagnoses and external cause codes (Y40–Y59, and incorporated previously published causality ratings estimating the probability that a specific diagnosis was ADR related. We applied the algorithms to the NSW Admitted Patient Data Collection records of 45 and Up Study participants (2011–2013. Results: Of 493 442 hospitalisations among 267 153 study participants during 2011–2013, 18.8% (n = 92 953 had hospital diagnosis codes that were potentially ADR related; 1.1% (n = 5305 had high/very high–probability ADR-related diagnosis codes (causality ratings: A1 and A2; and 2.0% (n = 10 039 had ADR-related external cause codes. Overall, 2.2% (n = 11 082 of cases were classified as including an ADR-based hospitalisation on either external cause codes or high/very high–probability ADR-related diagnosis codes. Hence, adding high/very high–probability ADR-related hospitalisation codes to standard external cause codes alone (Y40–Y59 increased the number of hospitalisations classified as having an ADR-related diagnosis by 10.4%. Only 6.7% of cases with high-probability ADR-related mental symptoms were captured by external cause codes. Conclusion: Selective use of high-probability ADR-related hospital diagnosis codes in addition to external cause codes yielded a modest increase in hospitalised ADR incidence, which is of potential clinical significance. Clinically validated combinations of diagnosis codes could potentially further enhance capture.

  4. Considerations Regarding the Observance of the Presumption of Innocence in the Media

    Directory of Open Access Journals (Sweden)

    Sandra Gradinaru

    2009-06-01

    Full Text Available The presumption of innocence, the right of privacy, of an intimate and a family life, the freedomof speech, officials, the deontological code of the journalist Abstract: In the context of the Rule of Law, amodern governing guarantees to anyone the presumption of innocence until is delivered an unappealablecriminal decision. Nevertheless, in almost all the cases, the media, by virtue of freedom of speech, bringsprejudices to the dignity, the honor and image of the officials, investigated in criminal cases, having as aunique argument the fact that a media campaign, searching the sensational, does nothing else thanreproducing hostile manifestations - public servant - thus influencing the public opinion. They affect theprinciple of presumption of innocence, inducing unfortunate effects above the default of justice. Thus, themedia takes the information from prosecutors that operate within the courts, shading them by the depreciatingallegations addressed to the public persons as defendants in criminal cases, creating to the public opinion adistorted image of reality, before the justice has passed through a final criminal decision on guilt or theirinnocence.

  5. Innocent until primed: mock jurors' racially biased response to the presumption of innocence.

    Directory of Open Access Journals (Sweden)

    Danielle M Young

    Full Text Available BACKGROUND: Research has shown that crime concepts can activate attentional bias to Black faces. This study investigates the possibility that some legal concepts hold similar implicit racial cues. Presumption of innocence instructions, a core legal principle specifically designed to eliminate bias, may instead serve as an implicit racial cue resulting in attentional bias. METHODOLOGY/PRINCIPAL FINDINGS: The experiment was conducted in a courtroom with participants seated in the jury box. Participants first watched a video of a federal judge reading jury instructions that contained presumption of innocence instructions, or matched length alternative instructions. Immediately following this video a dot-probe task was administered to assess the priming effect of the jury instructions. Presumption of innocence instructions, but not the alternative instructions, led to significantly faster response times to Black faces when compared with White faces. CONCLUSIONS/SIGNIFICANCE: These findings suggest that the core principle designed to ensure fairness in the legal system actually primes attention for Black faces, indicating that this supposedly fundamental protection could trigger racial stereotypes.

  6. Fault diagnosis of main coolant pump in the nuclear power station based on the principal component analysis

    International Nuclear Information System (INIS)

    Feng Junting; Xu Mi; Wang Guizeng

    2003-01-01

    The fault diagnosis method based on principal component analysis is studied. The fault character direction storeroom of fifteen parameters abnormity is built in the simulation for the main coolant pump of nuclear power station. The measuring data are analyzed, and the results show that it is feasible for the fault diagnosis system of main coolant pump in the nuclear power station

  7. Metabolic profile at first-time schizophrenia diagnosis: a population-based cross-sectional study

    Directory of Open Access Journals (Sweden)

    Horsdal HT

    2017-02-01

    Full Text Available Henriette Thisted Horsdal,1,2 Michael Eriksen Benros,2,3 Ole Köhler-Forsberg,2–4 Jesper Krogh,3 Christiane Gasse1,2,5 1National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus BSS, Aarhus University, Aarhus, 2The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, 3Faculty of Health Sciences, Mental Health Centre Copenhagen, University of Copenhagen, Copenhagen, 4Psychosis Research Unit, Aarhus University Hospital, Risskov, 5Centre for Integrated Register-Based Research, Aarhus University, Aarhus, Denmark Objective: Schizophrenia and/or antipsychotic drug use are associated with metabolic abnormalities; however, knowledge regarding metabolic status and physician’s monitoring of metabolic status at first schizophrenia diagnosis is sparse. We assessed the prevalence of monitoring for metabolic blood abnormalities and characterized the metabolic profiles in people with a first-time schizophrenia diagnosis. Methods: This is a population-based cross-sectional study including all adults born in Denmark after January 1, 1955, with their first schizophrenia diagnosis between 2000 and 2012 in the Central Denmark Region. Information on metabolic parameters was obtained from a clinical laboratory information system. Associations were calculated using Wilcoxon rank-sum tests, chi-square tests, logistic regression, and Spearman’s correlation coefficients. Results: A total of 2,452 people with a first-time schizophrenia diagnosis were identified, of whom 1,040 (42.4% were monitored for metabolic abnormalities. Among those monitored, 58.4% had an abnormal lipid profile and 13.8% had an abnormal glucose profile. People who had previously filled prescription(s for antipsychotic drugs were more likely to present an abnormal lipid measure (65.7% vs 46.8%, P<0.001 and abnormal glucose profile (16.4% vs 10.1%, P=0.01. Conclusion: Metabolic abnormalities are common at first

  8. Radiologic diagnosis of bone tumours using Webonex, a web-based artificial intelligence program

    International Nuclear Information System (INIS)

    Rasuli, P.; Rasouli, F.; Rasouli, T.

    2001-01-01

    Knowledge-based system is a decision support system in which an expert's knowledge and reasoning can be applied to problems in bounded knowledge domains. These systems, using knowledge and inference techniques, mimic human reasoning to solve problems. Knowledge-based systems are said to be 'intelligent' because they possess massive stores of information and exhibit many attributes commonly associated with human experts performing difficult tasks and using specialized knowledge and sophisticated problem-solving strategies. Knowledge-based systems differ from conventional software such as database systems in that they are able to reason about data and draw conclusions employing heuristic rules. Heuristics embody human expertise in some knowledge domain and are sometimes characterized as the 'rules of thumb' that one acquires through practical experience and uses to solve everyday problems. Knowledge-based systems have been developed in a variety of fields, including medical disciplines. A decision support system has been assisting clinicians in areas such as infectious disease therapy for many years. For example, these systems can help radiologists formulate and evaluate diagnostic hypotheses by recalling associations between diseases and imaging findings. Although radiologic technology relies heavily on computers, it has been slow to develop a knowledge-based system to aid in diagnoses. These systems can be valuable interactive educational tools for medical students. In 1992, we developed a DOS-based Bonex, a menu-driven expert system for the differential diagnosis of bone tumours using PDC Prolog. It was a rule-based expert system that led the user through a menu of questions and generated a hard copy report and a list of diagnoses with an estimate of the likelihood of each. Bonex was presented at the 1992 Annual Meeting of the Radiological Society of North America (RSNA) in Chicago. We also developed an expert system for the differential diagnosis of brain lesions

  9. Radiologic diagnosis of bone tumours using Webonex, a web-based artificial intelligence program

    Energy Technology Data Exchange (ETDEWEB)

    Rasuli, P. [Univ. of Ottawa, Dept. of Radiology, Ottawa Hospital, Ottawa, Ontario (Canada); Rasouli, F. [Research, Development and Engineering Center, PMUSA, Richmond, VA (United States); Rasouli, T. [Johns Hopkins Univ., Dept. of Cognitive Science, Baltimore, Maryland (United States)

    2001-08-01

    Knowledge-based system is a decision support system in which an expert's knowledge and reasoning can be applied to problems in bounded knowledge domains. These systems, using knowledge and inference techniques, mimic human reasoning to solve problems. Knowledge-based systems are said to be 'intelligent' because they possess massive stores of information and exhibit many attributes commonly associated with human experts performing difficult tasks and using specialized knowledge and sophisticated problem-solving strategies. Knowledge-based systems differ from conventional software such as database systems in that they are able to reason about data and draw conclusions employing heuristic rules. Heuristics embody human expertise in some knowledge domain and are sometimes characterized as the 'rules of thumb' that one acquires through practical experience and uses to solve everyday problems. Knowledge-based systems have been developed in a variety of fields, including medical disciplines. A decision support system has been assisting clinicians in areas such as infectious disease therapy for many years. For example, these systems can help radiologists formulate and evaluate diagnostic hypotheses by recalling associations between diseases and imaging findings. Although radiologic technology relies heavily on computers, it has been slow to develop a knowledge-based system to aid in diagnoses. These systems can be valuable interactive educational tools for medical students. In 1992, we developed a DOS-based Bonex, a menu-driven expert system for the differential diagnosis of bone tumours using PDC Prolog. It was a rule-based expert system that led the user through a menu of questions and generated a hard copy report and a list of diagnoses with an estimate of the likelihood of each. Bonex was presented at the 1992 Annual Meeting of the Radiological Society of North America (RSNA) in Chicago. We also developed an expert system for the differential

  10. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    Science.gov (United States)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  11. Guidelines for the diagnosis and management of gastroesophageal reflux disease: an evidence-based consensus.

    Science.gov (United States)

    Moraes-Filho, Joaquim Prado P; Navarro-Rodriguez, Tomas; Barbuti, Ricardo; Eisig, Jaime; Chinzon, Decio; Bernardo, Wanderley

    2010-01-01

    Gastroesophageal reflux disease (GERD) is one of the most common disorders in medical practice. A number of guidelines and recommendations for the diagnosis and management of GERD have been published in different countries, but a Brazilian accepted directive by the standards of evidence-based medicine is still lacking. As such, the aim of the Brazilian GERD Consensus Group was to develop guidelines for the diagnosis and management of GERD, strictly using evidence-based medicine methodology that could be clinically used by primary care physicians and specialists and would encompass the needs of physicians, investigators, insurance and regulatory bodies. A total of 30 questions were proposed. Systematic literature reviews, which defined inclusion and/or exclusion criteria, were conducted to identify and grade the available evidence to support each statement. A total of 11,069 papers on GERD were selected, of which 6,474 addressed the diagnosis and 4,595, therapeutics. Regarding diagnosis, 51 met the requirements for the analysis of evidence-based medicine: 19 of them were classified as grade A and 32 as grade B. As for therapeutics, 158 met the evidence-based medicine criteria; 89 were classified as grade A and 69 as grade B. In the topic Diagnosis, answers supported by publications grade A and B were accepted. In the topic Treatment only publications grade A were accepted: answers supported by publications grade B were submitted to the voting by the Consensus Group. The present publication presents the most representative studies that responded to the proposed questions, followed by pertinent comments. Follow examples. In patients with atypical manifestations, the conventional esophageal pH-metry contributes little to the diagnosis of GERD. The sensitivity, however, increases with the use of double-channel pH-metry. In patients with atypical manifestations, the impedance-pH-metry substantially contributes to the diagnosis of GERD. The examination, however, is costly

  12. An Approach of Diagnosis Based On The Hidden Markov Chains Model

    Directory of Open Access Journals (Sweden)

    Karim Bouamrane

    2008-07-01

    Full Text Available Diagnosis is a key element in industrial system maintenance process performance. A diagnosis tool is proposed allowing the maintenance operators capitalizing on the knowledge of their trade and subdividing it for better performance improvement and intervention effectiveness within the maintenance process service. The Tool is based on the Markov Chain Model and more precisely the Hidden Markov Chains (HMC which has the system failures determination advantage, taking into account the causal relations, stochastic context modeling of their dynamics and providing a relevant diagnosis help by their ability of dubious information use. Since the FMEA method is a well adapted artificial intelligence field, the modeling with Markov Chains is carried out with its assistance. Recently, a dynamic programming recursive algorithm, called 'Viterbi Algorithm', is being used in the Hidden Markov Chains field. This algorithm provides as input to the HMC a set of system observed effects and generates at exit the various causes having caused the loss from one or several system functions.

  13. Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2018-01-01

    Full Text Available The battery is a key component and the major fault source in electric vehicles (EVs. Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.

  14. Novel fiber optic-based needle redox imager for cancer diagnosis

    Science.gov (United States)

    Kanniyappan, Udayakumar; Xu, He N.; Tang, Qinggong; Gaitan, Brandon; Liu, Yi; Li, Lin Z.; Chen, Yu

    2018-02-01

    Despite various technological advancements in cancer diagnosis, the mortality rates were not decreased significantly. We aim to develop a novel optical imaging tool to assist cancer diagnosis effectively. Fluorescence spectroscopy/imaging is a fast, rapid, and minimally invasive technique which has been successfully applied to diagnosing cancerous cells/tissues. Recently, the ratiometric imaging of intrinsic fluorescence of reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD), as pioneered by Britton Chance and the co-workers in 1950-70's, has gained much attention to quantify the physiological parameters of living cells/tissues. The redox ratio, i.e., FAD/(FAD+NADH) or FAD/NADH, has been shown to be sensitive to various metabolic changes in in vivo and in vitro cells/tissues. Optical redox imaging has also been investigated for providing potential imaging biomarkers for cancer transformation, aggressiveness, and treatment response. Towards this goal, we have designed and developed a novel fiberoptic-based needle redox imager (NRI) that can fit into an 11G clinical coaxial biopsy needle for real time imaging during clinical cancer surgery. In the present study, the device is calibrated with tissue mimicking phantoms of FAD and NADH along with various technical parameters such as sensitivity, dynamic range, linearity, and spatial resolution of the system. We also conducted preliminary imaging of tissues ex vivo for validation. We plan to test the NRI on clinical breast cancer patients. Once validated this device may provide an effective tool for clinical cancer diagnosis.

  15. Star polymer-based unimolecular micelles and their application in bio-imaging and diagnosis.

    Science.gov (United States)

    Jin, Xin; Sun, Pei; Tong, Gangsheng; Zhu, Xinyuan

    2018-02-03

    As a novel kind of polymer with covalently linked core-shell structure, star polymers behave in nanostructure in aqueous medium at all concentration range, as unimolecular micelles at high dilution condition and multi-micelle aggregates in other situations. The unique morphologies endow star polymers with excellent stability and functions, making them a promising platform for bio-application. A variety of functions including imaging and therapeutics can be achieved through rational structure design of star polymers, and the existence of plentiful end-groups on shell offers the opportunity for further modification. In the last decades, star polymers have become an attracting platform on fabrication of novel nano-systems for bio-imaging and diagnosis. Focusing on the specific topology and physicochemical properties of star polymers, we have reviewed recent development of star polymer-based unimolecular micelles and their bio-application in imaging and diagnosis. The main content of this review summarizes the synthesis of integrated architecture of star polymers and their self-assembly behavior in aqueous medium, focusing especially on the recent advances on their bio-imaging application and diagnosis use. Finally, we conclude with remarks and give some outlooks for further exploration in this field. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

    Directory of Open Access Journals (Sweden)

    D. Viveros-Melo

    2017-02-01

    Full Text Available Case-based reasoning (CBR is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CBR

  17. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    Science.gov (United States)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-12-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  18. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    International Nuclear Information System (INIS)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-01-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis

  19. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    Energy Technology Data Exchange (ETDEWEB)

    Stoitsis, John [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)]. E-mail: stoitsis@biosim.ntua.gr; Valavanis, Ioannis [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Mougiakakou, Stavroula G. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Golemati, Spyretta [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Nikita, Alexandra [University of Athens, Medical School 152 28 Athens (Greece); Nikita, Konstantina S. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)

    2006-12-20

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  20. Validity criteria for the diagnosis of fatty liver by M probe-based controlled attenuation parameter.

    Science.gov (United States)

    Wong, Vincent Wai-Sun; Petta, Salvatore; Hiriart, Jean-Baptiste; Cammà, Calogero; Wong, Grace Lai-Hung; Marra, Fabio; Vergniol, Julien; Chan, Anthony Wing-Hung; Tuttolomondo, Antonino; Merrouche, Wassil; Chan, Henry Lik-Yuen; Le Bail, Brigitte; Arena, Umberto; Craxì, Antonio; de Lédinghen, Victor

    2017-09-01

    Controlled attenuation parameter (CAP) can be performed together with liver stiffness measurement (LSM) by transient elastography (TE) and is often used to diagnose fatty liver. We aimed to define the validity criteria of CAP. CAP was measured by the M probe prior to liver biopsy in 754 consecutive patients with different liver diseases at three centers in Europe and Hong Kong (derivation cohort, n=340; validation cohort, n=414; 101 chronic hepatitis B, 154 chronic hepatitis C, 349 non-alcoholic fatty liver disease, 37 autoimmune hepatitis, 49 cholestatic liver disease, 64 others; 277 F3-4; age 52±14; body mass index 27.2±5.3kg/m 2 ). The primary outcome was the diagnosis of fatty liver, defined as steatosis involving ≥5% of hepatocytes. The area under the receiver-operating characteristics curve (AUROC) for CAP diagnosis of fatty liver was 0.85 (95% CI 0.82-0.88). The interquartile range (IQR) of CAP had a negative correlation with CAP (r=-0.32, psteatosis was lower among patients with body mass index ≥30kg/m 2 and F3-4 fibrosis. The validity of CAP for the diagnosis of fatty liver is lower if the IQR of CAP is ≥40dB/m. Lay summary: Controlled attenuation parameter (CAP) is measured by transient elastography (TE) for the detection of fatty liver. In this large study, using liver biopsy as a reference, we show that the variability of CAP measurements based on its interquartile range can reflect the accuracy of fatty liver diagnosis. In contrast, other clinical factors such as adiposity and liver enzyme levels do not affect the performance of CAP. Copyright © 2017 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

  1. Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

    Science.gov (United States)

    Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R V Paul; Ostmo, Susan; Chiang, Michael F

    2015-11-01

    We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system. Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

  2. Weight-based nutritional diagnosis of Mexican children and adolescents with neuromotor disabilities.

    Science.gov (United States)

    Vega-Sanchez, Rodrigo; de la Luz Gomez-Aguilar, Maria; Haua, Karime; Rozada, Guadalupe

    2012-07-04

    Nutrition related problems are increasing worldwide but they have scarcely been evaluated in people with neuromotor disabilities, particularly in developing countries. In this study our aim was to describe the weight-based nutritional diagnoses of children and adolescents with neuromotor disabilities who attended a private rehabilitation center in Mexico City. Data from the first visit's clinical records of 410 patients who attended the Nutrition department at the Teleton Center for Children Rehabilitation, between 1999 and 2008, were analyzed. Sex, age, weight and height, length or segmental length data were collected and used to obtain the nutritional diagnosis based on international growth charts, as well as disability-specific charts. Weight for height was considered the main indicator. Cerebral palsy was the most frequent diagnosis, followed by spina bifida, muscular dystrophy, and Down's syndrome. Children with cerebral palsy showed a higher risk of presenting low weight/undernutrition (LW/UN) than children with other disabilities, which was three times higher in females. In contrast, children with spina bifida, particularly males, were more likely to be overweight/obese (OW/OB), especially after the age of 6 and even more after 11. Patients with muscular dystrophy showed a significantly lower risk of LW/UN than patients with other disabilities. In patients with Down's syndrome neither LW/UN nor OW/OB were different between age and sex. This is the first study that provides evidence of the nutritional situation of children and adolescents with neuromotor disabilities in Mexico, based on their weight status. Low weight and obesity affect a large number of these patients due to their disability, age and sex. Early nutritional diagnosis must be considered an essential component in the treatment of these patients to prevent obesity and malnutrition, and improve their quality of life.

  3. Narratives of Sexuality: Presumptions for a Queer Poetics

    Directory of Open Access Journals (Sweden)

    Anselmo Peres Alós

    2010-09-01

    Full Text Available The articulation of a queer epistemology allows us to think about textuality as a place of dramatization of a politic fiction that questions the heteronormative patterns of sex and gender, and proposes a strategy of resistance based both on bodies and pleasures and on politics of representation and reinvention of masculinities and femininities. Through the principles of narratology, it is studied in which way (or ways the narrative is configured as a space of negotiation, from a queer perspective, of nationality, sexuality, and gender in the enunciation. In this sense, literature rewrites both the sexual body, seen as the place of individual subjectivity, and the social/ national body, understood as a fiction that balances body and sexual sociabilities. At last, the contradictions and impasses that emerge from literature are analyzed, particularly in which concerns questions of race, class, and gender, as well as the potentialities and problematic points of a queer poetics as a place of cultural intervention, intending the construction and the comprehension of this queer poetics, where new arranges of social legibility are projected in a performative way.

  4. Fault diagnosis in spur gears based on genetic algorithm and random forest

    Science.gov (United States)

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  5. EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

    Directory of Open Access Journals (Sweden)

    Ridha Djemal

    2017-01-01

    Full Text Available Autism spectrum disorder (ASD is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD of autism ‎based on electroencephalography (EEG signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT, entropy (En, and artificial neural network (ANN. DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

  6. An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

    Science.gov (United States)

    Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

    2018-04-11

    In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

  7. Classification and Clinical Diagnosis of Fibromyalgia Syndrome: Recommendations of Recent Evidence-Based Interdisciplinary Guidelines

    Directory of Open Access Journals (Sweden)

    Mary-Ann Fitzcharles

    2013-01-01

    Full Text Available Objectives. Fibromyalgia syndrome (FMS, characterized by subjective complaints without physical or biomarker abnormality, courts controversy. Recommendations in recent guidelines addressing classification and diagnosis were examined for consistencies or differences. Methods. Systematic searches from January 2008 to February 2013 of the US-American National Guideline Clearing House, the Scottish Intercollegiate Guidelines Network, Guidelines International Network, and Medline for evidence-based guidelines for the management of FMS were conducted. Results. Three evidence-based interdisciplinary guidelines, independently developed in Canada, Germany, and Israel, recommended that FMS can be clinically diagnosed by a typical cluster of symptoms following a defined evaluation including history, physical examination, and selected laboratory tests, to exclude another somatic disease. Specialist referral is only recommended when some other physical or mental illness is reasonably suspected. The diagnosis can be based on the (modified preliminary American College of Rheumatology (ACR 2010 diagnostic criteria. Discussion. Guidelines from three continents showed remarkable consistency regarding the clinical concept of FMS, acknowledging that FMS is neither a distinct rheumatic nor mental disorder, but rather a cluster of symptoms, not explained by another somatic disease. While FMS remains an integral part of rheumatology, it is not an exclusive rheumatic condition and spans a broad range of medical disciplines.

  8. Model-Based Diagnosis and Prognosis of a Water Recycling System

    Science.gov (United States)

    Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

    2013-01-01

    A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

  9. Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

    Directory of Open Access Journals (Sweden)

    Yu Ding

    2018-01-01

    Full Text Available Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.

  10. Serological diagnosis of brucellosis.

    Science.gov (United States)

    Nielsen, K; Yu, W L

    2010-01-01

    To present a review and to describe the most widely used laboratory tests for serology diagnosis of brucellosis along with their pros and cons. Review the recent literature on brucellosis serology diagnostic tests. The choice of the testing strategy depends on the prevailing brucellosis epidemiological situation and the goal of testing. The 'gold standard' for the diagnosis of brucellosis is isolation and identification of the causative bacterium, a member of Brucella sp. Isolation of Brucella sp. requires high security laboratory facilities (biological containment level 3), highly skilled personnel, an extended turnaround time for results and it is considered a hazardous procedure. Hence brucellosis is generally diagnosed by detection of an elevated level of antibody in serum or other body fluid. This is a presumptive diagnosis as other microorganisms and perhaps environmental factors can also cause increased antibody levels. A large number of serological tests for brucellosis have been devised over the 100+ years since its initial isolation, starting with a simple agglutination test and progressing to sophisticated primary binding assays available today. However, no test devised to date is 100% accurate so generally serological diagnosis consists of testing sera by several tests, usually a screening test of high sensitivity, followed by a confirmatory test of high specificity.

  11. Clinical usefulness of normal data bases comparisons for the SPECT diagnosis of Alzheimer's disease

    International Nuclear Information System (INIS)

    Darcourt, J.; Koulibaly, P.M.; Migneco, O.; Dygai, I.; Robert, P.H.; Nobili, F.; Ebmeir, K.

    2002-01-01

    Aim. The possible added value of voxel by voxel comparisons to normal data bases has not been evaluated for the diagnosis of Alzheimer's disease (AD). We conducted a prospective comparison of the diagnostic performances of 2 software packages: Statistical Parametric Mapping (SPM) (Friston et al.) and NeuroGam (NGam) (Segami Corporation). Materials and methods. A total of 152 subjects (age ≥ 50 years) were included: 93 AD, 28 depressed patients and 31 normal controls (NC). They were studied in 4 centers as part of a European project 'SPECT in dementia' BMH4-98-3130. NC were used to build the normal data bases and the total population was submitted to the readers for the diagnosis of AD. AD final diagnosis was based on NINCDS/ADRDA criteria for probable AD and DSM-IV criteria for dementia of AD type. SPECT scans were obtained in each center with dedicated cameras 30 to 90 min after i.v. injection of 250 to 925 MBq of 99mTc-HMPAO. All data were reconstructed on the same workstation by filtered backprojection with attenuation correction. The 4.7 mm thick cuts (CUTS) were displayed in the transverse, sagittal and coronal planes with the same color scale. They also were submitted to the 2 packages tested. For SPM, we used SPM'96 for Windows'95. For each individual scan we computed the corresponding z-map by comparison to the NC data base. We used p<0.01 to threshold the t-maps and a p corrected value <0.01 on intensity for cluster selection. For NGam, the same NC were used to build the normal data base. Each individual scan was then compared to this base and the results consisted in a 3D parametric image of voxel by voxel standard deviations form the normal mean value. 4 expert readers (more than 3 years experience; more than 5 SPECT per week) were asked to class the scans as AD or not with a 4 degree of confidence. They reviewed the CUTS alone, CUTS+SPM and CUTS+NGam. ROC analysis was performed and the areas under curves (AUC) statistically compared. Results. Average

  12. Usefulness of additional fetal magnetic resonance imaging in the prenatal diagnosis of congenital abnormalities.

    Science.gov (United States)

    We, Ji Sun; Young, Lee; Park, In Yang; Shin, Jong Chul; Im, Soo Ah

    2012-12-01

    Our aim was to compare the value of fetal magnetic resonance imaging (MRI) with detailed ultrasound in the prenatal diagnosis of congenital abnormalities. This retrospective study reviewed the medical records of pregnant women and their neonates who, after ultrasound, were suspected to have congenital abnormalities. They then underwent a detailed ultrasound examination and a fetal MRI in our institutions. Fetal MRI was performed in 81 cases. Each prenatal presumptive diagnosis, based on detailed ultrasound examination and fetal MRI, was compared with the postnatal confirmed diagnosis. In 58 cases, the data collected were confirmed by the postnatal diagnosis. Supplemental information from fetal MRI was useful in 17 of the 22 cases involving the central nervous system (CNS), two of two cases involving the thorax, nine of nine cases involving the genitourinary system, two of eight cases involving the gastrointestinal system, and ten of ten cases involving complex malformations. Fetal MRI did not provide significantly useful information or facilitate a more accurate diagnosis except for CNS abnormalities. Fetal MRI was not superior to an ultrasound examination in the prenatal detection of congenital abnormalities. A detailed ultrasound examination performed by experienced obstetricians had satisfactory accuracy in the diagnosis of fetal abnormalities compared with fetal MRI. Fetal MRI might be useful in appropriate cases in Korea. Greater effort is required to increase the ultrasound knowledge and skill of competent obstetricians.

  13. CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency.

    Science.gov (United States)

    Bhavani, Selvaraj Rani; Senthilkumar, Jagatheesan; Chilambuchelvan, Arul Gnanaprakasam; Manjula, Dhanabalachandran; Krishnamoorthy, Ramasamy; Kannan, Arputharaj

    2015-03-27

    The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called "CIMIDx", based on representative association rules that support the diagnosis of medical images (mammograms). The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype's classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user's perspective. We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information from 150 breast cancer survivors from hospitals

  14. A GC/MS-based metabolomic approach for reliable diagnosis of phenylketonuria.

    Science.gov (United States)

    Xiong, Xiyue; Sheng, Xiaoqi; Liu, Dan; Zeng, Ting; Peng, Ying; Wang, Yichao

    2015-11-01

    ), which showed that phenylacetic acid may be used as a reliable discriminator for the diagnosis of PKU. The low false positive rate (1-specificity, 0.064) can be eliminated or at least greatly reduced by simultaneously referring to other markers, especially phenylpyruvic acid, a unique marker in PKU. Additionally, this standard was obtained with high sensitivity and specificity in a less invasive manner for diagnosing PKU compared with the Phe/Tyr ratio. Therefore, we conclude that urinary metabolomic information based on the improved oximation-silylation method together with GC/MS may be reliable for the diagnosis and differential diagnosis of PKU.

  15. Parasite-based malaria diagnosis: are health systems in Uganda equipped enough to implement the policy?

    Science.gov (United States)

    Kyabayinze, Daniel J; Achan, Jane; Nakanjako, Damalie; Mpeka, Betty; Mawejje, Henry; Mugizi, Rukaaka; Kalyango, Joan N; D'Alessandro, Umberto; Talisuna, Ambrose; Jean-Pierre, Van geertruyden

    2012-08-24

    Malaria case management is a key strategy for malaria control. Effective coverage of parasite-based malaria diagnosis (PMD) remains limited in malaria endemic countries. This study assessed the health system's capacity to absorb PMD at primary health care facilities in Uganda. In a cross sectional survey, using multi-stage cluster sampling, lower level health facilities (LLHF) in 11 districts in Uganda were assessed for 1) tools, 2) skills, 3) staff and infrastructure, and 4) structures, systems and roles necessary for the implementing of PMD. Tools for PMD (microscopy and/or RDTs) were available at 30 (24%) of the 125 LLHF. All LLHF had patient registers and 15% had functional in-patient facilities. Three months' long stock-out periods were reported for oral and parenteral quinine at 39% and 47% of LLHF respectively. Out of 131 health workers interviewed, 86 (66%) were nursing assistants; 56 (43%) had received on-job training on malaria case management and 47 (36%) had adequate knowledge in malaria case management. Overall, only 18% (131/730) Ministry of Health approved staff positions were filled by qualified personnel and 12% were recruited or transferred within six months preceding the survey. Of 186 patients that received referrals from LLHF, 130(70%) had received pre-referral anti-malarial drugs, none received pre-referral rectal artesunate and 35% had been referred due to poor response to antimalarial drugs. Primary health care facilities had inadequate human and infrastructural capacity to effectively implement universal parasite-based malaria diagnosis. The priority capacity building needs identified were: 1) recruitment and retention of qualified staff, 2) comprehensive training of health workers in fever management, 3) malaria diagnosis quality control systems and 4) strengthening of supply chain, stock management and referral systems.

  16. An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique

    International Nuclear Information System (INIS)

    Taheri-Garavand, Amin; Ahmadi, Hojjat; Omid, Mahmoud; Mohtasebi, Seyed Saeid; Mollazade, Kaveh; Russell Smith, Alan John; Carlomagno, Giovanni Maria

    2015-01-01

    This research presents a new intelligent fault diagnosis and condition monitoring system for classification of different conditions of cooling radiator using infrared thermal images. The system was adopted to classify six types of cooling radiator faults; radiator tubes blockage, radiator fins blockage, loose connection between fins and tubes, radiator door failure, coolant leakage, and normal conditions. The proposed system consists of several distinct procedures including thermal image acquisition, image pre-processing, image processing, two-dimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection using a genetic algorithm (GA), and finally classification by artificial neural networks (ANNs). The 2D-DWT is implemented to decompose the thermal images. Subsequently, statistical texture features are extracted from the original images and are decomposed into thermal images. The significant selected features are used to enhance the performance of the designed ANN classifier for the 6 types of cooling radiator conditions (output layer) in the next stage. For the tested system, the input layer consisted of 16 neurons based on the feature selection operation. The best performance of ANN was obtained with a 16-6-6 topology. The classification results demonstrated that this system can be employed satisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator. - Highlights: • Intelligent fault diagnosis of cooling radiator using thermal image processing. • Thermal image processing in a multiscale representation structure by 2D-DWT. • Selection features based on a hybrid system that uses both GA and ANN. • Application of ANN as classifier. • Classification accuracy of fault detection up to 93.83%

  17. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs

    International Nuclear Information System (INIS)

    Lei, Yaguo; Zuo, Ming J

    2009-01-01

    A Hilbert–Huang transform (HHT) is a time–frequency technique and has been widely applied to analyzing vibration signals in the field of fault diagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in fault diagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD

  18. Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

    Science.gov (United States)

    Peng, Chaoyong; Gao, Xiaorong; Peng, Jianping; Wang, Ai

    2018-04-01

    As one of the key components of railway vehicles, the operation condition of the axle box bearing has a significant effect on traffic safety. The acoustic diagnosis is more suitable than vibration diagnosis for trackside monitoring. The acoustic signal generated by the train axle box bearing is an amplitude modulation and frequency modulation signal with complex train running noise. Although empirical mode decomposition (EMD) and some improved time-frequency algorithms have proved to be useful in bearing vibration signal processing, it is hard to extract the bearing fault signal from serious trackside acoustic background noises by using those algorithms. Therefore, a kurtosis-optimization-based wavelet packet (KWP) denoising algorithm is proposed, as the kurtosis is the key indicator of bearing fault signal in time domain. Firstly, the geometry based Doppler correction is applied to signals of each sensor, and with the signal superposition of multiple sensors, random noises and impulse noises, which are the interference of the kurtosis indicator, are suppressed. Then, the KWP is conducted. At last, the EMD and Hilbert transform is applied to extract the fault feature. Experiment results indicate that the proposed method consisting of KWP and EMD is superior to the EMD.

  19. Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm

    Science.gov (United States)

    Luo, Ai-Jing; Chang, Wei-Fu; Xin, Zi-Rui; Ling, Hao; Li, Jun-Jie; Dai, Ping-Ping; Deng, Xuan-Tong; Zhang, Lei; Li, Shao-Gang

    2018-01-01

    AIM To figure out the contributed factors of the hospitalization expenses of senile cataract patients (HECP) and build up an area-specified senile cataract diagnosis related group (DRG) of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund. METHODS The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector (E-CHAID) model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc. RESULTS The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases. CONCLUSION The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund. PMID:29487824

  20. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

    Science.gov (United States)

    Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

    2018-05-01

    The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

  1. Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

    Directory of Open Access Journals (Sweden)

    Liang Hua

    2015-01-01

    Full Text Available Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

  2. Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

    Directory of Open Access Journals (Sweden)

    Yuning Qian

    2016-01-01

    Full Text Available This paper presents a single sensor based blind source separation approach, namely, the wavelet-assisted stationary subspace analysis (WSSA, for gearbox fault diagnosis in a wind turbine. Continuous wavelet transform (CWT is used as a preprocessing tool to decompose a single sensor measurement data into a set of wavelet coefficients to meet the multidimensional requirement of the stationary subspace analysis (SSA. The SSA is a blind source separation technique that can separate the multidimensional signals into stationary and nonstationary source components without the need for independency and prior information of the source signals. After that, the separated nonstationary source component with the maximum kurtosis value is analyzed by the enveloping spectral analysis to identify potential fault-related characteristic frequencies. Case studies performed on a wind turbine gearbox test system verify the effectiveness of the WSSA approach and indicate that it outperforms independent component analysis (ICA and empirical mode decomposition (EMD, as well as the spectral-kurtosis-based enveloping, for wind turbine gearbox fault diagnosis.

  3. Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm.

    Science.gov (United States)

    Luo, Ai-Jing; Chang, Wei-Fu; Xin, Zi-Rui; Ling, Hao; Li, Jun-Jie; Dai, Ping-Ping; Deng, Xuan-Tong; Zhang, Lei; Li, Shao-Gang

    2018-01-01

    To figure out the contributed factors of the hospitalization expenses of senile cataract patients (HECP) and build up an area-specified senile cataract diagnosis related group (DRG) of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund. The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector (E-CHAID) model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc. The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases. The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.

  4. OPTICAL AND DIELECTRIC SENSORS BASED ON ANTIMICROBIAL PEPTIDES FOR MICROORGANISMS DIAGNOSIS

    Directory of Open Access Journals (Sweden)

    Rafael Ramos Silva

    2014-08-01

    Full Text Available Antimicrobial peptides (AMPs are natural compounds isolated from a wide variety of organisms that include microorganisms, insects, amphibians, plants and humans. These biomolecules are considered as part of the innate immune system and are known as natural antibiotics, presenting a broad spectrum of activities against bacteria, fungi and/or viruses. Technological innovations have enabled AMPs to be utilized for the development of novel biodetection devices. Advances in nanotechnology, such as the synthesis of nanocomposites, nanoparticles, and nanotubes have permitted the development of nanostructured platforms with biocompatibility and greater surface areas for the immobilization of biocomponents, arising as additional tools for obtaining more efficient biosensors. Diverse AMPs have been used as biological recognition elements for obtaining biosensors with more specificity and lower detection limits, whose analytical response can be evaluated through electrochemical impedance and fluorescence spectroscopies. AMP-based biosensors have shown potential for applications such as supplementary tools for conventional diagnosis methods of microorganisms. In this review, conventional methods for microorganism diagnosis as well new strategies using AMPs for the development of impedimetric and fluorescent biosensors are highlighted. AMP-based biosensors show promise as methods for diagnosing infections and bacterial contaminations as well as applications in quality control for clinical analyses and microbiological laboratories.

  5. A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

    Science.gov (United States)

    Azami, Hamed; Escudero, Javier

    2015-08-01

    Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

  6. A new CFD based non-invasive method for functional diagnosis of coronary stenosis.

    Science.gov (United States)

    Xie, Xinzhou; Zheng, Minwen; Wen, Didi; Li, Yabing; Xie, Songyun

    2018-03-22

    Accurate functional diagnosis of coronary stenosis is vital for decision making in coronary revascularization. With recent advances in computational fluid dynamics (CFD), fractional flow reserve (FFR) can be derived non-invasively from coronary computed tomography angiography images (FFR CT ) for functional measurement of stenosis. However, the accuracy of FFR CT is limited due to the approximate modeling approach of maximal hyperemia conditions. To overcome this problem, a new CFD based non-invasive method is proposed. Instead of modeling maximal hyperemia condition, a series of boundary conditions are specified and those simulated results are combined to provide a pressure-flow curve for a stenosis. Then, functional diagnosis of stenosis is assessed based on parameters derived from the obtained pressure-flow curve. The proposed method is applied to both idealized and patient-specific models, and validated with invasive FFR in six patients. Results show that additional hemodynamic information about the flow resistances of a stenosis is provided, which cannot be directly obtained from anatomy information. Parameters derived from the simulated pressure-flow curve show a linear and significant correlations with invasive FFR (r > 0.95, P < 0.05). The proposed method can assess flow resistances by the pressure-flow curve derived parameters without modeling of maximal hyperemia condition, which is a new promising approach for non-invasive functional assessment of coronary stenosis.

  7. Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis

    Science.gov (United States)

    Zheng, Jinde; Pan, Haiyang; Yang, Shubao; Cheng, Junsheng

    2018-01-01

    Multiscale permutation entropy (MPE) is a recently proposed nonlinear dynamic method for measuring the randomness and detecting the nonlinear dynamic change of time series and can be used effectively to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing. To solve the drawback of coarse graining process in MPE, an improved MPE method called generalized composite multiscale permutation entropy (GCMPE) was proposed in this paper. Also the influence of parameters on GCMPE and its comparison with the MPE are studied by analyzing simulation data. GCMPE was applied to the fault feature extraction from vibration signal of rolling bearing and then based on the GCMPE, Laplacian score for feature selection and the Particle swarm optimization based support vector machine, a new fault diagnosis method for rolling bearing was put forward in this paper. Finally, the proposed method was applied to analyze the experimental data of rolling bearing. The analysis results show that the proposed method can effectively realize the fault diagnosis of rolling bearing and has a higher fault recognition rate than the existing methods.

  8. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer’s disease

    International Nuclear Information System (INIS)

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-01-01

    Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  9. ALA-based fluorescent diagnosis of malignant oral lesions in the presence of bacterial porphyrin formation

    Science.gov (United States)

    Schleier, P.; Berndt, A.; Zinner, K.; Zenk, W.; Dietel, W.; Pfister, W.

    2006-02-01

    The aminolevulinic acid (5-ALA) -based fluorescence diagnosis has been found to be promising for an early detection and demarcation of superficial oral squamous cell carcinomas (OSCC). This method has previously demonstrated high sensitivity, however this clinical trial showed a specificity of approximately 62 %. This specificity was mainly restricted by tumor detection in the oral cavity in the presence of bacteria. After topical ALA application in the mouth of patients with previously diagnosed OSSC, red fluorescent areas were observed which did not correlate to confirm histological findings. Swabs and plaque samples were taken from 44 patients and cultivated microbiologically. Fluorescence was investigated (OMA-system) from 32 different bacteria strains found naturally in the oral cavity. After ALA incubation, 30 of 32 strains were found to synthesize fluorescent porphyrins, mainly Protoporphyrin IX. Also multiple fluorescent spectra were obtained having peak wavelengths of 636 nm and around 618 nm - 620 nm indicating synthesis of different porphyrins, such as the lipophylic Protoporphyrin IX (PpIX) and hydrophylic porphyrins (water soluble porphyrins, wsp). Of the 32 fluorescent bacterial strains, 18 produced wsp, often in combination with PpIX, and 5 produced solely wsp. These results clarify that ALA-based fluorescence diagnosis without consideration or suppression of bacteria fluorescence may lead to false-positive findings. It is necessary to suppress bacteria fluorescence with suitable antiseptics before starting the procedure. In this study, when specific antiseptic pre-treatment was performed bacterial associated fluorescence was significantly reduced.

  10. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer’s disease

    Energy Technology Data Exchange (ETDEWEB)

    Bhateja, Vikrant, E-mail: bhateja.vikrant@gmail.com, E-mail: nhuongld@hus.edu.vn; Moin, Aisha; Srivastava, Anuja [Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh 226028 (India); Bao, Le Nguyen [Duytan University, Danang 550000 (Viet Nam); Lay-Ekuakille, Aimé [Department of Innovation Engineering, University of Salento, Lecce 73100 (Italy); Le, Dac-Nhuong, E-mail: bhateja.vikrant@gmail.com, E-mail: nhuongld@hus.edu.vn [Duytan University, Danang 550000 (Viet Nam); Haiphong University, Haiphong 180000 (Viet Nam)

    2016-07-15

    Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  11. Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm

    Directory of Open Access Journals (Sweden)

    Ai-Jing Luo

    2018-02-01

    Full Text Available AIM: To figure out the contributed factors of the hospitalization expenses of senile cataract patients (HECP and build up an area-specified senile cataract diagnosis related group (DRG of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund. METHODS: The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector (E-CHAID model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc. RESULTS: The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases. CONCLUSION: The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.

  12. Rapid diagnosis of sepsis with TaqMan-Based multiplex real-time PCR.

    Science.gov (United States)

    Liu, Chang-Feng; Shi, Xin-Ping; Chen, Yun; Jin, Ye; Zhang, Bing

    2018-02-01

    The survival rate of septic patients mainly depends on a rapid and reliable diagnosis. A rapid, broad range, specific and sensitive quantitative diagnostic test is the urgent need. Thus, we developed a TaqMan-Based Multiplex real-time PCR assays to identify bloodstream pathogens within a few hours. Primers and TaqMan probes were designed to be complementary to conserved regions in the 16S rDNA gene of different kinds of bacteria. To evaluate accurately, sensitively, and specifically, the known bacteria samples (Standard strains, whole blood samples) are determined by TaqMan-Based Multiplex real-time PCR. In addition, 30 blood samples taken from patients with clinical symptoms of sepsis were tested by TaqMan-Based Multiplex real-time PCR and blood culture. The mean frequency of positive for Multiplex real-time PCR was 96% at a concentration of 100 CFU/mL, and it was 100% at a concentration greater than 1000 CFU/mL. All the known blood samples and Standard strains were detected positively by TaqMan-Based Multiplex PCR, no PCR products were detected when DNAs from other bacterium were used in the multiplex assay. Among the 30 patients with clinical symptoms of sepsis, 18 patients were confirmed positive by Multiplex real-time PCR and seven patients were confirmed positive by blood culture. TaqMan-Based Multiplex real-time PCR assay with highly sensitivity, specificity and broad detection range, is a rapid and accurate method in the detection of bacterial pathogens of sepsis and should have a promising usage in the diagnosis of sepsis. © 2017 Wiley Periodicals, Inc.

  13. Review of Diagnosis-Related Group-Based Financing of Hospital Care

    Directory of Open Access Journals (Sweden)

    Natasa Mihailovic

    2016-05-01

    Full Text Available Since the 1990s, diagnosis-related group (DRG-based payment systems were gradually introduced in many countries. The main design characteristics of a DRG-based payment system are an exhaustive patient case classification system (ie, the system of diagnosis-related groupings and the payment formula, which is based on the base rate multiplied by a relative cost weight specific for each DRG. Cases within the same DRG code group are expected to undergo similar clinical evolution. Consecutively, they should incur the costs of diagnostics and treatment within a predefined scale. Such predictability was proven in a number of cost-of-illness studies conducted on major prosperity diseases alongside clinical trials on efficiency. This was the case with risky pregnancies, chronic obstructive pulmonary disease, diabetes, depression, alcohol addiction, hepatitis, and cancer. This article presents experience of introduced DRG-based payments in countries of western and eastern Europe, Scandinavia, United States, Canada, and Australia. This article presents the results of few selected reviews and systematic reviews of the following evidence: published reports on health system reforms by World Health Organization, World Bank, Organization for Economic Co-operation and Development, Canadian Institute for Health Information, Canadian Health Services Research Foundation, and Centre for Health Economics University of York. Diverse payment systems have different strengths and weaknesses in relation to the various objectives. The advantages of the DRG payment system are reflected in the increased efficiency and transparency and reduced average length of stay. The disadvantage of DRG is creating financial incentives toward earlier hospital discharges. Occasionally, such polices are not in full accordance with the clinical benefit priorities.

  14. An internet-based bioinformatics toolkit for plant biosecurity diagnosis and surveillance of viruses and viroids.

    Science.gov (United States)

    Barrero, Roberto A; Napier, Kathryn R; Cunnington, James; Liefting, Lia; Keenan, Sandi; Frampton, Rebekah A; Szabo, Tamas; Bulman, Simon; Hunter, Adam; Ward, Lisa; Whattam, Mark; Bellgard, Matthew I

    2017-01-11

    Detection and preventing entry of exotic viruses and viroids at the border is critical for protecting plant industries trade worldwide. Existing post entry quarantine screening protocols rely on time-consuming biological indicators and/or molecular assays that require knowledge of infecting viral pathogens. Plants have developed the ability to recognise and respond to viral infections through Dicer-like enzymes that cleave viral sequences into specific small RNA products. Many studies reported the use of a broad range of small RNAs encompassing the product sizes of several Dicer enzymes involved in distinct biological pathways. Here we optimise the assembly of viral sequences by using specific small RNA subsets. We sequenced the small RNA fractions of 21 plants held at quarantine glasshouse facilities in Australia and New Zealand. Benchmarking of several de novo assembler tools yielded SPAdes using a kmer of 19 to produce the best assembly outcomes. We also found that de novo assembly using 21-25 nt small RNAs can result in chimeric assemblies of viral sequences and plant host sequences. Such non-specific assemblies can be resolved by using 21-22 nt or 24 nt small RNAs subsets. Among the 21 selected samples, we identified contigs with sequence similarity to 18 viruses and 3 viroids in 13 samples. Most of the viruses were assembled using only 21-22 nt long virus-derived siRNAs (viRNAs), except for one Citrus endogenous pararetrovirus that was more efficiently assembled using 24 nt long viRNAs. All three viroids found in this study were fully assembled using either 21-22 nt or 24 nt viRNAs. Optimised analysis workflows were customised within the Yabi web-based analytical environment. We present a fully automated viral surveillance and diagnosis web-based bioinformatics toolkit that provides a flexible, user-friendly, robust and scalable interface for the discovery and diagnosis of viral pathogens. We have implemented an automated viral surveillance and

  15. Digital diagnosis and treatment of mandibular condylar fractures based on Extensible Neuro imaging Archive Toolkit (XNAT.

    Directory of Open Access Journals (Sweden)

    ZhongWei Zhou

    Full Text Available The treatment of condylar fractures has long been controversial. In this paper, we established a database for accurate measurement, storage, management and analysis of patients' data, in order to help determine the best treatment plan.First of all, the diagnosis and treatment database was established based on XNAT, including 339 cases of condylar fractures and their related information. Then image segmentation, registration and three-dimensional (3D measurement were used to measure and analyze the condyle shapes. Statistical analysis was used to analyze the anatomical structure changes of condyle and the surrounding tissues at different stages before and after treatment. The processes of condylar fracture reestablishment at different stages were also dynamically monitored. Finally, based on all these information, the digital diagnosis and treatment plans for condylar fractures were developed.For the patients less than 18 years old with no significant dislocation, surgical treatment and conservative treatment were equally effective for intracapsular fracture, and had no significant difference for neck and basal fractures. For patients above 18 years old, there was no significant difference between the two treatment methods for intracapsular fractures; but for condylar neck and basal fractures, surgical treatment was better than conservative treatment. When condylar fracture shift angle was greater than 11 degrees, and mandibular ramus height reduction was greater than 4mm, the patients felt the strongest pain, and their mouths opening was severely restricted. There were 170 surgical cases with condylar fracture shift angel greater than 11 degrees, and 118 of them (69.4% had good prognosis, 52 of them (30.6% had complications such as limited mouth opening. There were 173 surgical cases with mandibular ramus height reduction more than 4mm, and 112 of them (64.7% had good prognosis, 61 of them (35.3% had complications such as limited mouth opening

  16. Web-based diagnosis and therapy of auditory prerequisites for reading and spelling

    Directory of Open Access Journals (Sweden)

    Krammer, Sandra

    2006-11-01

    Full Text Available Cognitive deficits in auditory or visual processing or in verbal short-term-memory are amongst others risk factors for the development of dyslexia (reading and spelling disability. By early identification and intervention (optimally before school entry, detrimental effects of these cognitive deficits on reading and spelling might be prevented. The goal of the CASPAR-project is to develop and evaluate web-based tools for diagnosis and therapy of cognitive prerequisites for reading and spelling, which are appropriate for kindergarten children. In the first approach CASPAR addresses auditory processing disorders. This article describes a computerized and web-based approach for screening and testing phoneme discrimination and for promoting phoneme discrimination abilities through interactive games in kindergarteners.

  17. Space-Based Diagnosis of Surface Ozone Sensitivity to Anthropogenic Emissions

    Science.gov (United States)

    Martin, Randall V.; Fiore, Arlene M.; VanDonkelaar, Aaron

    2004-01-01

    We present a novel capability in satellite remote sensing with implications for air pollution control strategy. We show that the ratio of formaldehyde columns to tropospheric nitrogen dioxide columns is an indicator of the relative sensitivity of surface ozone to emissions of nitrogen oxides (NO(x) = NO + NO2) and volatile organic compounds (VOCs). The diagnosis from these space-based observations is highly consistent with current understanding of surface ozone chemistry based on in situ observations. The satellite-derived ratios indicate that surface ozone is more sensitive to emissions of NO(x) than of VOCs throughout most continental regions of the Northern Hemisphere during summer. Exceptions include Los Angeles and industrial areas of Germany. A seasonal transition occurs in the fall when surface ozone becomes less sensitive to NOx and more sensitive to VOCs.

  18. Application of Quantum Dots-Based Biotechnology in Cancer Diagnosis: Current Status and Future Perspectives

    Directory of Open Access Journals (Sweden)

    Chun-Wei Peng

    2010-01-01

    Full Text Available The semiconductor nanocrystal quantum dots (QDs have excellent photo-physical properties, and the QDs-based probes have achieved encouraging developments in cellular and in vivo molecular imaging. More and more researches showed that QDs-based technology may become a promising approach in cancer research. In this review, we focus on recent application of QDs in cancer diagnosis and treatment, including early detection of primary tumor such as ovarian cancer, breast cancer, prostate cancer and pancreatic cancer, as well as regional lymph nodes and distant metastases. With the development of QDs synthesis and modification, the effect of QDs on tumor metastasis investigation will become more and more important in the future.

  19. Connectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes.

    Directory of Open Access Journals (Sweden)

    Zack Dvey-Aharon

    Full Text Available This article presents a novel connectivity analysis method that is suitable for multi-node networks such as EEG, MEG or EcOG electrode recordings. Its diagnostic power and ability to interpret brain states in schizophrenia is demonstrated on a set of 50 subjects that constituted of 25 healthy and 25 diagnosed with schizophrenia and treated with medication. The method can also be used for the automatic detection of schizophrenia; it exhibits higher sensitivity than state-of-the-art methods with no false positives. The detection is based on an analysis from a minute long pattern-recognition computer task. Moreover, this connectivity analysis leads naturally to an optimal choice of electrodes and hence to highly statistically significant results that are based on data from only 3-5 electrodes. The method is general and can be used for the diagnosis of other psychiatric conditions, provided an appropriate computer task is devised.

  20. Definitive diagnosis of early enamel and dentin cracks based on microscopic evaluation.

    Science.gov (United States)

    Clark, David J; Sheets, Cherilyn G; Paquette, Jacinthe M

    2003-01-01

    The diagnoses of cracked teeth and incomplete coronal fracture have historically been symptom based. The dental operating microscope at 16x magnification can fundamentally change a clinician's ability to diagnose such conditions. Clinicians have been observing cracks under extreme magnification for nearly a decade. Patterns have become clear that can lead to appropriate treatment prior to symptoms or to devastation to tooth structure. Conversely, many cracks are not structural and can lead to misdiagnosis and overtreatment. Methodic microscopic examination, an understanding of crack progression, and an appreciation of the types of cracks will guide a doctor to make appropriate decisions. Teeth can have structural cracks in various stages. To date, diagnosis and treatment are very often at end stage of crack development. This article gives new guidelines for recognition, visualization, classification, and treatment of cracked teeth based on the routine use of 16x magnification. The significance of enamel cracks as they relate to dentinal cracks is detailed.

  1. Application of probabilistically weighted graphs to image-based diagnosis of Alzheimer's disease using diffusion MRI

    Science.gov (United States)

    Maryam, Syeda; McCrackin, Laura; Crowley, Mark; Rathi, Yogesh; Michailovich, Oleg

    2017-03-01

    The world's aging population has given rise to an increasing awareness towards neurodegenerative disorders, including Alzheimers Disease (AD). Treatment options for AD are currently limited, but it is believed that future success depends on our ability to detect the onset of the disease in its early stages. The most frequently used tools for this include neuropsychological assessments, along with genetic, proteomic, and image-based diagnosis. Recently, the applicability of Diffusion Magnetic Resonance Imaging (dMRI) analysis for early diagnosis of AD has also been reported. The sensitivity of dMRI to the microstructural organization of cerebral tissue makes it particularly well-suited to detecting changes which are known to occur in the early stages of AD. Existing dMRI approaches can be divided into two broad categories: region-based and tract-based. In this work, we propose a new approach, which extends region-based approaches to the simultaneous characterization of multiple brain regions. Given a predefined set of features derived from dMRI data, we compute the probabilistic distances between different brain regions and treat the resulting connectivity pattern as an undirected, fully-connected graph. The characteristics of this graph are then used as markers to discriminate between AD subjects and normal controls (NC). Although in this preliminary work we omit subjects in the prodromal stage of AD, mild cognitive impairment (MCI), our method demonstrates perfect separability between AD and NC subject groups with substantial margin, and thus holds promise for fine-grained stratification of NC, MCI and AD populations.

  2. Highly sensitive dendrimer-based nanoplasmonic biosensor for drug allergy diagnosis.

    Science.gov (United States)

    Soler, Maria; Mesa-Antunez, Pablo; Estevez, M-Carmen; Ruiz-Sanchez, Antonio Jesus; Otte, Marinus A; Sepulveda, Borja; Collado, Daniel; Mayorga, Cristobalina; Torres, Maria Jose; Perez-Inestrosa, Ezequiel; Lechuga, Laura M

    2015-04-15

    A label-free biosensing strategy for amoxicillin (AX) allergy diagnosis based on the combination of novel dendrimer-based conjugates and a recently developed nanoplasmonic sensor technology is reported. Gold nanodisks were functionalized with a custom-designed thiol-ending-polyamido-based dendron (d-BAPAD) peripherally decorated with amoxicilloyl (AXO) groups (d-BAPAD-AXO) in order to detect specific IgE generated in patient's serum against this antibiotic during an allergy outbreak. This innovative strategy, which follows a simple one-step immobilization procedure, shows exceptional results in terms of sensitivity and robustness, leading to a highly-reproducible and long-term stable surface which allows achieving extremely low limits of detection. Moreover, the viability of this biosensor approach to analyze human biological samples has been demonstrated by directly analyzing and quantifying specific anti-AX antibodies in patient's serum without any sample pretreatment. An excellent limit of detection (LoD) of 0.6ng/mL (i.e. 0.25kU/L) has been achieved in the evaluation of clinical samples evidencing the potential of our nanoplasmonic biosensor as an advanced diagnostic tool to quickly identify allergic patients. The results have been compared and validated with a conventional clinical immunofluorescence assay (ImmunoCAP test), confirming an excellent correlation between both techniques. The combination of a novel compact nanoplasmonic platform and a dendrimer-based strategy provides a highly sensitive label free biosensor approach with over two times better detectability than conventional SPR. Both the biosensor device and the carrier structure hold great potential in clinical diagnosis for biomarker analysis in whole serum samples and other human biological samples. Copyright © 2014 Elsevier B.V. All rights reserved.

  3. Restricting Access to ART on the Basis of Criminal Record : An Ethical Analysis of a State-Enforced "Presumption Against Treatment" With Regard to Assisted Reproductive Technologies.

    Science.gov (United States)

    Thompson, Kara; McDougall, Rosalind

    2015-09-01

    As assisted reproductive technologies (ART) become increasingly popular, debate has intensified over the ethical justification for restricting access to ART based on various medical and non-medical factors. In 2010, the Australian state of Victoria enacted world-first legislation that denies access to ART for all patients with certain criminal or child protection histories. Patients and their partners are identified via a compulsory police and child protection check prior to commencing ART and, if found to have a previous relevant conviction or child protection order, are given a "presumption against treatment." This article reviews the legislation and identifies arguments that may be used to justify restricting access to ART for various reasons. The arguments reviewed include limitations of reproductive rights, inheriting undesirable genetic traits, distributive justice, and the welfare of the future child. We show that none of these arguments justifies restricting access to ART in the context of past criminal history. We show that a "presumption against treatment" is an unjustified infringement on reproductive freedom and that it creates various inconsistencies in current social, medical, and legal policy. We argue that a state-enforced policy of restricting access to ART based on the non-medical factor of past criminal history is an example of unjust discrimination and cannot be ethically justified, with one important exception: in cases where ART treatment may be considered futile on the basis that the parents are not expected to raise the resulting child.

  4. A concise evidence-based physical examination for diagnosis of acromioclavicular joint pathology: a systematic review.

    Science.gov (United States)

    Krill, Michael K; Rosas, Samuel; Kwon, KiHyun; Dakkak, Andrew; Nwachukwu, Benedict U; McCormick, Frank

    2018-02-01

    The clinical examination of the shoulder joint is an undervalued diagnostic tool for evaluating acromioclavicular (AC) joint pathology. Applying evidence-based clinical tests enables providers to make an accurate diagnosis and minimize costly imaging procedures and potential delays in care. The purpose of this study was to create a decision tree analysis enabling simple and accurate diagnosis of AC joint pathology. A systematic review of the Medline, Ovid and Cochrane Review databases was performed to identify level one and two diagnostic studies evaluating clinical tests for AC joint pathology. Individual test characteristics were combined in series and in parallel to improve sensitivities and specificities. A secondary analysis utilized subjective pre-test probabilities to create a clinical decision tree algorithm with post-test probabilities. The optimal special test combination to screen and confirm AC joint pathology combined Paxinos sign and O'Brien's Test, with a specificity of 95.8% when performed in series; whereas, Paxinos sign and Hawkins-Kennedy Test demonstrated a sensitivity of 93.7% when performed in parallel. Paxinos sign and O'Brien's Test demonstrated the greatest positive likelihood ratio (2.71); whereas, Paxinos sign and Hawkins-Kennedy Test reported the lowest negative likelihood ratio (0.35). No combination of special tests performed in series or in parallel creates more than a small impact on post-test probabilities to screen or confirm AC joint pathology. Paxinos sign and O'Brien's Test is the only special test combination that has a small and sometimes important impact when used both in series and in parallel. Physical examination testing is not beneficial for diagnosis of AC joint pathology when pretest probability is unequivocal. In these instances, it is of benefit to proceed with procedural tests to evaluate AC joint pathology. Ultrasound-guided corticosteroid injections are diagnostic and therapeutic. An ultrasound-guided AC joint

  5. 1H NMR- based metabolomics approaches as non- invasive tools for diagnosis of endometriosis

    Directory of Open Access Journals (Sweden)

    Negar Ghazi

    2016-01-01

    Full Text Available Background: So far, non-invasive diagnostic approaches such as ultrasound, magnetic resonance imaging, or blood tests do not have sufficient diagnostic power for endometriosis disease. Lack of a non-invasive diagnostic test contributes to the long delay between onset of symptoms and diagnosis of endometriosis. Objective: The present study focuses on the identification of predictive biomarkers in serum by pattern recognition techniques and uses partial least square discriminant analysis, multi-layer feed forward artificial neural networks (ANNs and quadratic discriminant analysis (QDA modeling tools for the early diagnosis of endometriosis in a minimally invasive manner by 1H- NMR based metabolomics. Materials and Methods: This prospective cohort study was done in Pasteur Institute, Iran in June 2013. Serum samples of 31 infertile women with endometriosis (stage II and III who confirmed by diagnostic laparoscopy and 15 normal women were collected and analyzed by nuclear magnetic resonance spectroscopy. The model was built by using partial least square discriminant analysis, QDA, and ANNs to determine classifier metabolites for early prediction risk of disease. Results: The levels of 2- methoxyestron, 2-methoxy estradiol, dehydroepiandrostion androstendione, aldosterone, and deoxy corticosterone were enhanced significantly in infertile group. While cholesterol and primary bile acids levels were decreased. QDA model showed significant difference between two study groups. Positive and negative predict value levels obtained about 71% and 78%, respectively. ANNs provided also criteria for detection of endometriosis. Conclusion: The QDA and ANNs modeling can be used as computational tools in noninvasive diagnose of endometriosis. However, the model designed by QDA methods is more efficient compared to ANNs in diagnosis of endometriosis patients.

  6. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

    Science.gov (United States)

    Sun, Wenqing; Zheng, Bin; Qian, Wei

    2017-10-01

    This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

  7. Model-Based Sensor Placement for Component Condition Monitoring and Fault Diagnosis in Fossil Energy Systems

    Energy Technology Data Exchange (ETDEWEB)

    Mobed, Parham [Texas Tech Univ., Lubbock, TX (United States); Pednekar, Pratik [West Virginia Univ., Morgantown, WV (United States); Bhattacharyya, Debangsu [West Virginia Univ., Morgantown, WV (United States); Turton, Richard [West Virginia Univ., Morgantown, WV (United States); Rengaswamy, Raghunathan [Texas Tech Univ., Lubbock, TX (United States)

    2016-01-29

    Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desired for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.

  8. Brain medical image diagnosis based on corners with importance-values.

    Science.gov (United States)

    Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

    2017-11-21

    Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection

  9. An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

    Data.gov (United States)

    National Aeronautics and Space Administration — Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation...

  10. The radiological and histopathological differential diagnosis of chordoid neoplasms in skull base

    Directory of Open Access Journals (Sweden)

    PAN Bin-cai

    2013-07-01

    Full Text Available Background Chordoid neoplasms refer to tumors appearing to have histological features of embryonic notochord, which is characterized by cords and lobules of neoplastic cells arranged within myxoid matrix. Because of radiological and histological similarities with myxoid matrix and overlapping immunohistochemical profile, chordoma, chordoid meningioma, chordoid glioma, and rare extraskeletal myxoid chondrosarcoma enter in the radiological and histological differential diagnosis at the site of skull base. However, there is always a great challenge for histopathologists to make an accurate diagnosis when encountering a chordoid neoplasm within or near the central nervous system. The aim of this study is to investigate and summarize the radiological, histological features and immunohistochemical profiles of chordoid neoplasms in skull base, and to find a judicious panel of immunostains to unquestionably help in diagnostically challenging cases. Methods A total of 23 cases of chordoid neoplasms in skull base, including 10 chordomas, 5 chordoid meningiomas, 3 chordoid gliomas and 5 extraskeletal myxoid chondrosarcomas, were collected from the First Affiliated Hospital, Sun Yat-sen University and Guangdong Tongjiang Hospital. MRI examination was performed on the patients before surgical treatment. Microscopical examination and immunohistochemical staining study using vimentin (Vim, pan-cytokeratin (PCK, epithelial membrane antigen (EMA, S?100 protein (S-100, glial fibrillary acidic protein (GFAP, D2-40, Galectin-3, CD3, CD20, Ki-67 were performed on the samples of cases. The clinicopathological data of the patients was also analyzed retrospectively. Results Most of chordomas were localized in the clivus with heterogeneous hyperintensity on T2WI scanning. The breakage of clivus was observed in most cases. Histologically, the tumor cells of chordoma exhibited bland nuclear features and some contained abundant vacuolated cytoplasm (the so

  11. Evaluation and diagnosis of wrist pain: a case-based approach.

    Science.gov (United States)

    Shehab, Ramsey; Mirabelli, Mark H

    2013-04-15

    Patients with wrist pain commonly present with an acute injury or spontaneous onset of pain without a definite traumatic event. A fall onto an outstretched hand can lead to a scaphoid fracture, which is the most commonly fractured carpal bone. Conventional radiography alone can miss up to 30 percent of scaphoid fractures. Specialized views (e.g., posteroanterior in ulnar deviation, pronated oblique) and repeat radiography in 10 to 14 days can improve sensitivity for scaphoid fractures. If a suspected scaphoid fracture cannot be confirmed with plain radiography, a bone scan or magnetic resonance imaging can be used. Subacute or chronic wrist pain usually develops gradually with or without a prior traumatic event. In these cases, the differential diagnosis is wide and includes tendinopathy and nerve entrapment. Overuse of the muscles of the forearm and wrist may lead to tendinopathy. Radial pain involving mostly the first extensor compartment is commonly de Quervain tenosynovitis. The diagnosis is based on history and examination findings of a positive Finkelstein test and a negative grind test. Nerve entrapment at the wrist presents with pain and also with sensory and sometimes motor symptoms. In ulnar neuropathies of the wrist, the typical presentation is wrist discomfort with sensory changes in the fourth and fifth digits. Activities that involve repetitive or prolonged wrist extension, such as cycling, karate, and baseball (specifically catchers), may increase the risk of ulnar neuropathy. Electrodiagnostic tests identify the area of nerve entrapment and the extent of the pathology. Copyright © 2013 American Academy of Family Physicians.

  12. Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

    Science.gov (United States)

    Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

    2017-11-01

    With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

  13. Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

    Directory of Open Access Journals (Sweden)

    Xinghui Zhang

    2015-01-01

    Full Text Available Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

  14. The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

    Science.gov (United States)

    Lee, S. C.

    1989-01-01

    The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

  15. Discovering mammography-based machine learning classifiers for breast cancer diagnosis.

    Science.gov (United States)

    Ramos-Pollán, Raúl; Guevara-López, Miguel Angel; Suárez-Ortega, Cesar; Díaz-Herrero, Guillermo; Franco-Valiente, Jose Miguel; Rubio-Del-Solar, Manuel; González-de-Posada, Naimy; Vaz, Mario Augusto Pires; Loureiro, Joana; Ramos, Isabel

    2012-08-01

    This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details. The method can be used under different data acquisition circumstances and exploits computer clusters to select well performing MLC configurations. We evaluated 286 cases extracted from the repository owned by HSJ-FMUP, where specialized radiologists segmented regions on CC and/or MLO images (biopsies provided the golden standard). Around 20,000 MLC configurations were evaluated, obtaining classifiers achieving an area under the ROC curve of 0.996 when combining features vectors extracted from CC and MLO views of the same case.

  16. Development of a fluorescence-based sensor for rapid diagnosis of cyanide exposure.

    Science.gov (United States)

    Jackson, Randy; Oda, Robert P; Bhandari, Raj K; Mahon, Sari B; Brenner, Matthew; Rockwood, Gary A; Logue, Brian A

    2014-02-04

    Although commonly known as a highly toxic chemical, cyanide is also an essential reagent for many industrial processes in areas such as mining, electroplating, and synthetic fiber production. The "heavy" use of cyanide in these industries, along with its necessary transportation, increases the possibility of human exposure. Because the onset of cyanide toxicity is fast, a rapid, sensitive, and accurate method for the diagnosis of cyanide exposure is necessary. Therefore, a field sensor for the diagnosis of cyanide exposure was developed based on the reaction of naphthalene dialdehyde, taurine, and cyanide, yielding a fluorescent β-isoindole. An integrated cyanide capture "apparatus", consisting of sample and cyanide capture chambers, allowed rapid separation of cyanide from blood samples. Rabbit whole blood was added to the sample chamber, acidified, and the HCN gas evolved was actively transferred through a stainless steel channel to the capture chamber containing a basic solution of naphthalene dialdehyde (NDA) and taurine. The overall analysis time (including the addition of the sample) was cyanide exposure. Most importantly, the sensor was 100% accurate in diagnosing cyanide poisoning for acutely exposed rabbits.

  17. Gear fault diagnosis based on the structured sparsity time-frequency analysis

    Science.gov (United States)

    Sun, Ruobin; Yang, Zhibo; Chen, Xuefeng; Tian, Shaohua; Xie, Yong

    2018-03-01

    Over the last decade, sparse representation has become a powerful paradigm in mechanical fault diagnosis due to its excellent capability and the high flexibility for complex signal description. The structured sparsity time-frequency analysis (SSTFA) is a novel signal processing method, which utilizes mixed-norm priors on time-frequency coefficients to obtain a fine match for the structure of signals. In order to extract the transient feature from gear vibration signals, a gear fault diagnosis method based on SSTFA is proposed in this work. The steady modulation components and impulsive components of the defective gear vibration signals can be extracted simultaneously by choosing different time-frequency neighborhood and generalized thresholding operators. Besides, the time-frequency distribution with high resolution is obtained by piling different components in the same diagram. The diagnostic conclusion can be made according to the envelope spectrum of the impulsive components or by the periodicity of impulses. The effectiveness of the method is verified by numerical simulations, and the vibration signals registered from a gearbox fault simulator and a wind turbine. To validate the efficiency of the presented methodology, comparisons are made among some state-of-the-art vibration separation methods and the traditional time-frequency analysis methods. The comparisons show that the proposed method possesses advantages in separating feature signals under strong noise and accounting for the inner time-frequency structure of the gear vibration signals.

  18. Conceptual Diagnosis Model Based on Distinct Knowledge Dyads for Interdisciplinary Environments

    Directory of Open Access Journals (Sweden)

    Cristian VIZITIU

    2014-06-01

    Full Text Available The present paper has a synergic dual purpose of bringing a psychological and neuroscience related perspective oriented towards decision making and knowledge creation diagnosis in the frame of Knowledge Management. !e conceptual model is built by means ofCognitive-Emotional and Explicit-Tacit knowledge dyads and structured on Analytic Hierarchy Process (AHP according to the hypothesis which designates the first dyad as an accessing mechanism of knowledge stored in the second dyad. Due to the well acknowledged needsconcerning new advanced decision making instruments and enhanced knowledge creation processes in the field of technical space projects emphasized by a high level of complexity, the herein study tries also to prove the relevance of the proposed conceptual diagnosis modelin Systems Engineering (SE methodology which foresees at its turn concurrent engineering within interdisciplinary working environments. !e theoretical model, entitled DiagnoSE, has the potential to provide practical implications to space/space related business sector butnot merely, and on the other hand, to trigger and inspire other knowledge management related researches for refining and testing the proposed instrument in SE or other similar decision making based working environment.

  19. Fault Diagnosis of Rotating Machinery Based on the Multiscale Local Projection Method and Diagonal Slice Spectrum

    Directory of Open Access Journals (Sweden)

    Yong Lv

    2018-04-01

    Full Text Available The vibration signals of bearings and gears measured from rotating machinery usually have nonlinear, nonstationary characteristics. The local projection algorithm cannot only reduce the noise of the nonlinear system, but can also preserve the nonlinear deterministic structure of the signal. The influence of centroid selection on the performance of noise reduction methods is analyzed, and the multiscale local projection method of centroid was proposed in this paper. This method considers both the geometrical shape and statistical error of the signal in high dimensional phase space, which can effectively eliminate the noise and preserve the complete geometric structure of the attractors. The diagonal slice spectrum can identify the frequency components of quadratic phase coupling and enlarge the coupled frequency component in the nonlinear signal. Therefore, the proposed method based on the above two algorithms can achieve more accurate results of fault diagnosis of gears and rolling bearings. The simulated signal is used to verify its effectiveness in a numerical simulation. Then, the proposed method is conducted for fault diagnosis of gears and rolling bearings in application researches. The fault characteristics of faulty bearings and gears can be extracted successfully in the researches. The experimental results indicate the effectiveness of the novel proposed method.

  20. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Karthikeyan Elangovan

    2017-10-01

    Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

  1. An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image

    Science.gov (United States)

    Liu, Hang; Xie, Ting; Ran, Jian; Gao, Shan

    2017-10-01

    It is essential for a data center to maintain server security and stability. Long-time overload operation or high room temperature may cause service disruption even a server crash, which would result in great economic loss for business. Currently, the methods to avoid server outages are monitoring and forecasting. Thermal camera can provide fine texture information for monitoring and intelligent thermal management in large data center. This paper presents an efficient method for server thermal fault monitoring and diagnosis based on infrared image. Initially thermal distribution of server is standardized and the interest regions of the image are segmented manually. Then the texture feature, Hu moments feature as well as modified entropy feature are extracted from the segmented regions. These characteristics are applied to analyze and classify thermal faults, and then make efficient energy-saving thermal management decisions such as job migration. For the larger feature space, the principal component analysis is employed to reduce the feature dimensions, and guarantee high processing speed without losing the fault feature information. Finally, different feature vectors are taken as input for SVM training, and do the thermal fault diagnosis after getting the optimized SVM classifier. This method supports suggestions for optimizing data center management, it can improve air conditioning efficiency and reduce the energy consumption of the data center. The experimental results show that the maximum detection accuracy is 81.5%.

  2. Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

    Science.gov (United States)

    Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

    2007-12-15

    Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

  3. Disposable Electrochemical Immunosensor Diagnosis Device Based on Nanoparticle Probe and Immunochromatographic Strip

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Guodong; Lin, Ying-Ying; Wang, Jun; Wu, Hong; Wai, Chien M.; Lin, Yuehe

    2007-10-15

    We describe a disposable electrochemical immunosensor diagnosis device that is based on the immunochromatographic strip technique and an electrochemical immunoassay based on quantum dot (QD, CdS@ZnS) labels. The device takes advantage of the speed and low-cost of the conventional immunochromatographic strip test and the high-sensitivity of the nanoparticle-based electrochemical immunoassay. A sandwich immunoreaction was performed on the immunochromatographic strip, and the captured QD labels in the test zone were determined by highly sensitive stripping voltammetric measurement of the dissolved metallic component (cadmium) with a disposable-screen-printed electrode, which is embedded underneath the membrane on the test zone. The new device coupled with a portable electrochemical analyzer shows great promise for in-field and point-of-care quantitative testing of disease-related protein biomarkers. The parameters (e.g., voltammetric measurement of QD labels, antibody immobilization, the loading amount of QD-antibody, and the immunoreaction time) that govern the sensitivity and reproducibility of the device were optimized with IgG model analyte. The voltammetric response of the optimized device is highly linear over the range of 0.1 to 10 ng mL-1 IgG, and the limit of detection is estimated to be 30 pg mL-1 in association with a 7-min immunoreaction time. The detection limit was improved to 10 pg mL-1 using a 20-min immunoreaction time. The new disposable electrochemical diagnosis device thus provides a more user-friendly, rapid, clinically accurate, less expensive, and quantitative tool for protein detection.

  4. Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer

    International Nuclear Information System (INIS)

    Kitahama, Hiroyuki

    1991-01-01

    The aim of this study is to present efficacy of storage phosphor-based digital mammography (CR-mammography) in diagnosis of breast cancer. Ninety-seven cases with breast cancer including 44 cases less than 2 cm in macroscopic size (t1 cases) were evaluated using storage phosphor-based digital mammography (2000 x 2510 pixels by 10 bits). Abnormal findings on CR-mammography were detected in 86 cases (88.7%) of 97 women with breast cancer. Sensitivity of CR-mammography was 88.7%. It was superior to that of film-screen mammography. On t1 breast cancer cases, sensitivity on CR-mammography was 88.6%. False negative rate in t1 breast cancer cases was reduced by image processing using CR-mammography. To evaluate microcalcifications, CR-mammograms and film-screen mammograms were investigated in 22 cases of breast cancer proven pathologically the existence of microcalcifications and 11 paraffin tissue blocks of breast cancer. CR-mammography was superior to film-screen mammography in recognizing of microcalcifications. As regards the detectability for the number and the shape of microcalcifications, CR-mammography was equivalent to film-screen mammography. Receiver operating characteristic (ROC) analysis by eight observers was performed for CR-mammography and film-screen mammography with 54 breast cancer patients and 54 normal cases. The detectability of abnormal findings of breast cancer on CR-mammography (ROC area=0.91) was better than that on film-screen mammography (ROC area=0.88) (p<0.05). Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer was discussed and demonstrated in this study. (author)

  5. The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer’s disease diagnosis

    Directory of Open Access Journals (Sweden)

    Raymundo eCassani

    2014-03-01

    Full Text Available Over the last decade, electroencephalography (EEG has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD. EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system ``semi-automated. Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (disadvantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR algorithms (both alone and in combination with each other on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR, blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA, and wavelet enhanced independent component analysis (wICA. Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate, early detection (control vs. mild, and disease progression (mild vs. moderate, thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.

  6. Serological Assays Based on Recombinant Viral Proteins for the Diagnosis of Arenavirus Hemorrhagic Fevers

    Directory of Open Access Journals (Sweden)

    Masayuki Saijo

    2012-10-01

    Full Text Available The family Arenaviridae, genus Arenavirus, consists of two phylogenetically independent groups: Old World (OW and New World (NW complexes. The Lassa and Lujo viruses in the OW complex and the Guanarito, Junin, Machupo, Sabia, and Chapare viruses in the NW complex cause viral hemorrhagic fever (VHF in humans, leading to serious public health concerns. These viruses are also considered potential bioterrorism agents. Therefore, it is of great importance to detect these pathogens rapidly and specifically in order to minimize the risk and scale of arenavirus outbreaks. However, these arenaviruses are classified as BSL-4 pathogens, thus making it difficult to develop diagnostic techniques for these virus infections in institutes without BSL-4 facilities. To overcome these difficulties, antibody detection systems in the form of an enzyme-linked immunosorbent assay (ELISA and an indirect immunofluorescence assay were developed using recombinant nucleoproteins (rNPs derived from these viruses. Furthermore, several antigen-detection assays were developed. For example, novel monoclonal antibodies (mAbs to the rNPs of Lassa and Junin viruses were generated. Sandwich antigen-capture (Ag-capture ELISAs using these mAbs as capture antibodies were developed and confirmed to be sensitive and specific for detecting the respective arenavirus NPs. These rNP-based assays were proposed to be useful not only for an etiological diagnosis of VHFs, but also for seroepidemiological studies on VHFs. We recently developed arenavirus neutralization assays using vesicular stomatitis virus (VSV-based pseudotypes bearing arenavirus recombinant glycoproteins. The goal of this article is to review the recent advances in developing laboratory diagnostic assays based on recombinant viral proteins for the diagnosis of VHFs and epidemiological studies on the VHFs caused by arenaviruses.

  7. Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology

    Directory of Open Access Journals (Sweden)

    Chaves Rosa

    2012-07-01

    Full Text Available Abstract Background Functional brain images such as Single-Photon Emission Computed Tomography (SPECT and Positron Emission Tomography (PET have been widely used to guide the clinicians in the Alzheimer’s Disease (AD diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD Systems. Methods It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT, Principal Component Analysis (PCA or Partial Least Squares (PLS (the two latter also analysed with a LMNN transformation. Regarding the classifiers, kernel Support Vector Machines (SVMs and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. Results Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i linear transformation of the PLS or PCA reduced data, ii feature reduction technique, and iii classifier (with Euclidean, Mahalanobis or Energy-based methodology. The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT and 90.67%, 88% and 93.33% (for PET, respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. Conclusions All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between

  8. Presumptive binge eating disorder in type 2 diabetes mellitus patients and its effect in metabolic control

    Directory of Open Access Journals (Sweden)

    Sandra Soares Melo

    2009-09-01

    Full Text Available Objective: This study sought to determine the presence of diagnosis suggestive of binge eating disorder in individuals with type 2 diabetes mellitus, and to evaluate the influence of such disorder on the metabolic control. Methods: sixty-three patients with type 2 diabetes mellitus and registered  at the Diabetes and Hypertension Program of a Health Unit in the town of Balneário Camboriú, Santa Catarina, Brazil, were evaluated. The diagnosis of binge eating disorder was made by analysis of the Questionnaire on Eating and Weight Patterms – Revised. For the evaluation of metabolic control, 10 ml of blood was collected, and the serum glucose, glycated hemoglobin, tryglicerides, cholestrol and fractions were determined. Weight and height were determined for evaluation of national nutritional state, according to the body mass index. Rresults: Among the evaluated individuals, 29% presented a diagnosis suggestive of binge eating disorder, with higher prevalence among females. The individuals with diagnosis suggestive of binge eating disorder presented a higher average body mass index value than the group without diagnosis. The serum concentrations of glycated hemoglobin (p = 0.02 and triglicerides (p = 0.03 were statistically higher in the group with diagnosis suggestive of binge eating disorder. Cconclusions: Based on the results of this study, it is possible to conclude that the presence of binge eating disorder in individuals with type 2 diabetes mellitus favors an increase in body weight and has a negative influence on metabolic control, contributing to the early emergence of complications related to the disease.

  9. Matrix Failure Modes and Effects Analysis as a Knowledge Base for a Real Time Automated Diagnosis Expert System

    Science.gov (United States)

    Herrin, Stephanie; Iverson, David; Spukovska, Lilly; Souza, Kenneth A. (Technical Monitor)

    1994-01-01

    Failure Modes and Effects Analysis contain a wealth of information that can be used to create the knowledge base required for building automated diagnostic Expert systems. A real time monitoring and diagnosis expert system based on an actual NASA project's matrix failure modes and effects analysis was developed. This Expert system Was developed at NASA Ames Research Center. This system was first used as a case study to monitor the Research Animal Holding Facility (RAHF), a Space Shuttle payload that is used to house and monitor animals in orbit so the effects of space flight and microgravity can be studied. The techniques developed for the RAHF monitoring and diagnosis Expert system are general enough to be used for monitoring and diagnosis of a variety of other systems that undergo a Matrix FMEA. This automated diagnosis system was successfully used on-line and validated on the Space Shuttle flight STS-58, mission SLS-2 in October 1993.

  10. Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis.

    Science.gov (United States)

    Wang, Yalin; Yuan, Lei; Shi, Jie; Greve, Alexander; Ye, Jieping; Toga, Arthur W; Reiss, Allan L; Thompson, Paul M

    2013-07-01

    Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification. Copyright © 2013

  11. Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter

    Directory of Open Access Journals (Sweden)

    Li Sun

    2014-01-01

    Full Text Available Fluidized bed combustion (FBC boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD. The simulation results demonstrate that the approach is feasible and effective.

  12. Method of Fusion Diagnosis for Dam Service Status Based on Joint Distribution Function of Multiple Points

    Directory of Open Access Journals (Sweden)

    Zhenxiang Jiang

    2016-01-01

    Full Text Available The traditional methods of diagnosing dam service status are always suitable for single measuring point. These methods also reflect the local status of dams without merging multisource data effectively, which is not suitable for diagnosing overall service. This study proposes a new method involving multiple points to diagnose dam service status based on joint distribution function. The function, including monitoring data of multiple points, can be established with t-copula function. Therefore, the possibility, which is an important fusing value in different measuring combinations, can be calculated, and the corresponding diagnosing criterion is established with typical small probability theory. Engineering case study indicates that the fusion diagnosis method can be conducted in real time and the abnormal point can be detected, thereby providing a new early warning method for engineering safety.

  13. Gearbox fault diagnosis based on time-frequency domain synchronous averaging and feature extraction technique

    Science.gov (United States)

    Zhang, Shengli; Tang, Jiong

    2016-04-01

    Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.

  14. Effective acupuncture practice through diagnosis based on distribution of meridian pathways & related syndromes.

    Science.gov (United States)

    Chen, Yemeng; Zheng, Xin; Li, Hui; Zhang, Qunce; Wang, Tianfang

    2011-01-01

    This article discusses the importance of acupuncture practice utilizing diagnosis and distribution of various meridians and connecting channels based on meridian theory. The meridian system is considered as basic anatomy for acupuncture, so the corresponding pathways and related syndromes of different channels should play a key role in differentiation, known as meridian-related pattern differentiation. Since this doctrine originated in ancient times and was not so well developed in later generations, many acupuncturists are not able to utilize it efficiently. The authors survey how this doctrine was weakened during the past century, especially in acupuncture education for foreigners, and how this important method is currently being reinvigorated. This article also lays out the ways this doctrine can be applied clinically and introduces examples of a variety of indications including some difficult cases, such as whiplash injury, intervertebral disc herniation, oculomotor nerve paralysis, and eczema, etc.

  15. Personalized Clinical Diagnosis in Data Bases for Treatment Support in Phthisiology.

    Science.gov (United States)

    Lugovkina, T K; Skornyakov, S N; Golubev, D N; Egorov, E A; Medvinsky, I D

    2016-01-01

    The decision-making is a key event in the clinical practice. The program products with clinical decision support models in electronic data-base as well as with fixed decision moments of the real clinical practice and treatment results are very actual instruments for improving phthisiological practice and may be useful in the severe cases caused by the resistant strains of Mycobacterium tuberculosis. The methodology for gathering and structuring of useful information (critical clinical signals for decisions) is described. Additional coding of clinical diagnosis characteristics was implemented for numeric reflection of the personal situations. The created methodology for systematization and coding Clinical Events allowed to improve the clinical decision models for better clinical results.

  16. Fetal Cell Based Prenatal Diagnosis: Perspectives on the Present and Future

    Directory of Open Access Journals (Sweden)

    Morris Fiddler

    2014-09-01

    Full Text Available The ability to capture and analyze fetal cells from maternal circulation or other sources during pregnancy has been a goal of prenatal diagnostics for over thirty years. The vision of replacing invasive prenatal diagnostic procedures with the prospect of having the entire fetal genome in hand non-invasively for chromosomal and molecular studies for both clinical and research use has brought many investigators and innovations into the effort. While the object of this desire, however, has remained elusive, the aspiration for this approach to non-invasive prenatal diagnosis remains and the inquiry has continued. With the advent of screening by cell-free DNA analysis, the standards for fetal cell based prenatal diagnostics have been sharpened. Relevant aspects of the history and the current status of investigations to meet the goal of having an accessible and reliable strategy for capturing and analyzing fetal cells during pregnancy are reviewed.

  17. Waiting time disparities in breast cancer diagnosis and treatment: a population-based study in France.

    Science.gov (United States)

    Molinié, F; Leux, C; Delafosse, P; Ayrault-Piault, S; Arveux, P; Woronoff, A S; Guizard, A V; Velten, M; Ganry, O; Bara, S; Daubisse-Marliac, L; Tretarre, B

    2013-10-01

    Waiting times are key indicators of a health's system performance, but are not routinely available in France. We studied waiting times for diagnosis and treatment according to patients' characteristics, tumours' characteristics and medical management options in a sample of 1494 breast cancers recorded in population-based registries. The median waiting time from the first imaging detection to the treatment initiation was 34 days. Older age, co-morbidity, smaller size of tumour, detection by organised screening, biopsy, increasing number of specimens removed, multidisciplinary consulting meetings and surgery as initial treatment were related to increased waiting times in multivariate models. Many of these factors were related to good practices guidelines. However, the strong influence of organised screening programme and the disparity of waiting times according to geographical areas were of concern. Better scheduling of diagnostic tests and treatment propositions should improve waiting times in the management of breast cancer in France. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Explore the Possibility of Early Clinical Diagnosis of Endocrine Ophthalmopathy Based on Eye Symptoms of Hyperthyroidism

    Directory of Open Access Journals (Sweden)

    V. G. Likhvantseva

    2016-01-01

    Full Text Available Purpose: to study the possibility of early clinical diagnosis of endocrine ophthalmopathy based on ocular symptoms of hyperthyroidism. Patients and methods: we analyzed the prevalence of ocular symptoms of hyperthyroidism in 139 patients (278 orbits with newly diagnosed endocrine ophthalmopathy (group 1, developed on the background of diffuse toxic goiter. The comparison group consisted of 80 patients (160 orbits with newly diagnosed diffuse toxic goiter with no radiographic evidence of endocrine ophthalmopathy (group 2. All patients were examined by an ophthalmologist and endocrinologist. We analyzed the prevalence of ocular symptoms of hyperthyroidism (symptom Dalrymple’, Mobius’, Zenger’, and combinations thereof, often encountered in diffuse toxic goiter, flowing with endocrine ophthalmopathy, and/or lack thereof - in the group of “thyrotoxic exophthalmos”. We took into account the frequency distribution of these clinical signs, and their combinations. We analyzed the clinical sensitivity and specificity of diagnosis based on the three most common symptoms, and their combinations, associated both with thyrotoxicosis and with endocrine ophthalmopathy. Results: Dalrymple’ symptom, is more common in thyrotoxic exophthalmos than with endocrine ophthalmopathy (compared to 100.0% versus 61.9 %, p<0,001. This suggests that Dalrymple’ symptom leads to over diagnosis aspect endocrine ophthalmopathy. It is obvious that it can be used to recognize and thyrotoxic exophthalmos hyperthyroidism, but you cann’t credibly claim based on orbit about the presence of the disease. In this aspect, the greatest practical interest to provide a comparative assessment of the frequency of detection of symptoms of Mobius’ and Zenger’ and their combinations in a population of endocrine ophthalmopathy and in the group of thyrotoxic exophthalmos. Significantly more symptoms Zenger’ and Mobius’ developed with endocrine ophthalmopathy (66,2% and 81

  19. Validation of Antibody-Based Strategies for Diagnosis of Pediatric Celiac Disease Without Biopsy.

    Science.gov (United States)

    Wolf, Johannes; Petroff, David; Richter, Thomas; Auth, Marcus K H; Uhlig, Holm H; Laass, Martin W; Lauenstein, Peter; Krahl, Andreas; Händel, Norman; de Laffolie, Jan; Hauer, Almuthe C; Kehler, Thomas; Flemming, Gunter; Schmidt, Frank; Rodrigues, Astor; Hasenclever, Dirk; Mothes, Thomas

    2017-08-01

    A diagnosis of celiac disease is made based on clinical, genetic, serologic, and duodenal morphology features. Recent pediatric guidelines, based largely on retrospective data, propose omitting biopsy analysis for patients with concentrations of IgA against tissue transglutaminase (IgA-TTG) >10-fold the upper limit of normal (ULN) and if further criteria are met. A retrospective study concluded that measurements of IgA-TTG and total IgA, or IgA-TTG and IgG against deamidated gliadin (IgG-DGL) could identify patients with and without celiac disease. Patients were assigned to categories of no celiac disease, celiac disease, or biopsy required, based entirely on antibody assays. We aimed to validate the positive and negative predictive values (PPV and NPV) of these diagnostic procedures. We performed a prospective study of 898 children undergoing duodenal biopsy analysis to confirm or rule out celiac disease at 13 centers in Europe. We compared findings from serologic analysis with findings from biopsy analyses, follow-up data, and diagnoses made by the pediatric gastroenterologists (celiac disease, no celiac disease, or no final diagnosis). Assays to measure IgA-TTG, IgG-DGL, and endomysium antibodies were performed by blinded researchers, and tissue sections were analyzed by local and blinded reference pathologists. We validated 2 procedures for diagnosis: total-IgA and IgA-TTG (the TTG-IgA procedure), as well as IgG-DGL with IgA-TTG (TTG-DGL procedure). Patients were assigned to categories of no celiac disease if all assays found antibody concentrations celiac disease if at least 1 assay measured antibody concentrations >10-fold the ULN. All other cases were considered to require biopsy analysis. ULN values were calculated using the cutoff levels suggested by the test kit manufacturers. HLA typing was performed for 449 participants. We used models that considered how specificity values change with prevalence to extrapolate the PPV and NPV to populations with lower

  20. Specific T-cell epitopes for immunoassay-based diagnosis of Mycobacterium tuberculosis infection

    DEFF Research Database (Denmark)

    Brock, I; Weldingh, K; Leyten, EM

    2004-01-01

    Specific T-cell epitopes for immunoassay-based diagnosis of Mycobacterium tuberculosis infection.Brock I, Weldingh K, Leyten EM, Arend SM, Ravn P, Andersen P. Department of Infectious Disease Immunology, Statens Serum Institute, Artillerivej 5, DK-2300 Copenhagen S, Denmark. The currently used...... method for immunological detection of tuberculosis infection, the tuberculin skin test, has low specificity. Antigens specific for Mycobacterium tuberculosis to replace purified protein derivative are therefore urgently needed. We have performed a rigorous assessment of the diagnostic potential of four...... recently identified antigens (Rv2653, Rv2654, Rv3873, and Rv3878) from genomic regions that are lacking from the Mycobacterium bovis bacillus Calmette-Guerin (BCG) vaccine strains as well as from the most common nontuberculous mycobacteria. The fine specificity of potential epitopes in these molecules...

  1. Specific T-cell epitopes for immunoassay-based diagnosis of Mycobacterium tuberculosis infection

    DEFF Research Database (Denmark)

    Brock, I; Weldingh, K; Leyten, EM

    2004-01-01

    Specific T-cell epitopes for immunoassay-based diagnosis of Mycobacterium tuberculosis infection.Brock I, Weldingh K, Leyten EM, Arend SM, Ravn P, Andersen P. Department of Infectious Disease Immunology, Statens Serum Institute, Artillerivej 5, DK-2300 Copenhagen S, Denmark. The currently used...... method for immunological detection of tuberculosis infection, the tuberculin skin test, has low specificity. Antigens specific for Mycobacterium tuberculosis to replace purified protein derivative are therefore urgently needed. We have performed a rigorous assessment of the diagnostic potential of four...... selected and combined the specific peptide stretches from the four proteins not recognized by M. bovis BCG-vaccinated individuals. These peptide stretches were tested with peripheral blood mononuclear cells obtained from patients with microscopy- or culture-confirmed tuberculosis and from healthy M. bovis...

  2. A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory.

    Science.gov (United States)

    Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying

    2016-01-01

    Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.

  3. Supporting diagnosis and treatment in medical care based on Big Data processing.

    Science.gov (United States)

    Lupşe, Oana-Sorina; Crişan-Vida, Mihaela; Stoicu-Tivadar, Lăcrămioara; Bernard, Elena

    2014-01-01

    With information and data in all domains growing every day, it is difficult to manage and extract useful knowledge for specific situations. This paper presents an integrated system architecture to support the activity in the Ob-Gin departments with further developments in using new technology to manage Big Data processing - using Google BigQuery - in the medical domain. The data collected and processed with Google BigQuery results from different sources: two Obstetrics & Gynaecology Departments, the TreatSuggest application - an application for suggesting treatments, and a home foetal surveillance system. Data is uploaded in Google BigQuery from Bega Hospital Timişoara, Romania. The analysed data is useful for the medical staff, researchers and statisticians from public health domain. The current work describes the technological architecture and its processing possibilities that in the future will be proved based on quality criteria to lead to a better decision process in diagnosis and public health.

  4. Web-based computer-aided-diagnosis (CAD) system for bone age assessment (BAA) of children

    Science.gov (United States)

    Zhang, Aifeng; Uyeda, Joshua; Tsao, Sinchai; Ma, Kevin; Vachon, Linda A.; Liu, Brent J.; Huang, H. K.

    2008-03-01

    Bone age assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on a left hand and wrist radiograph. The most commonly used standard: Greulich and Pyle (G&P) Hand Atlas was developed 50 years ago and exclusively based on Caucasian population. Moreover, inter- & intra-observer discrepancies using this method create a need of an objective and automatic BAA method. A digital hand atlas (DHA) has been collected with 1,400 hand images of normal children from Asian, African American, Caucasian and Hispanic descends. Based on DHA, a fully automatic, objective computer-aided-diagnosis (CAD) method was developed and it was adapted to specific population. To bring DHA and CAD method to the clinical environment as a useful tool in assisting radiologist to achieve higher accuracy in BAA, a web-based system with direct connection to a clinical site is designed as a novel clinical implementation approach for online and real time BAA. The core of the system, a CAD server receives the image from clinical site, processes it by the CAD method and finally, generates report. A web service publishes the results and radiologists at the clinical site can review it online within minutes. This prototype can be easily extended to multiple clinical sites and will provide the foundation for broader use of the CAD system for BAA.

  5. Identification of embryonic chromosomal abnormality using FISH-based preimplantaion genetic diagnosis

    Institute of Scientific and Technical Information of China (English)

    叶英辉; 徐晨明; 金帆; 钱羽力

    2004-01-01

    Objective: Embryonic chromosomal abnormality is one of the main reasons for in vitro fertilization (IVF) failure. This study aimed at evaluating the value of Fluorescence in-situ Hybridization (FISH)-based Preimplantation Genetic Diagnosis (PGD) in screening for embryonic chromosomal abnormality to increase the successful rate of IVF. Method: Ten couples, four with high risk of chromosomal abnormality and six infertile couples, underwent FISH-based PGD during IVF procedure. At day 3, one or two blastomeres were aspirated from each embryo. Biopsied blastomeres were examined using FISH analysis to screen out embryos with chromosomal abnormalities. At day 4, embryos without detectable chromosomal abnormality were transferred to the mother bodies as in regular IVF. Results: Among 54 embryos screened using FISH-based PGD, 30 embryos were detected to have chromosomal abnormalities. The 24 healthy embryos were implanted, resulting in four clinical pregnancies, two of which led to successful normal birth of two healthy babies; one to ongoing pregnancy during the writing of this article; and one to ectopic pregnancy. Conclusion: FISH-based PGD is an effective method for detecting embryonic chromosomal abnormality, which is one of the common causes of spontaneous miscarriages and chromosomally unbalanced offsprings.

  6. Identification of embryonic chromosomal abnormality using FISH-based preimplantaion genetic diagnosis

    Institute of Scientific and Technical Information of China (English)

    叶英辉; 徐晨明; 金帆; 钱羽力

    2004-01-01

    Objective: Embryonic chromosomal abnormality is one of the main reasons for in vitro fertilization (IVF)failure. This study aimed at evaluating the value of Fluorescence in-situ Hybridization (FISH)-based Preimplantation Genetic Diagnosis (PGD) in screening for embryonic chromosomal abnormality to increase the successful rate of IVF. Method:Ten couples, four with high risk of chromosomal abnormality and six infertile couples, underwent FISH-based PGD during IVF procedure. At day 3, one or two blastomeres were aspirated from each embryo. Biopsied blastomeres were examined using FISH analysis to screen out embryos with chromosomal abnormalities. At day 4, embryos without detectable chromosomal abnormality were transferred to the mother bodies as in regular IVF. Results: Among 54 embryos screened using FISH-based PGD, 30 embryos were detected to have chromosomal abnormalities. The 24 healthy embryos were implanted,resulting in four clinical pregnancies, two of which led to successful normal birth of two healthy babies; one to ongoing pregnancy during the writing of this article; and one to ectopic pregnancy. Conclusion: FISH-based PGD is an effective method for detecting embryonic chromosomal abnormality, which is one of the common causes of spontaneous miscarriages and chromosomally unbalanced offsprings.

  7. Model-based energy monitoring and diagnosis of telecommunication cooling systems

    International Nuclear Information System (INIS)

    Sorrentino, Marco; Acconcia, Matteo; Panagrosso, Davide; Trifirò, Alena

    2016-01-01

    A methodology is proposed for on-line monitoring of cooling load supplied by Telecommunication (TLC) cooling systems. Sensible cooling load is estimated via a proportional integral controller-based input estimator, whereas a lumped parameters model was developed aiming at estimating air handling units (AHUs) latent heat load removal. The joint deployment of above estimators enables accurate prediction of total cooling load, as well as of related AHUs and free-coolers energy performance. The procedure was then proven effective when extended to cooling systems having a centralized chiller, through model-based estimation of a key performance metric, such as the energy efficiency ratio. The results and experimental validation presented throughout the paper confirm the suitability of the proposed procedure as a reliable and effective energy monitoring and diagnostic tool for TLC applications. Moreover, the proposed modeling approach, beyond its direct contribution towards smart use and conservation of energy, can be fruitfully deployed as a virtual sensor of removed heat load into a variety of residential and industrial applications. - Highlights: • Accurate cooling load prediction in telecommunication rooms. • Development of an input-estimator for sensible cooling load simulation. • Model-based estimation of latent cooling load. • Model-based prediction of centralized chiller energy performance in central offices. • Diagnosis-oriented application of proposed cooling load estimator.

  8. Diagnosis and treatment of acute ankle injuries: development of an evidence-based algorithm

    Directory of Open Access Journals (Sweden)

    Hans Polzer

    2012-01-01

    Full Text Available Acute ankle injuries are among the most common injuries in emergency departments. However, a standardized examination and an evidence-based treatment are missing. Therefore, aim of this study was to systematically search the current literature, classify the evidence and develop an algorithm for diagnosis and treatment of acute ankle injuries. We systematically searched PubMed and the Cochrane Database for randomized controlled trials, meta-analysis, systematic reviews, or if applicable observational studies and classified them according to their level of evidence. According to the currently available literature, the following recommendations are given. The Ottawa Ankle/Foot Rule should be applied in order to rule out fractures, Physical examination is sufficient for diagnosing injuries to the lateral ligament complex. Classification into stable and unstable injuries is applicable and of clinical importance. The squeeze-, crossed leg- and external rotation test are indicative for injuries of the syndesmosis. Magnetic resonance imaging is recommended to verify such injuries. Stable ankle sprains have a good prognosis, while for unstable ankle sprains conservative treatment is at least as effective as operative treatment without carrying possible complications. Early functional treatment leads to the fastest recovery and the least rate of re-injury. Supervised rehabilitation reduces residual symptoms and re-injuries. Taken these recommendations into account, we here present an applicable and evidence-based step by step decision pathway for the diagnosis and treatment of acute ankle injuries, which can be implemented in any emergency department or doctor’s practice. It provides quality assurance for the patient and confidence for the attending physician.

  9. Real time diagnosis of bladder cancer with probe-based confocal laser endomicroscopy

    Science.gov (United States)

    Liu, Jen-Jane; Wu, Katherine; Adams, Winifred; Hsiao, Shelly T.; Mach, Kathleen E.; Beck, Andrew H.; Jensen, Kristin C.; Liao, Joseph C.

    2011-02-01

    Probe-based confocal laser endomicroscopy (pCLE) is an emerging technology for in vivo optical imaging of the urinary tract. Particularly for bladder cancer, real time optical biopsy of suspected lesions will likely lead to improved management of bladder cancer. With pCLE, micron scale resolution is achieved with sterilizable imaging probes (1.4 or 2.6 mm diameter), which are compatible with standard cystoscopes and resectoscopes. Based on our initial experience to date (n = 66 patients), we have demonstrated the safety profile of intravesical fluorescein administration and established objective diagnostic criteria to differentiate between normal, benign, and neoplastic urothelium. Confocal images of normal bladder showed organized layers of umbrella cells, intermediate cells, and lamina propria. Low grade bladder cancer is characterized by densely packed monomorphic cells with central fibrovascular cores, whereas high grade cancer consists of highly disorganized microarchitecture and pleomorphic cells with indistinct cell borders. Currently, we are conducting a diagnostic accuracy study of pCLE for bladder cancer diagnosis. Patients scheduled to undergo transurethral resection of bladder tumor are recruited. Patients undergo first white light cystocopy (WLC), followed by pCLE, and finally histologic confirmation of the resected tissues. The diagnostic accuracy is determined both in real time by the operative surgeon and offline after additional image processing. Using histology as the standard, the sensitivity, specificity, positive and negative predictive value of WLC and WLC + pCLE are calculated. With additional validation, pCLE may prove to be a valuable adjunct to WLC for real time diagnosis of bladder cancer.

  10. Computer-aided diagnosis based on enhancement of degraded fundus photographs.

    Science.gov (United States)

    Jin, Kai; Zhou, Mei; Wang, Shaoze; Lou, Lixia; Xu, Yufeng; Ye, Juan; Qian, Dahong

    2018-05-01

    Retinal imaging is an important and effective tool for detecting retinal diseases. However, degraded images caused by the aberrations of the eye can disguise lesions, so that a diseased eye can be mistakenly diagnosed as normal. In this work, we propose a new image enhancement method to improve the quality of degraded images. A new method is used to enhance degraded-quality fundus images. In this method, the image is converted from the input RGB colour space to LAB colour space and then each normalized component is enhanced using contrast-limited adaptive histogram equalization. Human visual system (HVS)-based fundus image quality assessment, combined with diagnosis by experts, is used to evaluate the enhancement. The study included 191 degraded-quality fundus photographs of 143 subjects with optic media opacity. Objective quality assessment of image enhancement (range: 0-1) indicated that our method improved colour retinal image quality from an average of 0.0773 (variance 0.0801) to an average of 0.3973 (variance 0.0756). Following enhancement, area under curves (AUC) were 0.996 for the glaucoma classifier, 0.989 for the diabetic retinopathy (DR) classifier, 0.975 for the age-related macular degeneration (AMD) classifier and 0.979 for the other retinal diseases classifier. The relatively simple method for enhancing degraded-quality fundus images achieves superior image enhancement, as demonstrated in a qualitative HVS-based image quality assessment. This retinal image enhancement may, therefore, be employed to assist ophthalmologists in more efficient screening of retinal diseases and the development of computer-aided diagnosis. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

  11. Essentials in the diagnosis of acid-base disorders and their high altitude application.

    Science.gov (United States)

    Paulev, P E; Zubieta-Calleja, G R

    2005-09-01

    This report describes the historical development in the clinical application of chemical variables for the interpretation of acid-base disturbances. The pH concept was already introduced in 1909. Following World War II, disagreements concerning the definition of acids and bases occurred, and since then two strategies have been competing. Danish scientists in 1923 defined an acid as a substance able to give off a proton at a given pH, and a base as a substance that could bind a proton, whereas the North American Singer-Hasting school in 1948 defined acids as strong non-buffer anions and bases as non-buffer cations. As a consequence of this last definition, electrolyte disturbances were mixed up with real acid-base disorders and the variable, strong ion difference (SID), was introduced as a measure of non-respiratory acid-base disturbances. However, the SID concept is only an empirical approximation. In contrast, the Astrup/Siggaard-Andersen school of scientists, using computer strategies and the Acid-base Chart, has made diagnosis of acid-base disorders possible at a glance on the Chart, when the data are considered in context with the clinical development. Siggaard-Andersen introduced Base Excess (BE) or Standard Base Excess (SBE) in the extracellular fluid volume (ECF), extended to include the red cell volume (eECF), as a measure of metabolic acid-base disturbances and recently replaced it by the term Concentration of Titratable Hydrogen Ion (ctH). These two concepts (SBE and ctH) represent the same concentration difference, but with opposite signs. Three charts modified from the Siggaard-Andersen Acid-Base Chart are presented for use at low, medium and high altitudes of 2500 m, 3500 m, and 4000 m, respectively. In this context, the authors suggest the use of Titratable Hydrogen Ion concentration Difference (THID) in the extended extracellular fluid volume, finding it efficient and better than any other determination of the metabolic component in acid-base

  12. [Status of diagnosis and treatment devices of acupuncture based on SooPAT and bibliometrics in China].

    Science.gov (United States)

    Bai, Lin; Ren, Yulan; Guo, Taipin; Chen, Lin; Zhou, Yumei; Feng, Shuwei; Li, Ji; Liang, Fanrong

    2016-11-12

    To perform a bibliometrics analysis on patent literature regarding diagnosis and treatment devices of acupuncture in China, aiming to provide references for the development of diagnosis and treatment devices of acupuncture. Based on SooPAT, a patent database, the patent literature regarding diagnosis and treatment devices of acupuncture in China was collected. With bibliometrics methods, the annual distribution of type, quantity, classification and content of diagnosis and treatment devices of acupuncture were analyzed. The number of acupuncture diagnosis and treatment devices reached its peak in 2012 and 2013 in China. The A61N in patent and utility model patent were the most, which were mainly related to electrotherapy, magnetic therapy, radioactive therapy and ultrasound therapy, etc. The main content was acupuncture treatment devices and meridian treatment devices. The 24-01 in design patent was the most, involving fixation devices used by doctors, hospitals and laboratories, etc. Currently the majority of diagnosis and treatment devices of acupuncture is therapeutic apparatus, while the acupuncture diagnosis devices are needed.

  13. An MRI-based diagnostic framework for early diagnosis of dyslexia

    International Nuclear Information System (INIS)

    El-Baz, A.; Casanova, M.; Mott, M.; Switala, A.; Gimel'farb, G.

    2008-01-01

    A computer-aided diagnosis (CAD) system for early diagnosis of dyslexia was developed and tested. Dyslexia can severely impair the learning abilities of children so improved diagnostic methods are needed. Neuropathological studies show abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We sought to develop an MRI-based macroscopic neuropathological correlate to the minicolumnopathy of dyslexia that relates to cortical connectivity: the gyral window. The brains of dyslexic patients often exhibit decreased gyrifications, so the thickness of gyral CWM for dyslexic subjects is greater than for normal subjects. We developed an MRI-based method for assessment of gyral CWM thickness with automated recognition of abnormal (e.g., dyslexic) brains. In vivo data was collected from 16 right-handed dyslexic men aged 18-40 years, and a group of 14 controls matched for gender, age, educational level, socioeconomic background, handedness and general intelligence. All the subjects were physically healthy and free of history of neurological diseases and head injury. Images were acquired with the same 1.5T MRI scanner (GE, Milwaukee, WI, USA) with voxel resolution 0.9375 x 0.9375 x 1.5 mm using a T1-weighted imaging sequence protocol. The ''ground truth'' diagnosis to evaluate the classification accuracy for each patient was given by the clinicians. The accuracy of diagnosis/classification of both the training and test subjects was evaluated using the Chi-square test at the three confidence levels - 85, 90 and 95% - in order to examine significant differences in the Levy distances. As expected, the 85% confidence level yielded the best results, the system correctly classified 16 out of 16 dyslexic subjects (a 100% accuracy) and 14 out of 14 control subjects (a 100% accuracy). At the 90% confidence level, 16 out of 16 dyslexic subjects were still classified correctly; however, only 13 out of 14 control subjects were correct, bringing the accuracy rate for the

  14. An MRI-based diagnostic framework for early diagnosis of dyslexia

    Energy Technology Data Exchange (ETDEWEB)

    El-Baz, A. [University of Louisville, Bioengineering Department, Louisville, KY (United States); Casanova, M.; Mott, M.; Switala, A. [University of Louisville, Department of Psychiatry and Behavioral Science, Louisville, KY (United States); Gimel' farb, G. [University of Auckland, Computer Science Department, Auckland (New Zealand)

    2008-09-15

    A computer-aided diagnosis (CAD) system for early diagnosis of dyslexia was developed and tested. Dyslexia can severely impair the learning abilities of children so improved diagnostic methods are needed. Neuropathological studies show abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We sought to develop an MRI-based macroscopic neuropathological correlate to the minicolumnopathy of dyslexia that relates to cortical connectivity: the gyral window. The brains of dyslexic patients often exhibit decreased gyrifications, so the thickness of gyral CWM for dyslexic subjects is greater than for normal subjects. We developed an MRI-based method for assessment of gyral CWM thickness with automated recognition of abnormal (e.g., dyslexic) brains. In vivo data was collected from 16 right-handed dyslexic men aged 18-40 years, and a group of 14 controls matched for gender, age, educational level, socioeconomic background, handedness and general intelligence. All the subjects were physically healthy and free of history of neurological diseases and head injury. Images were acquired with the same 1.5T MRI scanner (GE, Milwaukee, WI, USA) with voxel resolution 0.9375 x 0.9375 x 1.5 mm using a T1-weighted imaging sequence protocol. The ''ground truth'' diagnosis to evaluate the classification accuracy for each patient was given by the clinicians. The accuracy of diagnosis/classification of both the training and test subjects was evaluated using the Chi-square test at the three confidence levels - 85, 90 and 95% - in order to examine significant differences in the Levy distances. As expected, the 85% confidence level yielded the best results, the system correctly classified 16 out of 16 dyslexic subjects (a 100% accuracy) and 14 out of 14 control subjects (a 100% accuracy). At the 90% confidence level, 16 out of 16 dyslexic subjects were still classified correctly; however, only 13 out of 14 control subjects were correct, bringing the

  15. Usability evaluation of a web-based support system for people with a schizophrenia diagnosis.

    Science.gov (United States)

    van der Krieke, Lian; Emerencia, Ando C; Aiello, Marco; Sytema, Sjoerd

    2012-02-06

    Routine Outcome Monitoring (ROM) is a systematic way of assessing service users' health conditions for the purpose of better aiding their care. ROM consists of various measures used to assess a service user's physical, psychological, and social condition. While ROM is becoming increasingly important in the mental health care sector, one of its weaknesses is that ROM is not always sufficiently service user-oriented. First, clinicians tend to concentrate on those ROM results that provide information about clinical symptoms and functioning, whereas it has been suggested that a service user-oriented approach needs to focus on personal recovery. Second, service users have limited access to ROM results and they are often not equipped to interpret them. These problems need to be addressed, as access to resources and the opportunity to share decision making has been indicated as a prerequisite for service users to become a more equal partner in communication with their clinicians. Furthermore, shared decision making has been shown to improve the therapeutic alliance and to lead to better care. Our aim is to build a web-based support system which makes ROM results more accessible to service users and to provide them with more concrete and personalized information about their functioning (ie, symptoms, housing, social contacts) that they can use to discuss treatment options with their clinician. In this study, we will report on the usability of the web-based support system for service users with schizophrenia. First, we developed a prototype of a web-based support system in a multidisciplinary project team, including end-users. We then conducted a usability study of the support system consisting of (1) a heuristic evaluation, (2) a qualitative evaluation and (3) a quantitative evaluation. Fifteen service users with a schizophrenia diagnosis and four information and communication technology (ICT) experts participated in the study. The results show that people with a

  16. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics

    International Nuclear Information System (INIS)

    Chen, Zhicong; Wu, Lijun; Cheng, Shuying; Lin, Peijie; Wu, Yue; Lin, Wencheng

    2017-01-01

    Highlights: •An improved Simulink based modeling method is proposed for PV modules and arrays. •Key points of I-V curves and PV model parameters are used as the feature variables. •Kernel extreme learning machine (KELM) is explored for PV arrays fault diagnosis. •The parameters of KELM algorithm are optimized by the Nelder-Mead simplex method. •The optimized KELM fault diagnosis model achieves high accuracy and reliability. -- Abstract: Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are

  17. Dexamethasone Modifies Cystatin C-Based Diagnosis of Acute Kidney Injury During Cisplatin-Based Chemotherapy

    Directory of Open Access Journals (Sweden)

    Timothy J. Pianta

    2017-03-01

    Full Text Available Background/Aims: Plasma cystatin C (pCysC may be superior to serum creatinine (sCr as a surrogate of GFR. However, the performance of pCysC for diagnosing acute kidney injury (AKI after cisplatin-based chemotherapy is potentially affected by accompanying corticosteroid anti-emetic therapy and hydration. Methods: In a prospective observational study pCysC, sCr, urinary kidney injury molecule-1 (KIM-1, and urinary clusterin were measured over 2 weeks in 27 patients given first-cycle chemotherapy. The same variables were measured over 2 weeks in Sprague–Dawley rats given a single intraperitoneal injection of dexamethasone, cisplatin, or both, and in controls. Results: In patients, pCysC increases were greater than sCr 41% vs. 16%, mean paired difference 25% (95% CI: 16–34%], relative increases were ≥ 50% in 9 patients (35% for pCysC compared with 2 (8% for sCr (p = 0.04 and increases in sCr were accompanied by increased KIM-1 and clusterin excretion, but increases in pCysC alone were not. In rats, dexamethasone administration produced dose-dependent increases in pCysC (and augmented cisplatin-induced increases in pCysC, but did not augment histological injury, increases in sCr, or KIM-1 and clusterin excretion. Conclusions: In the presence of dexamethasone, elevation of pCysC does not reliably diagnose AKI after cisplatin-based chemotherapy.

  18. Development of A MEMS Based Manometric Catheter for Diagnosis of Functional Swallowing Disorders

    International Nuclear Information System (INIS)

    Hsu, H Y; Hariz, A J; Omari, T; Teng, M F; Sii, D; Chan, S; Lau, L; Tan, S; Lin, G; Haskard, M; Mulcahy, D; Bakewell, M

    2006-01-01

    Silicon pressure sensors based on micro-electro-mechanical-systems (MEMS) technologies are gaining popularity for applications in bio-medical devices. In this study, a silicon piezo-resistive pressure sensor die is used in a feasibility study of developing a manometric catheter for functional swallowing disorders diagnosis. The function of a manometric catheter is to measure the peak and intrabolus pressures along the esophageal segment during the swallowing action. Previous manometric catheters used the water perfusion technique to measure the pressure changes. This type of catheter is reusable, large in size and the pressure reading is recorded by an external transducer. Current manometric catheters use a solid state pressure sensor on the catheter itself to measure the pressure changes. This type of catheter reduces the discomfort to the patient but it is reusable and is very expensive. We carried out several studies and experiments on the MEMS-based pressure sensor die, and the results show the MEMS-based pressure sensors have a good stability and a good linearity output response, together with the advantage of low excitation biasing voltage and extremely small size. The MEMS-based sensor is the best device to use in the new generation of manometric catheters. The concept of the new MEMS-based manometric catheter consists of a pressure sensing sensor, supporting ring, the catheter tube and a data connector. Laboratory testing shows that the new calibrated catheter is capable of measuring pressure in the range from 0 to 100mmHg and maintaining stable condition on the zero baseline setting when no pressure is applied. In-vivo tests are carried out to compare the new MEMS based catheter with the current version of catheters used in the hospital

  19. Extranodal Non-Hodgkin's Lymphoma of Base of Tongue – Diagnosis by Fine Needle Aspiration Cytology

    Directory of Open Access Journals (Sweden)

    Jaya Manchanda

    2016-01-01

    Full Text Available Waldeyer's ring is the primary site of Non-Hodgkin's Lymphoma (NHL involvement in approximately 5 to 10% of all lymphoma patients. Of all Waldeyer's ring NHLs, the tonsil is the most frequent site,followed by the nasopharynx. Lymphomas arising from base of the tongue are less frequent, accounting for 7% of all primary Waldeyer's ring NHLs. The possible differential diagnosisincludes Squamous Cell Carcinoma (SCC, which is the most common malignancy of the tongue base, salivary gland malignancy, (adenoid cystic carcinoma or mucoepidermoidcarcinoma and infection processes, such as tuberculosis. Here we present a case of 43 year old male presenting with mass lesion of the base of tongue and odynophagia. The diagnosis was initially made by ne needle aspiration of this lesion. Subsequent imaging investigations revealed a lobulated mass inltrating bowel loop in the right iliac fossa. Histopathological and immunohistochemistry tests for both lesions conrmed extra-nodal, primary NHL Bcell diffuse, large cell type.

  20. Electromechanical impedance-based health diagnosis for tendon and anchorage zone in a nuclear containment structure

    Science.gov (United States)

    Min, Jiyoung; Shim, Hyojin; Yun, Chung-Bang

    2012-04-01

    For a nuclear containment structure, the structural health monitoring is essential because of its high potential risk and grave social impact. In particular, the tendon and anchorage zone are to be monitored because they are under high tensile or compressive stress. In this paper, a method to monitor the tendon force and the condition of the anchorage zone is presented by using the impedance-based health diagnosis system. First, numerical simulations were conducted for cases with various loose tensile forces on the tendon as well as damages on the bearing plate and concrete structure. Then, experimental studies were carried out on a scaled model of the anchorage system. The relationship between the loose tensile force and the impedance-based damage index was analyzed by a regression analysis. When a structure gets damaged, the damage index increases so that the status of damage can be identified. The results of the numerical and experimental studies indicate a big potential of the proposed impedance-based method for monitoring the tendon and anchorage system.

  1. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

    Science.gov (United States)

    Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

    2016-03-01

    As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.

  2. Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

    CERN Document Server

    Ding, Steven X

    2013-01-01

    Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: ·         new material on fault isolation and identification, and fault detection in feedback control loops; ·         extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and ·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...

  3. A novel local learning based approach with application to breast cancer diagnosis

    Science.gov (United States)

    Xu, Songhua; Tourassi, Georgia

    2012-03-01

    In this paper, we introduce a new local learning based approach and apply it for the well-studied problem of breast cancer diagnosis using BIRADS-based mammographic features. To learn from our clinical dataset the latent relationship between these features and the breast biopsy result, our method first dynamically partitions the whole sample population into multiple sub-population groups through stochastically searching the sample population clustering space. Each encountered clustering scheme in our online searching process is then used to create a certain sample population partition plan. For every resultant sub-population group identified according to a partition plan, our method then trains a dedicated local learner to capture the underlying data relationship. In our study, we adopt the linear logistic regression model as our local learning method's base learner. Such a choice is made both due to the well-understood linear nature of the problem, which is compellingly revealed by a rich body of prior studies, and the computational efficiency of linear logistic regression--the latter feature allows our local learning method to more effectively perform its search in the sample population clustering space. Using a database of 850 biopsy-proven cases, we compared the performance of our method with a large collection of publicly available state-of-the-art machine learning methods and successfully demonstrated its performance advantage with statistical significance.

  4. A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

    Directory of Open Access Journals (Sweden)

    HungLinh Ao

    2014-01-01

    Full Text Available This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs. Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

  5. Automatic detection of measurement points for non-contact vibrometer-based diagnosis of cardiac arrhythmias

    Science.gov (United States)

    Metzler, Jürgen; Kroschel, Kristian; Willersinn, Dieter

    2017-03-01

    Monitoring of the heart rhythm is the cornerstone of the diagnosis of cardiac arrhythmias. It is done by means of electrocardiography which relies on electrodes attached to the skin of the patient. We present a new system approach based on the so-called vibrocardiogram that allows an automatic non-contact registration of the heart rhythm. Because of the contactless principle, the technique offers potential application advantages in medical fields like emergency medicine (burn patient) or premature baby care where adhesive electrodes are not easily applicable. A laser-based, mobile, contactless vibrometer for on-site diagnostics that works with the principle of laser Doppler vibrometry allows the acquisition of vital functions in form of a vibrocardiogram. Preliminary clinical studies at the Klinikum Karlsruhe have shown that the region around the carotid artery and the chest region are appropriate therefore. However, the challenge is to find a suitable measurement point in these parts of the body that differs from person to person due to e. g. physiological properties of the skin. Therefore, we propose a new Microsoft Kinect-based approach. When a suitable measurement area on the appropriate parts of the body are detected by processing the Kinect data, the vibrometer is automatically aligned on an initial location within this area. Then, vibrocardiograms on different locations within this area are successively acquired until a sufficient measuring quality is achieved. This optimal location is found by exploiting the autocorrelation function.

  6. Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

    NARCIS (Netherlands)

    E. Schwartz (Emanuel); R. Izmailov (Rauf); M. Spain (Michael); A. Barnes (Anthony); J.P. Mapes (James); P.C. Guest (Paul); H. Rahmoune (Hassan); S. Pietsch (Sandra); F.M. Leweke (Marcus); M. Rothermundt (Matthias); J. Steiner (Johann); D. Koethe (Dagmar); L. Kranaster (Laura); P. Ohrmann (Patricia); T. Suslow (Thomas); Y. Levin (Yishai); B. Bogerts (Bernhard); N.J.M. van Beveren (Nico); G. McAllister (George); N. Weber (Natalya); D. Niebuhr (David); D. Cowan (David); R.H. Yolken (Robert); S. Bahn (Sabine)

    2010-01-01

    textabstractAbstract: We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51

  7. [Overcoming the limitations of the descriptive and categorical approaches in psychiatric diagnosis: a proposal based on Bayesian networks].

    Science.gov (United States)

    Sorias, Soli

    2015-01-01

    Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.

  8. Privacy-Preserving Self-Helped Medical Diagnosis Scheme Based on Secure Two-Party Computation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yi Sun

    2014-01-01

    Full Text Available With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients’ health information and doctors’ diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions.

  9. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  10. A qualitative signature for early diagnosis of hepatocellular carcinoma based on relative expression orderings.

    Science.gov (United States)

    Ao, Lu; Zhang, Zimei; Guan, Qingzhou; Guo, Yating; Guo, You; Zhang, Jiahui; Lv, Xingwei; Huang, Haiyan; Zhang, Huarong; Wang, Xianlong; Guo, Zheng

    2018-04-23

    Currently, using biopsy specimens to confirm suspicious liver lesions of early hepatocellular carcinoma are not entirely reliable because of insufficient sampling amount and inaccurate sampling location. It is necessary to develop a signature to aid early hepatocellular carcinoma diagnosis using biopsy specimens even when the sampling location is inaccurate. Based on the within-sample relative expression orderings of gene pairs, we identified a simple qualitative signature to distinguish both hepatocellular carcinoma and adjacent non-tumour tissues from cirrhosis tissues of non-hepatocellular carcinoma patients. A signature consisting of 19 gene pairs was identified in the training data sets and validated in 2 large collections of samples from biopsy and surgical resection specimens. For biopsy specimens, 95.7% of 141 hepatocellular carcinoma tissues and all (100%) of 108 cirrhosis tissues of non-hepatocellular carcinoma patients were correctly classified. Especially, all (100%) of 60 hepatocellular carcinoma adjacent normal tissues and 77.5% of 80 hepatocellular carcinoma adjacent cirrhosis tissues were classified to hepatocellular carcinoma. For surgical resection specimens, 99.7% of 733 hepatocellular carcinoma specimens were correctly classified to hepatocellular carcinoma, while 96.1% of 254 hepatocellular carcinoma adjacent cirrhosis tissues and 95.9% of 538 hepatocellular carcinoma adjacent normal tissues were classified to hepatocellular carcinoma. In contrast, 17.0% of 47 cirrhosis from non-hepatocellular carcinoma patients waiting for liver transplantation were classified to hepatocellular carcinoma, indicating that some patients with long-lasting cirrhosis could have already gained hepatocellular carcinoma characteristics. The signature can distinguish both hepatocellular carcinoma tissues and tumour-adjacent tissues from cirrhosis tissues of non-hepatocellular carcinoma patients even using inaccurately sampled biopsy specimens, which can aid early

  11. Frequency and predictors of psychological distress after a diagnosis of epilepsy: A community-based study.

    Science.gov (United States)

    Xu, Ying; Hackett, Maree L; Glozier, Nick; Nikpour, Armin; Bleasel, Andrew; Somerville, Ernest; Lawson, John; Jan, Stephen; Hyde, Lorne; Todd, Lisa; Martiniuk, Alexandra; Ireland, Carol; Anderson, Craig S

    2017-10-01

    The objective of the study was to determine the frequency and predictors of psychological distress after a diagnosis of epilepsy. The Sydney Epilepsy Incidence Study to Measure Illness Consequences (SEISMIC) was a prospective, multicenter, community-based study of people of all ages with newly diagnosed epilepsy in Sydney, Australia. Analyses involved multivariate logistic regression and multinomial logit regression to identify predictors of psychological distress, assessed using the Hospital Anxiety and Depression Scale (HADS) and the Strengths and Difficulties Questionnaire (SDQ), as part of structured interviews. Psychological distress occurred in 33% (95% confidence interval [CI] 26 to 40%) and 24% (95% CI 18 to 31%) of 180 adults at baseline and 12months, respectively, and 23% (95% CI 14 to 33%) of 77 children at both time points. Thirty adults and 7 children had distress at baseline who recovered at 12months, while 15 adults and 7 children had new onset of distress during this period. History of psychiatric or behavioral disorder (for adults, odds ratio [OR] 6.82, 95% CI 3.08 to 15.10; for children, OR 28.85, 95% CI 2.88 to 288.60) and higher psychosocial disability (adults, OR 1.17, 95% CI 1.07 to 1.27) or lower family functioning (children, OR 1.80, 95% CI 1.08 to 3.02) were associated with psychological distress (C statistics 0.80 and 0.78). Psychological distress is common and fluctuates in frequency after a diagnosis of epilepsy. Those with premorbid psychological, psychosocial, and family problems are at high risk of this adverse outcome. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Get the Diagnosis: an evidence-based medicine collaborative Wiki for diagnostic test accuracy.

    Science.gov (United States)

    Hammer, Mark M; Kohlberg, Gavriel D

    2017-04-01

    Despite widespread calls for its use, there are challenges to the implementation of evidence-based medicine (EBM) in clinical practice. In response to the challenges of finding timely, pertinent information on diagnostic test accuracy, we developed an online, crowd-sourced Wiki on diagnostic test accuracy called Get the Diagnosis (GTD, http://www.getthediagnosis.org). Since its launch in November 2008 till October 2015, GTD has accumulated information on 300 diagnoses, with 1617 total diagnostic entries. There are a total of 1097 unique diagnostic tests with a mean of 5.4 tests (range 0-38) per diagnosis. 73% of entries (1182 of 1617) have an associated sensitivity and specificity and 89% of entries (1432 of 1617) have associated peer-reviewed literature citations. Altogether, GTD contains 474 unique literature citations. For a sample of three diagnoses, the search precision (percentage of relevant results in the first 30 entries) in GTD was 100% as compared with a range of 13.3%-63.3% for PubMed and between 6.7% and 76.7% for Google Scholar. GTD offers a fast, precise and efficient way to look up diagnostic test accuracy. On three selected examples, GTD had a greater precision rate compared with PubMed and Google Scholar in identifying diagnostic test information. GTD is a free resource that complements other currently available resources. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  13. Reasoning based in cases applied to diagnosis of electric generators; Razonamiento basado en casos aplicado al diagnostico de generadores electricos

    Energy Technology Data Exchange (ETDEWEB)

    De la Torre Vega, H. Octavio; Garcia Tevillo, Arturo; Campuzano Martinez, Roberto [Instituto de Investigaciones Electricas, Temixco, Morelos (Mexico); Lopez Azamar, Ernesto [Comision Federal de Electricidad (Mexico)

    2000-07-01

    The development of a system for the diagnosis of electrical generators that apply techniques of artificial intelligence, is presented, as it is the reasoning based on cases, to support the work of the diagnosis engineer. This system is part of a system called CADIS, dedicated to the diagnosis of electrical generators out of line and reason of previous articles. In this occasion the characteristics of the reasoning module based on experiences (SirBE) are emphasized, indicating how to make a diagnosis using similar cases and how to edit the system base of experience, using the interactive editor of cases. It is included, in addition, a summarized example which represents a case for SirBE and how the system helps to make a diagnosis. [Spanish] Se presenta el desarrollo de un sistema de diagnostico de generadores electricos que aplica tecnicas de inteligencia artificial, como es el razonamiento basado en casos, para apoyar la labor del ingeniero de diagnostico. Este sistema es parte de un sistema denominado CADIS, dedicado al diagnostico de generadores electricos fuera de linea y motivo de articulos anteriores. En esta ocasion se resaltan las caracteristicas del modulo de razonamiento basado en experiencias (SirBE), indicando como realizar un diagnostico utilizando casos similares y como editar la base de experiencia del sistema utilizando el editor interactivo de casos. Se incluye, ademas, un ejemplo resumido de lo que representa un caso para SiRBE y como el sistema ayuda a realizar un diagnostico.

  14. A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis.

    Science.gov (United States)

    El-Sappagh, Shaker; Elmogy, Mohammed; Riad, A M

    2015-11-01

    Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Cost-sensitive case-based reasoning using a genetic algorithm: application to medical diagnosis.

    Science.gov (United States)

    Park, Yoon-Joo; Chun, Se-Hak; Kim, Byung-Chun

    2011-02-01

    The paper studies the new learning technique called cost-sensitive case-based reasoning (CSCBR) incorporating unequal misclassification cost into CBR model. Conventional CBR is now considered as a suitable technique for diagnosis, prognosis and prescription in medicine. However it lacks the ability to reflect asymmetric misclassification and often assumes that the cost of a positive diagnosis (an illness) as a negative one (no illness) is the same with that of the opposite situation. Thus, the objective of this research is to overcome the limitation of conventional CBR and encourage applying CBR to many real world medical cases associated with costs of asymmetric misclassification errors. The main idea involves adjusting the optimal cut-off classification point for classifying the absence or presence of diseases and the cut-off distance point for selecting optimal neighbors within search spaces based on similarity distribution. These steps are dynamically adapted to new target cases using a genetic algorithm. We apply this proposed method to five real medical datasets and compare the results with two other cost-sensitive learning methods-C5.0 and CART. Our finding shows that the total misclassification cost of CSCBR is lower than other cost-sensitive methods in many cases. Even though the genetic algorithm has limitations in terms of unstable results and over-fitting training data, CSCBR results with GA are better overall than those of other methods. Also the paired t-test results indicate that the total misclassification cost of CSCBR is significantly less than C5.0 and CART for several datasets. We have proposed a new CBR method called cost-sensitive case-based reasoning (CSCBR) that can incorporate unequal misclassification costs into CBR and optimize the number of neighbors dynamically using a genetic algorithm. It is meaningful not only for introducing the concept of cost-sensitive learning to CBR, but also for encouraging the use of CBR in the medical area

  16. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    Directory of Open Access Journals (Sweden)

    Weiying Wang

    2014-01-01

    Full Text Available Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  17. Cognitive evaluation for the diagnosis of Alzheimer's disease based on Turing Test and Virtual Environments.

    Science.gov (United States)

    Fernandez Montenegro, Juan Manuel; Argyriou, Vasileios

    2017-05-01

    Alzheimer's screening tests are commonly used by doctors to diagnose the patient's condition and stage as early as possible. Most of these tests are based on pen-paper interaction and do not embrace the advantages provided by new technologies. This paper proposes novel Alzheimer's screening tests based on virtual environments and game principles using new immersive technologies combined with advanced Human Computer Interaction (HCI) systems. These new tests are focused on the immersion of the patient in a virtual room, in order to mislead and deceive the patient's mind. In addition, we propose two novel variations of Turing Test proposed by Alan Turing as a method to detect dementia. As a result, four tests are introduced demonstrating the wide range of screening mechanisms that could be designed using virtual environments and game concepts. The proposed tests are focused on the evaluation of memory loss related to common objects, recent conversations and events; the diagnosis of problems in expressing and understanding language; the ability to recognize abnormalities; and to differentiate between virtual worlds and reality, or humans and machines. The proposed screening tests were evaluated and tested using both patients and healthy adults in a comparative study with state-of-the-art Alzheimer's screening tests. The results show the capacity of the new tests to distinguish healthy people from Alzheimer's patients. Copyright © 2017. Published by Elsevier Inc.

  18. Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

    Directory of Open Access Journals (Sweden)

    Moshen Kuai

    2018-03-01

    Full Text Available For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN Adaptive Neuro-fuzzy Inference System (ANFIS in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

  19. A pre-trained convolutional neural network based method for thyroid nodule diagnosis.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing

    2017-01-01

    In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. Computer-aided diagnosis of mammographic masses using geometric verification-based image retrieval

    Science.gov (United States)

    Li, Qingliang; Shi, Weili; Yang, Huamin; Zhang, Huimao; Li, Guoxin; Chen, Tao; Mori, Kensaku; Jiang, Zhengang

    2017-03-01

    Computer-Aided Diagnosis of masses in mammograms is an important indicator of breast cancer. The use of retrieval systems in breast examination is increasing gradually. In this respect, the method of exploiting the vocabulary tree framework and the inverted file in the mammographic masse retrieval have been proved high accuracy and excellent scalability. However it just considered the features in each image as a visual word and had ignored the spatial configurations of features. It greatly affect the retrieval performance. To overcome this drawback, we introduce the geometric verification method to retrieval in mammographic masses. First of all, we obtain corresponding match features based on the vocabulary tree framework and the inverted file. After that, we grasps the main point of local similarity characteristic of deformations in the local regions by constructing the circle regions of corresponding pairs. Meanwhile we segment the circle to express the geometric relationship of local matches in the area and generate the spatial encoding strictly. Finally we judge whether the matched features are correct or not, based on verifying the all spatial encoding are whether satisfied the geometric consistency. Experiments show the promising results of our approach.

  1. Do diagnosis-related group-based payments incentivise hospitals to adjust output mix?

    Science.gov (United States)

    Liang, Li-Lin

    2015-04-01

    This study investigates whether the diagnosis-related group (DRG)-based payment method motivates hospitals to adjust output mix in order to maximise profits. The hypothesis is that when there is an increase in profitability of a DRG, hospitals will increase the proportion of that DRG (own-price effects) and decrease those of other DRGs (cross-price effects), except in cases where there are scope economies in producing two different DRGs. This conjecture is tested in the context of the case payment scheme (CPS) under Taiwan's National Health Insurance programme over the period of July 1999 to December 2004. To tackle endogeneity of DRG profitability and treatment policy, a fixed-effects three-stage least squares method is applied. The results support the hypothesised own-price and cross-price effects, showing that DRGs which share similar resources appear to be complements rather substitutes. For-profit hospitals do not appear to be more responsive to DRG profitability, possibly because of their institutional characteristics and bonds with local communities. The key conclusion is that DRG-based payments will encourage a type of 'product-range' specialisation, which may improve hospital efficiency in the long run. However, further research is needed on how changes in output mix impact patient access and pay-outs of health insurance. Copyright © 2014 John Wiley & Sons, Ltd.

  2. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    Science.gov (United States)

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  3. Traffic fatality indicators in Brazil: State diagnosis based on data envelopment analysis research.

    Science.gov (United States)

    Bastos, Jorge Tiago; Shen, Yongjun; Hermans, Elke; Brijs, Tom; Wets, Geert; Ferraz, Antonio Clóvis Pinto

    2015-08-01

    The intense economic growth experienced by Brazil in recent decades and its consequent explosive motorization process have evidenced an undesirable impact: the increasing and unbroken trend in traffic fatality numbers. In order to contribute to road safety diagnosis on a national level, this study presents a research into two main indicators available in Brazil: mortality rate (represented by fatalities per capita) and fatality rate (represented by two sub-indicators, i.e., fatalities per vehicle and fatalities per vehicle kilometer traveled). These indicators were aggregated into a composite indicator or index through a multiple layer data envelopment analysis (DEA) composite indicator model, which looks for the optimum combination of indicators' weights for each decision-making unit, in this case 27 Brazilian states. The index score represents the road safety performance, based on which a ranking of states can be made. Since such a model has never been applied for road safety evaluation in Brazil, its parameters were calibrated based on the experience of more consolidated European Union research in ranking its member countries using DEA techniques. Secondly, cluster analysis was conducted aiming to provide more realistic performance comparisons and, finally, the sensitivity of the results was measured through a bootstrapping method application. It can be concluded that by combining fatality indicators, defining clusters and applying bootstrapping procedures a trustworthy ranking can be created, which is valuable for nationwide road safety planning. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

    Science.gov (United States)

    Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim

    2017-03-01

    Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.

  5. Diagnosis and microecological characteristics of aerobic vaginitis in outpatients based on preformed enzymes.

    Science.gov (United States)

    Wang, Zhi-Liang; Fu, Lan-Yong; Xiong, Zheng-Ai; Qin, Qin; Yu, Teng-Hua; Wu, Yu-Tong; Hua, Yuan-Yuan; Zhang, Yong-Hong

    2016-02-01

    Aerobic vaginitis (AV) is a recently proposed term for genital tract infection in women. The diagnosis of AV is mainly based on descriptive diagnostic criteria proposed by Donders and co-workers. The objective of this study is to report AV prevalence in southwest China using an objective assay kit based on preformed enzymes and also to determine its characteristics. A total of 1948 outpatients were enrolled and tested by a commercial diagnostic kit to investigate the AV prevalence and characteristics in southwestern China. The study mainly examined the vaginal ecosystem, age distribution, Lactobacillus amount, and changes in pH. Differences within groups were analyzed by Wilcoxon two-sample test. The AV detection rate is 15.40%. The AV patients were usually seen in the sexually active age group of 20-30 years, followed by those in the age group of 30-40 years. The vaginal ecosystems of all the patients studied were absolutely abnormal, and diagnosed to have a combined infection [aerobic vaginitis (AV) + bacterial vaginitis (BV) 61.33%; 184/300]. Aerobic bacteria, especially Staphylococcus aureus and Escherichia coli, were predominantly found in the vaginal samples of these women. AV is a common type of genital infection in southwestern China and is characterized by sexually active age and combined infection predominated by the AV and BV type. Copyright © 2016. Published by Elsevier B.V.

  6. A ROC-based feature selection method for computer-aided detection and diagnosis

    Science.gov (United States)

    Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

    2014-03-01

    Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

  7. Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS.

    Science.gov (United States)

    Kuai, Moshen; Cheng, Gang; Pang, Yusong; Li, Yong

    2018-03-05

    For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

  8. Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest.

    Science.gov (United States)

    Ma, Suliang; Chen, Mingxuan; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

    2018-04-16

    Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

  9. Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

    Science.gov (United States)

    Wang, H.; Jing, X. J.

    2017-07-01

    This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

  10. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    Directory of Open Access Journals (Sweden)

    Mariela Cerrada

    2015-09-01

    Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

  11. Prescription of respiratory medication without an asthma diagnosis in children: a population based study.

    NARCIS (Netherlands)

    Zuidgeest, M.G.P.; Dijk, L. van; Smit, H.A.; Wouden, J.C. van der; Brunekreef, B.; Leufkens, H.G.M.; Bracke, M.

    2008-01-01

    BACKGROUND: In pre-school children a diagnosis of asthma is not easily made and only a minority of wheezing children will develop persistent atopic asthma. According to the general consensus a diagnosis of asthma becomes more certain with increasing age. Therefore the congruence between asthma

  12. Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Muhammad Sohaib

    2018-01-01

    Full Text Available Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing, but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE- based deep neural networks (DNNs, a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.

  13. Hydra: A web-based system for cardiovascular analysis, diagnosis and treatment.

    Science.gov (United States)

    Novo, J; Hermida, A; Ortega, M; Barreira, N; Penedo, M G; López, J E; Calvo, C

    2017-02-01

    Cardiovascular (CV) risk stratification is a highly complex process involving an extensive set of clinical trials to support the clinical decision-making process. There are many clinical conditions (e.g. diabetes, obesity, stress, etc.) that can lead to the early diagnosis or establishment of cardiovascular disease. In order to determine all these clinical conditions, a complete set of clinical patient analyses is typically performed, including a physical examination, blood analysis, electrocardiogram, blood pressure (BP) analysis, etc. This article presents a web-based system, called Hydra, which integrates a full and detailed set of services and functionalities for clinical decision support in order to help and improve the work of clinicians in cardiovascular patient diagnosis, risk assessment, treatment and monitoring over time. Hydra integrates a number of different services: a service for inputting all the information gathered by specialists (physical examination, habits, BP, blood analysis, electrocardiogram, etc.); a tool to automatically determine the CV risk stratification, including well-known standard risk stratification tables; and, finally, various tools to incorporate, analyze and graphically present the records of the ambulatory BP monitoring that provides BP analysis over a given period of time (24 or 48 hours). In addition, the platform presents a set of reports derived from all the information gathered from the patient in order to support physicians in their clinical decisions. Hydra was tested and validated in a real domain. In particular, internal medicine specialists at the Hypertension Unit of the Santiago de Compostela University Hospital (CHUS) validated the platform and used it in different clinical studies to demonstrate its utility. It was observed that the platform increased productivity and accuracy in the assessment of patient data yielding a cost reduction in clinical practice. This paper proposes a complete platform that includes

  14. Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

    Science.gov (United States)

    Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

    2017-08-20

    Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93

  15. Cloning of the koi herpesvirus (KHV gene encoding thymidine kinase and its use for a highly sensitive PCR based diagnosis

    Directory of Open Access Journals (Sweden)

    Gilad Oren

    2005-03-01

    Full Text Available Abstract Background Outbreaks with mass mortality among common carp Cyprinus carpio carpio and koi Cyprinus carpio koi have occurred worldwide since 1998. The herpes-like virus isolated from diseased fish is different from Herpesvirus cyprini and channel catfish virus and was accordingly designated koi herpesvirus (KHV. Diagnosis of KHV infection based on viral isolation and current PCR assays has a limited sensitivity and therefore new tools for the diagnosis of KHV infections are necessary. Results A robust and sensitive PCR assay based on a defined gene sequence of KHV was developed to improve the diagnosis of KHV infection. From a KHV genomic library, a hypothetical thymidine kinase gene (TK was identified, subcloned and expressed as a recombinant protein. Preliminary characterization of the recombinant TK showed that it has a kinase activity using dTTP but not dCTP as a substrate. A PCR assay based on primers selected from the defined DNA sequence of the TK gene was developed and resulted in a 409 bp amplified fragment. The TK based PCR assay did not amplify the DNAs of other fish herpesviruses such as Herpesvirus cyprini (CHV and the channel catfish virus (CCV. The TK based PCR assay was specific for the detection of KHV and was able to detect as little as 10 fentograms of KHV DNA corresponding to 30 virions. The TK based PCR was compared to previously described PCR assays and to viral culture in diseased fish and was shown to be the most sensitive method of diagnosis of KHV infection. Conclusion The TK based PCR assay developed in this work was shown to be specific for the detection of KHV. The TK based PCR assay was more sensitive for the detection of KHV than previously described PCR assays; it was as sensitive as virus isolation which is the golden standard method for KHV diagnosis and was able to detect as little as 10 fentograms of KHV DNA corresponding to 30 virions.

  16. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks

    Science.gov (United States)

    Le, Minh Hung; Chen, Jingyu; Wang, Liang; Wang, Zhiwei; Liu, Wenyu; (Tim Cheng, Kwang-Ting; Yang, Xin

    2017-08-01

    Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to ‘see’ the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our

  17. Diagnosis of Short-Circuit Fault in Large-Scale Permanent-Magnet Wind Power Generator Based on CMAC

    Directory of Open Access Journals (Sweden)

    Chin-Tsung Hsieh

    2013-01-01

    Full Text Available This study proposes a method based on the cerebellar model arithmetic controller (CMAC for fault diagnosis of large-scale permanent-magnet wind power generators and compares the results with Error Back Propagation (EBP. The diagnosis is based on the short-circuit faults in permanent-magnet wind power generators, magnetic field change, and temperature change. Since CMAC is characterized by inductive ability, associative ability, quick response, and similar input signals exciting similar memories, it has an excellent effect as an intelligent fault diagnosis implement. The experimental results suggest that faults can be diagnosed effectively after only training CMAC 10 times. In comparison to training 151 times for EBP, CMAC is better than EBP in terms of training speed.

  18. PCR-based techniques for leprosy diagnosis: from the laboratory to the clinic.

    Directory of Open Access Journals (Sweden)

    Alejandra Nóbrega Martinez

    2014-04-01

    Full Text Available In leprosy, classic diagnostic tools based on bacillary counts and histopathology have been facing hurdles, especially in distinguishing latent infection from active disease and diagnosing paucibacillary clinical forms. Serological tests and IFN-gamma releasing assays (IGRA that employ humoral and cellular immune parameters, respectively, are also being used, but recent results indicate that quantitative PCR (qPCR is a key technique due to its higher sensitivity and specificity. In fact, advances concerning the structure and function of the Mycobacterium leprae genome led to the development of specific PCR-based gene amplification assays for leprosy diagnosis and monitoring of household contacts. Also, based on the validation of point-of-care technologies for M. tuberculosis DNA detection, it is clear that the same advantages of rapid DNA detection could be observed in respect to leprosy. So far, PCR has proven useful in the determination of transmission routes, M. leprae viability, and drug resistance in leprosy. However, PCR has been ascertained to be especially valuable in diagnosing difficult cases like pure neural leprosy (PNL, paucibacillary (PB, and patients with atypical clinical presentation and histopathological features compatible with leprosy. Also, the detection of M. leprae DNA in different samples of the household contacts of leprosy patients is very promising. Although a positive PCR result is not sufficient to establish a causal relationship with disease outcome, quantitation provided by qPCR is clearly capable of indicating increased risk of developing the disease and could alert clinicians to follow these contacts more closely or even define rules for chemoprophylaxis.

  19. Evidence- and consensus-based practice guidelines for the diagnosis of irritable bowel syndrome.

    Science.gov (United States)

    Fass, R; Longstreth, G F; Pimentel, M; Fullerton, S; Russak, S M; Chiou, C F; Reyes, E; Crane, P; Eisen, G; McCarberg, B; Ofman, J

    2001-09-24

    Irritable bowel syndrome (IBS) presents a significant diagnostic and management challenge for primary care practitioners. Improving the accuracy and timeliness of diagnosis may result in improved quality and efficiency of care. To systematically appraise the existing diagnostic criteria and combine the evidence with expert opinion to derive evidence- and consensus-based guidelines for a diagnostic approach to patients with suspected IBS. We performed a systematic literature review (January 1966-April 2000) of computerized bibliographic databases. Articles meeting explicit inclusion criteria for diagnostic studies in IBS were subjected to critical appraisal, which formed the basis of guideline statements presented to an expert panel. To develop a diagnostic algorithm, an expert panel of specialists and primary care physicians was used to fill in gaps in the literature. Consensus was developed using a modified Delphi technique. The systematic literature review identified only 13 published studies regarding the effectiveness of competing diagnostic approaches for IBS, the accuracy of diagnostic tests, and the internal validity of current diagnostic symptom criteria. Few studies met accepted methodological criteria. While symptom criteria have been validated, the utility of endoscopic and other diagnostic interventions remains unknown. An analysis of the literature, combined with consensus from experienced clinicians, resulted in the development of a diagnostic algorithm relevant to primary care that emphasizes a symptom-based diagnostic approach, refers patients with alarm symptoms to subspecialists, and reserves radiographic, endoscopic, and other tests for referral cases. The resulting algorithm highlights the reliance on symptom criteria and comprises a primary module, 3 submodules based on the predominant symptom pattern (constipation, diarrhea, and pain) and severity level, and a subspecialist referral module. The dearth of available evidence highlights the need

  20. Research on Fault Diagnosis of HTR-PM Based on Multilevel Flow Model

    International Nuclear Information System (INIS)

    Zhang Yong; Zhou Yangping

    2014-01-01

    In this paper, we focus on the application of Multilevel Flow Model (MFM) in the automatic real-time fault diagnosis of High Temperature Gas-cooled Reactor Pebble-bed Module (HTR-PM) accidents. In the MFM, the plant process is described abstractly in function level by mass, energy and information flows, which reveal the interaction between different components and capacitate the causal reasoning between functions according to the flow properties. Thus, in the abnormal status, a goal-function-component oriented fault diagnosis can be performed with the model at a very quick speed and abnormal alarms can be also precisely explained by the reasoning relationship of the model. By using MFM, a fault diagnosis model of HTR-PM plant is built, and the detailed process of fault diagnosis is also shown by the flowcharts. Due to lack of simulation data about HTR-PM, experiments are not conducted to evaluate the fault diagnosis performance, but analysis of algorithm feasibility and complexity shows that the diagnosis system will have a good ability to detect and diagnosis accidents timely. (author)