Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

Science.gov (United States)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Teleworks: a CSCW application for remote medical diagnosis support and teleconsultation.

Science.gov (United States)

Makris, L; Kamilatos, I; Kopsacheilis, E V; Strintzis, M G

1998-06-01

The present paper describes methods for the design of both synchronous and asynchronous computer-supported cooperative work (CSCW) procedures suitable for the medical application area and specifically for the purpose of medical teleconsultation and remote diagnosis support. The experimental implementation of a CSCW system built upon a PC/Windows platform is detailed as an example of a low-cost system suitable for adoption in a wide range of medical teleconsultation applications.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

Science.gov (United States)

Chin Neoh, Siew; Srisukkham, Worawut; Zhang, Li; Todryk, Stephen; Greystoke, Brigit; Peng Lim, Chee; Alamgir Hossain, Mohammed; Aslam, Nauman

2015-01-01

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method. PMID:26450665

A Diagnosis Support System for Abnormal Situations of Hanbit Units 3 and 4

International Nuclear Information System (INIS)

Kim, Yochan; Jung, Wondea

2013-01-01

In contrast with previous research, we separated the flowchart into a search phase of an AOP category and the phase of an AOP in order for the operators to informatively and efficiently find an AOP. Meanwhile, Kang et al. developed a technique to associate alarm response procedures from annunciated alarms and data related with their causes. The search engine in this system, however, associates complex abnormal situations with multiple alarms and considers multiple abnormal situations to be diagnosed. The developed system shows how some advanced digital functions can collaboratively enhance a human operator's cognition. We expect that improvements and integration of these kinds of functions into the instrument and control of an MCR will continue. When an abnormal situation occurs in a nuclear power plant, the operators in the main control room (MCR) diagnose the cause of the abnormal situation based on the occurring alarms. However, because there are many different alarms and abnormal operating procedures (AOPs) in an MCR, it is necessary to develop education techniques or diagnosis supporting tools for aiding operators to efficiently cope with abnormal situations. Owing to the recent development of new power plants and new human resources, the necessity of these techniques and tools has been magnified. There have been some efforts to support operators in diagnosing abnormal situations from annunciated alarms. This paper introduces an integrated system that not only educates operators but also aids operators in searching AOPs under actual situations. For the purpose of education, this system provides flowcharts to find an AOP from annunciated alarms and a mimic alarm window that displays annunciated alarms during a selected abnormal situation. For the purpose of aiding a real-time search, this system has a function that shows AOPs related to the inputted alarm data and calculates the similarity of the AOPs and the alarm data. The system was implemented by Livecode 6

A Diagnosis Support System for Abnormal Situations of Hanbit Units 3 and 4

Energy Technology Data Exchange (ETDEWEB)

Kim, Yochan; Jung, Wondea [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2013-10-15

In contrast with previous research, we separated the flowchart into a search phase of an AOP category and the phase of an AOP in order for the operators to informatively and efficiently find an AOP. Meanwhile, Kang et al. developed a technique to associate alarm response procedures from annunciated alarms and data related with their causes. The search engine in this system, however, associates complex abnormal situations with multiple alarms and considers multiple abnormal situations to be diagnosed. The developed system shows how some advanced digital functions can collaboratively enhance a human operator's cognition. We expect that improvements and integration of these kinds of functions into the instrument and control of an MCR will continue. When an abnormal situation occurs in a nuclear power plant, the operators in the main control room (MCR) diagnose the cause of the abnormal situation based on the occurring alarms. However, because there are many different alarms and abnormal operating procedures (AOPs) in an MCR, it is necessary to develop education techniques or diagnosis supporting tools for aiding operators to efficiently cope with abnormal situations. Owing to the recent development of new power plants and new human resources, the necessity of these techniques and tools has been magnified. There have been some efforts to support operators in diagnosing abnormal situations from annunciated alarms. This paper introduces an integrated system that not only educates operators but also aids operators in searching AOPs under actual situations. For the purpose of education, this system provides flowcharts to find an AOP from annunciated alarms and a mimic alarm window that displays annunciated alarms during a selected abnormal situation. For the purpose of aiding a real-time search, this system has a function that shows AOPs related to the inputted alarm data and calculates the similarity of the AOPs and the alarm data. The system was implemented by

Support Vector Machine Diagnosis of Acute Abdominal Pain

Science.gov (United States)

Björnsdotter, Malin; Nalin, Kajsa; Hansson, Lars-Erik; Malmgren, Helge

This study explores the feasibility of a decision-support system for patients seeking care for acute abdominal pain, and, specifically the diagnosis of acute diverticulitis. We used a linear support vector machine (SVM) to separate diverticulitis from all other reported cases of abdominal pain and from the important differential diagnosis non-specific abdominal pain (NSAP). On a database containing 3337 patients, the SVM obtained results comparable to those of the doctors in separating diverticulitis or NSAP from the remaining diseases. The distinction between diverticulitis and NSAP was, however, substantially improved by the SVM. For this patient group, the doctors achieved a sensitivity of 0.714 and a specificity of 0.963. When adjusted to the physicians' results, the SVM sensitivity/specificity was higher at 0.714/0.985 and 0.786/0.963 respectively. Age was found as the most important discriminative variable, closely followed by C-reactive protein level and lower left side pain.

Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care.

Science.gov (United States)

Belard, Arnaud; Buchman, Timothy; Forsberg, Jonathan; Potter, Benjamin K; Dente, Christopher J; Kirk, Allan; Elster, Eric

2017-04-01

Improving diagnosis and treatment depends on clinical monitoring and computing. Clinical decision support systems (CDSS) have been in existence for over 50 years. While the literature points to positive impacts on quality and patient safety, outcomes, and the avoidance of medical errors, technical and regulatory challenges continue to retard their rate of integration into clinical care processes and thus delay the refinement of diagnoses towards personalized care. We conducted a systematic review of pertinent articles in the MEDLINE, US Department of Health and Human Services, Agency for Health Research and Quality, and US Food and Drug Administration databases, using a Boolean approach to combine terms germane to the discussion (clinical decision support, tools, systems, critical care, trauma, outcome, cost savings, NSQIP, APACHE, SOFA, ICU, and diagnostics). References were selected on the basis of both temporal and thematic relevance, and subsequently aggregated around four distinct themes: the uses of CDSS in the critical and surgical care settings, clinical insertion challenges, utilization leading to cost-savings, and regulatory concerns. Precision diagnosis is the accurate and timely explanation of each patient's health problem and further requires communication of that explanation to patients and surrogate decision-makers. Both accuracy and timeliness are essential to critical care, yet computed decision support systems (CDSS) are scarce. The limitation arises from the technical complexity associated with integrating and filtering large data sets from diverse sources. Provider mistrust and resistance coupled with the absence of clear guidance from regulatory bodies further retard acceptance of CDSS. While challenges to develop and deploy CDSS are substantial, the clinical, quality, and economic impacts warrant the effort, especially in disciplines requiring complex decision-making, such as critical and surgical care. Improving diagnosis in health care

Support Tool in the Diagnosis of Major Depressive Disorder

Science.gov (United States)

Nunes, Luciano Comin; Pinheiro, Plácido Rogério; Pequeno, Tarcísio Cavalcante; Pinheiro, Mirian Calíope Dantas

Major Depressive Disorder have been responsible for millions of professionals temporary removal, and even permanent, from diverse fields of activities around the world, generating damage to social, financial, productive systems and social security, and especially damage to the image of the individual and his family that these disorders produce in individuals who are patients, characteristics that make them stigmatized and discriminated into their society, making difficult their return to the production system. The lack of early diagnosis has provided reactive and late measures, only when the professional suffering psychological disorder is already showing signs of incapacity for working and social relationships. This article aims to assist in the decision making to establish early diagnosis of these types of psychological disorders. It presents a proposal for a hybrid model composed of expert system structured methodologies for decision support (Multi-Criteria Decision Analysis - MCDA) and representations of knowledge structured in logical rules of production and probabilities (Artificial Intelligence - AI).

Flu Diagnosis System Using Jaccard Index and Rough Set Approaches

Science.gov (United States)

Efendi, Riswan; Azah Samsudin, Noor; Mat Deris, Mustafa; Guan Ting, Yip

2018-04-01

Jaccard index and rough set approaches have been frequently implemented in decision support systems with various domain applications. Both approaches are appropriate to be considered for categorical data analysis. This paper presents the applications of sets operations for flu diagnosis systems based on two different approaches, such as, Jaccard index and rough set. These two different approaches are established using set operations concept, namely intersection and subset. The step-by-step procedure is demonstrated from each approach in diagnosing flu system. The similarity and dissimilarity indexes between conditional symptoms and decision are measured using Jaccard approach. Additionally, the rough set is used to build decision support rules. Moreover, the decision support rules are established using redundant data analysis and elimination of unclassified elements. A number data sets is considered to attempt the step-by-step procedure from each approach. The result has shown that rough set can be used to support Jaccard approaches in establishing decision support rules. Additionally, Jaccard index is better approach for investigating the worst condition of patients. While, the definitely and possibly patients with or without flu can be determined using rough set approach. The rules may improve the performance of medical diagnosis systems. Therefore, inexperienced doctors and patients are easier in preliminary flu diagnosis.

Diagnosis aids with artificial intelligence in the PSAD system

International Nuclear Information System (INIS)

Dourgnon-Hanoune, A.; Porcheron, M.; Ricard, B.

1996-01-01

To improve monitoring and diagnosis capabilities in nuclear power plants, Electricite de France (EDF) has designed an integrated monitoring and diagnosis assistance system: PSAD - Poste de Surveillance et d'Aide au Diagnostic. The development of this sophisticated monitoring and data processing system requires the addition of analysis and diagnosis assistance capabilities. Diagnostic knowledge based systems have thus been added to the functions monitored in PSAD: DIVA for turbine generators, and DIAPO for reactor coolant pumps. These systems rely on a representation of the diagnostic reasoning process of experts and of supporting knowledge. Diagnosis in both systems is performed through an abductive reasoning process applied to component fault models and observations derived from their actual behavior, as provided by the monitoring functions. The basic theoretical elements of this diagnostic model are summarized in a first part of this paper. In a second part, DIVA and DIAPO specific elements are described. (authors)

E-chem page: A Support System for Remote Diagnosis of Water Quality in Boiling Water Reactors

International Nuclear Information System (INIS)

Naohiro Kusumi; Takayasu Kasahara; Kazuhiko Akamine; Kenji Tada; Naoshi Usui; Nobuyuki Oota

2002-01-01

It is important to control and maintain water quality for nuclear power plants. Chemical engineers sample and monitor reactor water from various subsystems and analyze the chemical quality as routine operations. With regard to controlling water quality, new technologies have been developed and introduced to improve the water quality from both operation and material viewpoints. To maintain the quality, it is important to support chemical engineers in evaluating the water quality and realizing effective retrieval of stored data and documents. We have developed a remote support system using the Internet to diagnose BWR water quality, which we call e-chem page. The e-chem page integrates distributed data and information in a Web server, and makes it easy to evaluate the data on BWR water chemistry. This system is composed of four functions: data transmission, water quality evaluation, inquiry and history retrieval system, and reference to documents on BWR water chemistry. The developed system is now being evaluated in trial operations by Hitachi, Ltd. and an electric power company. In addition diagnosis technology applying independent component analysis (ICA) is being developed to improve predictive capability of the system. This paper describes the structure and function of the e-chem page and presents results of obtained with the proposed system for the prediction of chemistry conditions in reactor water. (authors)

[Diagnosis and the technology for optimizing the medical support of a troop unit].

Science.gov (United States)

Korshever, N G; Polkovov, S V; Lavrinenko, O V; Krupnov, P A; Anastasov, K N

2000-05-01

The work is devoted to investigation of the system of military unit medical support with the use of principles and states of organizational diagnosis; development of the method allowing to assess its functional activity; and determination of optimization trends. Basing on the conducted organizational diagnosis and expert inquiry the informative criteria were determined which characterize the stages of functioning of the military unit medical support system. To evaluate the success of military unit medical support the complex multi-criteria pattern was developed and algorithm of this process optimization was substantiated. Using the results obtained, particularly realization of principles and states of decision taking theory in machine program it is possible to solve more complex problem of comparison between any number of military units: to dispose them according to priority decrease; to select the programmed number of the best and worst; to determine the trends of activity optimization in corresponding medical service personnel.

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

Milk fever and subclinical hypocalcaemia--an evaluation of parameters on incidence risk, diagnosis, risk factors and biological effects as input for a decision support system for disease control

DEFF Research Database (Denmark)

Houe, H; Østergaard, S; Thilsing-Hansen, T

2001-01-01

The present review analyses the documentation on incidence, diagnosis, risk factors and effects of milk fever and subclinical hypocalcaemia. It is hereby evaluated whether the existing documentation seems sufficient for further modelling in a decision support system for selection of a control...... concerning incidence, diagnosis, risk factors and effects seems sufficient for a systematic inclusion in a decision support system. A model on milk fever should take into consideration the variation in biological data and individual herd characteristics. The inclusion of subclinical hypocalcaemia would...... of risk factors is outlined. The clinical symptoms of milk fever are highly specific and the disease level may thus be determined from recording of treatments. Diagnosis of subclinical hypocalcaemia needs to include laboratory examinations or it may be determined by multiplying the incidence of milk fever...

Decision Support System In Heart Disease Diagnosis By Case Based Recommendation

Directory of Open Access Journals (Sweden)

Prinsha Prakash

2015-02-01

Full Text Available Abstract Heart disease is the main leading killer as well as a major cause of disability. Its timely detection and correct diagnosis plays a vital role in human life. In a limited period of time recalling the data from Doctors unaided memory may lead to wrong judgments. While taking decisions Doctor analyses the physical condition and test results of the patient. In the same way our system compares the data provided to Doctor and getting a result through CBR technique. Results from the system will help the Doctor to conclude the decision and reduce human errors may occur. Our system is able to analyze scanned results of heart and being a helping hand to the doctor in all manners.

Advanced intelligent computational technologies and decision support systems

CERN Document Server

Kountchev, Roumen

2014-01-01

This book offers a state of the art collection covering themes related to Advanced Intelligent Computational Technologies and Decision Support Systems which can be applied to fields like healthcare assisting the humans in solving problems. The book brings forward a wealth of ideas, algorithms and case studies in themes like: intelligent predictive diagnosis; intelligent analyzing of medical images; new format for coding of single and sequences of medical images; Medical Decision Support Systems; diagnosis of Down’s syndrome; computational perspectives for electronic fetal monitoring; efficient compression of CT Images; adaptive interpolation and halftoning for medical images; applications of artificial neural networks for real-life problems solving; present and perspectives for Electronic Healthcare Record Systems; adaptive approaches for noise reduction in sequences of CT images etc.

A fault diagnosis system for nuclear power plant operation

International Nuclear Information System (INIS)

Ohga, Yukiharu; Hayashi, Yoshiharu; Yuchi, Hiroyuki; Utena, Shunsuke; Maeda, Akihiko

2002-01-01

A fault diagnosis system has been developed to support operators in nuclear power plants. In the system various methods are combined to get a diagnosis result which provides better detection sensitivity and result reliability. The system is composed of an anomaly detection part with diagnosis modules, an integration part which obtains the diagnosis result by combining results from each diagnosis module, and a prediction part with state prediction and estimation modules. For the anomaly detection part, three kinds of modules are prepared: plant signal processing, early fault detection and event identification modules. The plant signal processing module uses wavelet transform and chaos technologies as well as fast Fourier transform (FFT) to analyze vibration sensor signals and to detect signal anomaly. The early fault detection module uses the neural network model of a plant subprocess to estimate the process variable values assuming normal conditions, and to detect an anomaly by comparing the measured and estimated values. The event identification module identifies the kind of occurring event by using the neural network and knowledge processing. In the integration part the diagnosis is performed by using knowledge processing. The knowledge for diagnosis is structured based on the means-ends abstraction hierarchy to simplify knowledge input and maintenance. In the prediction part, the prediction module predicts the future changes of process variables and plant interlock statuses and the estimation module estimates the values of unmeasurable variables. A prototype system has been developed and the system performance was evaluated. The evaluation results show that the developed technologies are effective to improve the human-machine system for plant operation. (author)

Design of Online Monitoring and Fault Diagnosis System for Belt Conveyors Based on Wavelet Packet Decomposition and Support Vector Machine

Directory of Open Access Journals (Sweden)

Wei Li

2013-01-01

Full Text Available Belt conveyors are the equipment widely used in coal mines and other manufacturing factories, whose main components are a number of idlers. The faults of belt conveyors can directly influence the daily production. In this paper, a fault diagnosis method combining wavelet packet decomposition (WPD and support vector machine (SVM is proposed for monitoring belt conveyors with the focus on the detection of idler faults. Since the number of the idlers could be large, one acceleration sensor is applied to gather the vibration signals of several idlers in order to reduce the number of sensors. The vibration signals are decomposed with WPD, and the energy of each frequency band is extracted as the feature. Then, the features are employed to train an SVM to realize the detection of idler faults. The proposed fault diagnosis method is firstly tested on a testbed, and then an online monitoring and fault diagnosis system is designed for belt conveyors. An experiment is also carried out on a belt conveyor in service, and it is verified that the proposed system can locate the position of the faulty idlers with a limited number of sensors, which is important for operating belt conveyors in practices.

Integrated ADIOS-IGENPRO operator advisory support system

International Nuclear Information System (INIS)

Lee, Dong Young; Park, J. H.; Kim, J. T.; Kim, C. H.; Park, W. M.; Hwang, I. K.; Cheon, S. W.; Song, S. J.

2001-05-01

The I and C systems and control rooms of nuclear power plants have been constructed by using the automatic control concept and changed to computer-based systems in nowadays. For Increase of an automation and CRT, the role of operators is changed to monitor the condition of the nuclear power plants. Therefore, the information that is offered to operators has to integrate in order for operator to understand the hole condition of plants. In commercial nuclear plants, raw data of sensors and components are shown in a control room. So, operators can not diagnose the condition of plants correctly. For a development of an integrated operator aid system which contain an alarm processing system and a fault diagnosis system, we integrated IGENPRO of ANL(Argonne National Lab.) and ADIOS of KAERI (Korea Atomic Energy Institute). IGENPRO is a fault diagnosis system contains three module such as PROTREN, PRODIAG and PROTREN. ADIOS is an alarm processing system that informs operators of important alarms. The integrated operator advisory support system developed in the research is composed of an alarm processing module and a fault diagnosis module. The alarm processing module shows important alarms to operator by using dynamic alarm filtering methods. The fault diagnosis module shows the cause of faults of sensors and hardwares

Integrated ADIOS-IGENPRO operator advisory support system

Energy Technology Data Exchange (ETDEWEB)

Lee, Dong Young; Park, J. H.; Kim, J. T.; Kim, C. H.; Park, W. M.; Hwang, I. K.; Cheon, S. W.; Song, S. J

2001-05-01

The I and C systems and control rooms of nuclear power plants have been constructed by using the automatic control concept and changed to computer-based systems in nowadays. For Increase of an automation and CRT, the role of operators is changed to monitor the condition of the nuclear power plants. Therefore, the information that is offered to operators has to integrate in order for operator to understand the hole condition of plants. In commercial nuclear plants, raw data of sensors and components are shown in a control room. So, operators can not diagnose the condition of plants correctly. For a development of an integrated operator aid system which contain an alarm processing system and a fault diagnosis system, we integrated IGENPRO of ANL(Argonne National Lab.) and ADIOS of KAERI (Korea Atomic Energy Institute). IGENPRO is a fault diagnosis system contains three module such as PROTREN, PRODIAG and PROTREN. ADIOS is an alarm processing system that informs operators of important alarms. The integrated operator advisory support system developed in the research is composed of an alarm processing module and a fault diagnosis module. The alarm processing module shows important alarms to operator by using dynamic alarm filtering methods. The fault diagnosis module shows the cause of faults of sensors and hardwares.

A User-Centered Cooperative Information System for Medical Imaging Diagnosis.

Science.gov (United States)

Gomez, Enrique J.; Quiles, Jose A.; Sanz, Marcos F.; del Pozo, Francisco

1998-01-01

Presents a cooperative information system for remote medical imaging diagnosis. General computer-supported cooperative work (CSCW) problems addressed are definition of a procedure for the design of user-centered cooperative systems (conceptual level); and improvement of user feedback and optimization of the communication bandwidth in highly…

Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis

Science.gov (United States)

Ong, James C.; Remolina, Emilio; Breeden, David; Stroozas, Brett A.; Mohammed, John L.

2012-01-01

Any reasoning system is fallible, so crew members and flight controllers must be able to cross-check automated diagnoses of spacecraft or habitat problems by considering alternate diagnoses and analyzing related evidence. Cross-checking improves diagnostic accuracy because people can apply information processing heuristics, pattern recognition techniques, and reasoning methods that the automated diagnostic system may not possess. Over time, cross-checking also enables crew members to become comfortable with how the diagnostic reasoning system performs, so the system can earn the crew s trust. We developed intelligent data visualization software that helps users cross-check automated diagnoses of system faults more effectively. The user interface displays scrollable arrays of timelines and time-series graphs, which are tightly integrated with an interactive, color-coded system schematic to show important spatial-temporal data patterns. Signal processing and rule-based diagnostic reasoning automatically identify alternate hypotheses and data patterns that support or rebut the original and alternate diagnoses. A color-coded matrix display summarizes the supporting or rebutting evidence for each diagnosis, and a drill-down capability enables crew members to quickly view graphs and timelines of the underlying data. This system demonstrates that modest amounts of diagnostic reasoning, combined with interactive, information-dense data visualizations, can accelerate system diagnosis and cross-checking.

Urinary proteomics to support diagnosis of stroke.

Directory of Open Access Journals (Sweden)

Jesse Dawson

Full Text Available Accurate diagnosis in suspected ischaemic stroke can be difficult. We explored the urinary proteome in patients with stroke (n = 69, compared to controls (n = 33, and developed a biomarker model for the diagnosis of stroke. We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Potentially disease-specific peptides were identified and a classifier based on these was generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. We developed two biomarker-based classifiers, employing 14 biomarkers (nominal p-value <0.004 or 35 biomarkers (nominal p-value <0.01. When tested on a blinded test set of 47 independent samples, the classification factor was significantly different between groups; for the 35 biomarker model, median value of the classifier was 0.49 (-0.30 to 1.25 in cases compared to -1.04 (IQR -1.86 to -0.09 in controls, p<0.001. The 35 biomarker classifier gave sensitivity of 56%, specificity was 93% and the AUC on ROC analysis was 0.86. This study supports the potential for urinary proteomic biomarker models to assist with the diagnosis of acute stroke in those with mild symptoms. We now plan to refine further and explore the clinical utility of such a test in large prospective clinical trials.

Approach to Health Supporting System Using Traditional Chinese Medicine

Science.gov (United States)

Watsuji, Tadashi; Shinohara, Shoji; Arita, Seizaburo

The primary prevention of disease related to the lifestyle is an essential theme in medical research. Preventing before it arises is the important concept in traditional Chinese medicine (TCM). Since TCM, which emphasizes individual physical condition in medical treatment, has recently attracted considerable attention globally, objective diagnostic methods in TCM have been investigated in this work. Firstly, the fuzzy theory was applied to develop a tongue diagnosis supporting system based on the tongue diagnosis in TCM. Secondly, the usefulness of TCM health questionnaire was examined to identify individual physical condition. Our results suggest that the TCM health questionnaire is useful in the construction of a health supporting system based on TCM.

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.

Operator support system using computational intelligence techniques

International Nuclear Information System (INIS)

Bueno, Elaine Inacio; Pereira, Iraci Martinez

2015-01-01

Computational Intelligence Systems have been widely applied in Monitoring and Fault Detection Systems in several processes and in different kinds of applications. These systems use interdependent components ordered in modules. It is a typical behavior of such systems to ensure early detection and diagnosis of faults. Monitoring and Fault Detection Techniques can be divided into two categories: estimative and pattern recognition methods. The estimative methods use a mathematical model, which describes the process behavior. The pattern recognition methods use a database to describe the process. In this work, an operator support system using Computational Intelligence Techniques was developed. This system will show the information obtained by different CI techniques in order to help operators to take decision in real time and guide them in the fault diagnosis before the normal alarm limits are reached. (author)

Operator support system using computational intelligence techniques

Energy Technology Data Exchange (ETDEWEB)

Bueno, Elaine Inacio, E-mail: ebueno@ifsp.edu.br [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Sao Paulo, SP (Brazil); Pereira, Iraci Martinez, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2015-07-01

Computational Intelligence Systems have been widely applied in Monitoring and Fault Detection Systems in several processes and in different kinds of applications. These systems use interdependent components ordered in modules. It is a typical behavior of such systems to ensure early detection and diagnosis of faults. Monitoring and Fault Detection Techniques can be divided into two categories: estimative and pattern recognition methods. The estimative methods use a mathematical model, which describes the process behavior. The pattern recognition methods use a database to describe the process. In this work, an operator support system using Computational Intelligence Techniques was developed. This system will show the information obtained by different CI techniques in order to help operators to take decision in real time and guide them in the fault diagnosis before the normal alarm limits are reached. (author)

Accident diagnosis system based on real-time decision tree expert system

Science.gov (United States)

Nicolau, Andressa dos S.; Augusto, João P. da S. C.; Schirru, Roberto

2017-06-01

Safety is one of the most studied topics when referring to power stations. For that reason, sensors and alarms develop an important role in environmental and human protection. When abnormal event happens, it triggers a chain of alarms that must be, somehow, checked by the control room operators. In this case, diagnosis support system can help operators to accurately identify the possible root-cause of the problem in short time. In this article, we present a computational model of a generic diagnose support system based on artificial intelligence, that was applied on the dataset of two real power stations: Angra1 Nuclear Power Plant and Santo Antônio Hydroelectric Plant. The proposed system processes all the information logged in the sequence of events before a shutdown signal using the expert's knowledge inputted into an expert system indicating the chain of events, from the shutdown signal to its root-cause. The results of both applications showed that the support system is a potential tool to help the control room operators identify abnormal events, as accidents and consequently increase the safety.

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.

A Knowledge-Based Expert System Using MFM Model for Operator Supporting

International Nuclear Information System (INIS)

Mo, Kun; Seong, Poong Hyun

2005-01-01

In this paper, a knowledge-based expert system using MFM (Multi-level Flow Modeling) is proposed for enhancing the operators' ability to cope with various situations in nuclear power plant. There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to help the operator to assess the situations promptly and accurately, and to regulate their operations according to these situations. it is necessary to develop an expert systems to help the operator for the fault diagnosis, alarm analysis, and operation results estimation for each operation. Many kinds of operator supporting systems focusing on different functions have been developed. Most of them used various methodologies for single diagnosis function or operation permission function. The proposed system integrated functions of fault diagnosis, alarm analysis and operation results estimation by the MFM basic algorithm for the operator supporting

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.

Automated Diagnosis and Control of Complex Systems

Science.gov (United States)

Kurien, James; Plaunt, Christian; Cannon, Howard; Shirley, Mark; Taylor, Will; Nayak, P.; Hudson, Benoit; Bachmann, Andrew; Brownston, Lee; Hayden, Sandra;

Design of a decision support system, trained on GPU, for assisting melanoma diagnosis in dermatoscopy images

Science.gov (United States)

Glotsos, Dimitris; Kostopoulos, Spiros; Lalissidou, Stella; Sidiropoulos, Konstantinos; Asvestas, Pantelis; Konstandinou, Christos; Xenogiannopoulos, George; Konstantina Nikolatou, Eirini; Perakis, Konstantinos; Bouras, Thanassis; Cavouras, Dionisis

2015-09-01

The purpose of this study was to design a decision support system for assisting the diagnosis of melanoma in dermatoscopy images. Clinical material comprised images of 44 dysplastic (clark's nevi) and 44 malignant melanoma lesions, obtained from the dermatology database Dermnet. Initially, images were processed for hair removal and background correction using the Dull Razor algorithm. Processed images were segmented to isolate moles from surrounding background, using a combination of level sets and an automated thresholding approach. Morphological (area, size, shape) and textural features (first and second order) were calculated from each one of the segmented moles. Extracted features were fed to a pattern recognition system assembled with the Probabilistic Neural Network Classifier, which was trained to distinguish between benign and malignant cases, using the exhaustive search and the leave one out method. The system was designed on the GPU card (GeForce 580GTX) using CUDA programming framework and C++ programming language. Results showed that the designed system discriminated benign from malignant moles with 88.6% accuracy employing morphological and textural features. The proposed system could be used for analysing moles depicted on smart phone images after appropriate training with smartphone images cases. This could assist towards early detection of melanoma cases, if suspicious moles were to be captured on smartphone by patients and be transferred to the physician together with an assessment of the mole's nature.

Diagnosis of multiple system atrophy.

Science.gov (United States)

Palma, Jose-Alberto; Norcliffe-Kaufmann, Lucy; Kaufmann, Horacio

2018-05-01

Multiple system atrophy (MSA) may be difficult to distinguish clinically from other disorders, particularly in the early stages of the disease. An autonomic-only presentation can be indistinguishable from pure autonomic failure. Patients presenting with parkinsonism may be misdiagnosed as having Parkinson disease. Patients presenting with the cerebellar phenotype of MSA can mimic other adult-onset ataxias due to alcohol, chemotherapeutic agents, lead, lithium, and toluene, or vitamin E deficiency, as well as paraneoplastic, autoimmune, or genetic ataxias. A careful medical history and meticulous neurological examination remain the cornerstone for the accurate diagnosis of MSA. Ancillary investigations are helpful to support the diagnosis, rule out potential mimics, and define therapeutic strategies. This review summarizes diagnostic investigations useful in the differential diagnosis of patients with suspected MSA. Currently used techniques include structural and functional brain imaging, cardiac sympathetic imaging, cardiovascular autonomic testing, olfactory testing, sleep study, urological evaluation, and dysphagia and cognitive assessments. Despite advances in the diagnostic tools for MSA in recent years and the availability of consensus criteria for clinical diagnosis, the diagnostic accuracy of MSA remains sub-optimal. As other diagnostic tools emerge, including skin biopsy, retinal biomarkers, blood and cerebrospinal fluid biomarkers, and advanced genetic testing, a more accurate and earlier recognition of MSA should be possible, even in the prodromal stages. This has important implications as misdiagnosis can result in inappropriate treatment, patient and family distress, and erroneous eligibility for clinical trials of disease-modifying drugs. Copyright © 2017 Elsevier B.V. All rights reserved.

Discrete event systems diagnosis and diagnosability

CERN Document Server

Sayed-Mouchaweh, Moamar

2014-01-01

Discrete Event Systems: Diagnosis and Diagnosability addresses the problem of fault diagnosis of Discrete Event Systems (DES). This book provides the basic techniques and approaches necessary for the design of an efficient fault diagnosis system for a wide range of modern engineering applications. The different techniques and approaches are classified according to several criteria such as: modeling tools (Automata, Petri nets) that is used to construct the model; the information (qualitative based on events occurrences and/or states outputs, quantitative based on signal processing and data analysis) that is needed to analyze and achieve the diagnosis; the decision structure (centralized, decentralized) that is required to achieve the diagnosis. The goal of this classification is to select the efficient method to achieve the fault diagnosis according to the application constraints. This book focuses on the centralized and decentralized event based diagnosis approaches using formal language and automata as mode...

Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

International Nuclear Information System (INIS)

Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

2004-01-01

In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

The Early Intervention Readiness Program (EIRP): A Post-ASD Diagnosis Family Support Program

Science.gov (United States)

Tolmie, Rhiannon S.; Bruck, Susan; Kerslake, Rachel

2017-01-01

A child's diagnosis with autism spectrum disorder (ASD) can be an extremely stressful time for families. Researchers suggest that the period immediately following ASD diagnosis is a key time for professionals to guide families by providing appropriate information about support options. This article describes a family support program, developed by…

PSG-EXPERT. An expert system for the diagnosis of sleep disorders.

Science.gov (United States)

Fred, A; Filipe, J; Partinen, M; Paiva, T

2000-01-01

This paper describes PSG-EXPERT, an expert system in the domain of sleep disorders exploring polysomnographic data. The developed software tool is addressed from two points of view: (1)--as an integrated environment for the development of diagnosis-oriented expert systems; (2)--as an auxiliary diagnosis tool in the particular domain of sleep disorders. Developed over a Windows platform, this software tool extends one of the most popular shells--CLIPS (C Language Integrated Production System) with the following features: backward chaining engine; graph-based explanation facilities; knowledge editor including a fuzzy fact editor and a rules editor, with facts-rules integrity checking; belief revision mechanism; built-in case generator and validation module. It therefore provides graphical support for knowledge acquisition, edition, explanation and validation. From an application domain point of view, PSG-Expert is an auxiliary diagnosis system for sleep disorders based on polysomnographic data, that aims at assisting the medical expert in his diagnosis task by providing automatic analysis of polysomnographic data, summarising the results of this analysis in terms of a report of major findings and possible diagnosis consistent with the polysomnographic data. Sleep disorders classification follows the International Classification of Sleep Disorders. Major features of the system include: browsing on patients data records; structured navigation on Sleep Disorders descriptions according to ASDA definitions; internet links to related pages; diagnosis consistent with polysomnographic data; graphical user-interface including graph-based explanatory facilities; uncertainty modelling and belief revision; production of reports; connection to remote databases.

Real-Time Clinical Decision Support System with Data Stream Mining

Directory of Open Access Journals (Sweden)

Yang Zhang

2012-01-01

Full Text Available This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare, diabetes diagnosis, and so forth. Most of the existing software technologies are case-based data mining systems. They only can analyze finite and structured data set and can only work well in their early years and can hardly meet today's medical requirement. In this paper, we describe a clinical-support-system based data stream mining technology; the design has taken into account all the shortcomings of the existing clinical support systems.

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

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.

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

Least-cost failure diagnosis in uncertain reliability systems

International Nuclear Information System (INIS)

Cox, Louis Anthony; Chiu, Steve Y.; Sun Xiaorong

1996-01-01

) appears to give excellent results. Several computational experiments are summarized in support of these conclusions, and extensions to reliability systems with repair are briefly considered. Next, it is shown that diagnosis can proceed when aleatory and epistemic uncertainties are both present using the same techniques developed for aleatory probabilities alone. If only the epistemic probability distribution of system descriptions is known, then the same heuristics that are used to diagnose a system's failure state for systems with known descriptions can also be used to identify the system and diagnose its failure state when there is epistemic uncertainty about the identity of the system. This result suggests a unified approach to least-cost failure diagnosis in reliability systems with both aleatory probabilities of component failures and epistemic probabilities for system descriptions

Building and application of the performance diagnosis system for nuclear power plants

International Nuclear Information System (INIS)

Ono, S.; Kanbara, K.; Sugawara, Y.

2010-01-01

To achieve a low-carbon society, we promote utilization of nuclear energy, which plays a zero-emission power generation. Therefore the nuclear power plants have been imposed a stable supply of electricity. The condition based maintenance (CBM) is effective in order to maintain a stable operation of the nuclear power plants. We built the performance diagnosis system based on the heat and mass balance calculation as one of supporting tools for the CBM. Moreover we note that the performance diagnosis system is built for steam turbine cycle operating with saturated steam conditions. (author)

An application of first-principles diagnosis to a thermalhydraulic system

International Nuclear Information System (INIS)

Lapointe, P.A.; Chung, J.

1990-03-01

Recent advances in computer technology, such as artificial intelligence and interactive multimedia, offer significant new opportunities to enhance nuclear plant safety and improve the performance of the operations staff. Atomic Energy of Canada Limited is developing a framework on which newer approaches to operator support systems will be implemented. A prototype system has been developed for plant information access and display, on-line advice and diagnosis, and interactive operating procedures; it is called the Operator Companion. This paper describes the work performed for the development of the fault detection and diagnostic module within the Operator Companion. An early prototype of the module was developed for a small heat transfer circuit. A qualitative physics representation coupled with first-principles diagnosis using constraint suspension was utilized. Temporal information was required; it was integrated into the diagnosis using a type of directed graph to record event dependencies. This graph dynamically altered the qualitative model to reflect changes in the system over time. Although not completely formal, out method has successfully integrated the time diagnostic information to permit the identification of the faulty components and limit the number of spurious candidates in the tests performed. This paper summarizes the qualitative diagnosis concepts, describes the special temporal reasoning scheme developed, and presents a summary of the results obtained

Diagnosis in the Enterprise Management System

Directory of Open Access Journals (Sweden)

Skrynkovskyy Ruslan M.

2016-08-01

Full Text Available The aim of the article is to define the role and place of the diagnosis management system in the structure of the task system of the enterprise diagnosis. There suggested the essence of the concept of «diagnosis of the enterprise», which is understood as the process of identification, analysis and evaluation of the enterprise state and trends in its changes (changes of the state on the basis of relevant business indicators in order to develop recommendations on the elimination of problematic points and weaknesses in the functioning of the enterprise to ensure a qualitatively new level of its development and formation of prospects with consideration to the consequences of violation of the legislation in the field of economics and enterprise management and law (legal responsibility for the violation of the labor law, tax law, law on protection of economic competition, law on trade secret, etc.. It was found that the diagnosis in the system of enterprise management: 1 is a structural component (or a partial diagnosis task in a group of private diagnosis tasks in the system of diagnosis task of the enterprise activity; 2 as a sub-function of the control function (as a general function of management includes such components as: assessment (identification of key features, characteristics, parameters (indexes, indicators, properties; analysis (a thorough study of the structure, dynamics, trends, etc.; identification (involves determination of deviations of parameters from the criteria and/or standards, formulation of diagnosis. Prospects for further research in this direction are the development of methods for quantitative assessment of the effectiveness of the management system with the purpose of its introducing in practical activities of enterprises, namely in the processes of decision-making.

Fuzzy fault diagnosis system of MCFC

Institute of Scientific and Technical Information of China (English)

Wang Zhenlei; Qian Feng; Cao Guangyi

2005-01-01

A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.

Social support and delays seeking care after HIV diagnosis, North Carolina, 2000-2006.

Science.gov (United States)

McCoy, Sandra I; Strauss, Ronald P; MacDonald, Pia D M; Leone, Peter A; Eron, Joseph J; Miller, William C

2009-09-01

Many adults in the USA enter primary care late in the course of HIV infection, countering the clinical benefits of timely HIV services and missing opportunities for risk reduction. Our objective was to determine if perceived social support was associated with delay entering care after an HIV diagnosis. Two hundred and sixteen patients receiving primary care at a large, university-based HIV outpatient clinic in North Carolina were included in the study. Dimensions of functional social support (emotional/informational, tangible, affectionate, and positive social interaction) were quantified with a modified Medical Outcomes Study Social Support Scale and included in proportional hazards models to determine their effect on delays seeking care. The median delay between diagnosis and entry to primary care was 5.9 months. Levels of social support were high but only positive social interaction was moderately associated with delayed presentation in adjusted models. The effect of low perceived positive social interaction on the time to initiation of primary care differed by history of alcoholism (no history of alcoholism, hazard ratio (HR): 1.43, 95% confidence interval (CI): 0.88, 2.34; history of alcoholism, HR: 0.71, 95% CI: 0.40, 1.28). Ensuring timely access to HIV care remains a challenge in the southeastern USA. Affectionate, tangible, and emotional/informational social support were not associated with the time from diagnosis to care. The presence of positive social interaction may be an important factor influencing care-seeking behavior after diagnosis.

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

International Nuclear Information System (INIS)

Traverso, A; Lopez Torres, E; Cerello, P; Fantacci, M E

2017-01-01

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists. (paper)

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

Science.gov (United States)

Traverso, A.; Lopez Torres, E.; Fantacci, M. E.; Cerello, P.

2017-05-01

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists.

An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription.

Science.gov (United States)

Shen, Ying; Yuan, Kaiqi; Chen, Daoyuan; Colloc, Joël; Yang, Min; Li, Yaliang; Lei, Kai

2018-03-01

The available antibiotic decision-making systems were developed from a physician's perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctor's advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor. In such cases, a reliable antibiotic prescription support system is needed. This study describes the construction and optimization of the sensitivity and specificity of a decision support system named IDDAP, which is based on ontologies for infectious disease diagnosis and antibiotic therapy. The ontology for this system was constructed by collecting existing ontologies associated with infectious diseases, syndromes, bacteria and drugs into the ontology's hierarchical conceptual schema. First, IDDAP identifies a potential infectious disease based on a patient's self-described disease state. Then, the system searches for and proposes an appropriate antibiotic therapy specifically adapted to the patient based on factors such as the patient's body temperature, infection sites, symptoms/signs, complications, antibacterial spectrum, contraindications, drug-drug interactions between the proposed therapy and previously prescribed medication, and the route of therapy administration. The constructed domain ontology contains 1,267,004 classes, 7,608,725 axioms, and 1,266,993 members of "SubClassOf" that pertain to infectious diseases, bacteria, syndromes, anti-bacterial drugs and other relevant components. The system includes 507 infectious diseases and their therapy methods in combination with 332 different infection sites, 936 relevant symptoms of the digestive, reproductive, neurological and other systems, 371 types of complications, 838,407 types of bacteria, 341 types of antibiotics, 1504 pairs of reaction rates (antibacterial spectrum) between antibiotics and bacteria, 431

Energy systems Diagnosis in developing countries

International Nuclear Information System (INIS)

Girod, J.

1991-01-01

Energy systems diagnosis is necessary to allow evaluation of energy balance by administration and political authorities of a country. First, the author describes the principle stages of energetic diagnosis. Then this work is divided into three parts: First part: Energy consumption diagnosis in several districts (families, utilities, agriculture, transport, industry) Second part: Energy supplies diagnosis (energy markets). Third part: Interactions between energy consumption and energy supply. 28 figs.; 52 tabs.; 107 refs

A Novel Sonification Approach to Support the Diagnosis of Alzheimer’s Dementia

Directory of Open Access Journals (Sweden)

Letizia Gionfrida

2017-12-01

Full Text Available Alzheimer’s disease is the most common neurodegenerative form of dementia that steadily worsens and eventually leads to death. Its set of symptoms include loss of cognitive function and memory decline. Structural and functional imaging methods such as CT, MRI, and PET scans play an essential role in the diagnosis process, being able to identify specific areas of cerebral damages. While the accuracy of these imaging techniques increases over time, the severity assessment of dementia remains challenging and susceptible to cognitive and perceptual errors due to intra-reader variability among physicians. Doctors have not agreed upon standardized measurement of cell loss used to specifically diagnose dementia among individuals. These limitations have led researchers to look for supportive diagnosis tools to enhance the spectrum of diseases characteristics and peculiarities. Here is presented a supportive auditory tool to aid in diagnosing patients with different levels of Alzheimer’s. This tool introduces an audible parameter mapped upon three different brain’s lobes. The motivating force behind this supportive auditory technique arise from the fact that AD is distinguished by a decrease of the metabolic activity (hypometabolism in the parietal and temporal lobes of the brain. The diagnosis is then performed by comparing metabolic activity of the affected lobes to the metabolic activity of other lobes that are not generally affected by AD (i.e., sensorimotor cortex. Results from the diagnosis process compared with the ground truth show that physicians were able to categorize different levels of AD using the sonification generated in this study with higher accuracy than using a standard diagnosis procedure, based on the visualization alone.

Expert system for nuclear power plant feedwater system diagnosis

International Nuclear Information System (INIS)

Meguro, R.; Kinoshita, Y.; Sato, T.; Yokota, Y.; Yokota, M.

1987-01-01

The Expert System for Nuclear Power Plant Feedwater System Diagnosis has been developed to assist maintenance engineers in nuclear power plants. This system adopts the latest process computer TOSBAC G8050 and the expert system developing tool TDES2, and has a large scale knowledge base which consists of the expert knowledge and experience of engineers in many fields. The man-machine system, which has been developed exclusively for diagnosis, improves the man-machine interface and realizes the graphic displays of diagnostic process and path, stores diagnostic results and searches past reference

Privacy-preserving clinical decision support system using Gaussian kernel-based classification.

Science.gov (United States)

Rahulamathavan, Yogachandran; Veluru, Suresh; Phan, Raphael C-W; Chambers, Jonathon A; Rajarajan, Muttukrishnan

2014-01-01

A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised.

Intelligent System for Diagnosis of a Three-Phase Separator

Directory of Open Access Journals (Sweden)

Irina Ioniţă

2016-03-01

Full Text Available Intelligent systems for diagnosis have been used in a variety of domains: financial evaluation, credit scoring problem, identification of software and hardware problems of mechanical and electronic equipment, medical diagnosis, fault detection in gas-oil production plants etc. The goal of diagnosis systems is to classify the observed symptoms as being caused by some diagnosis class while advising systems perform such a classification and offer corrective remedies (recommendations. The current paper discuss the opportunity to combine more intelligent techniques and methodologies (intelligent agents, data mining and expert systems to increase the accuracy of results obtained from the diagnosis of a three-phase separator. The results indicate that the diagnosis hybrid system benefits from the advantages of each module component: intelligent agent module, data mining module and expert system module.

Interface support of fault diagnosis strategies : a user-driven approach

NARCIS (Netherlands)

Schaaf, van der T.W.; Brinkman, J.A.

1993-01-01

In this paper an overview is presented of a research program to provide interface designers with guidelines to support process control- and fault diagnosis tasks by control-room operators. Different methods of eliciting information needs of end-users had to be developed, as explained by an

Support of the Laboratory in the Diagnosis of Fungal Ocular Infections

Science.gov (United States)

Vanzzini Zago, Virginia; Alcantara Castro, Marino; Naranjo Tackman, Ramon

2012-01-01

This is a retrospective, and descriptive study about the support that the laboratory of microbiology aids can provide in the diagnosis of ocular infections in patients whom were attended a tertiary-care hospital in México City in a 10-year-time period. We describe the microbiological diagnosis in palpebral mycose; in keratitis caused by Fusarium, Aspergillus, Candida, and melanized fungi; endophthalmitis; one Histoplasma scleritis and one mucormycosis. Nowadays, ocular fungal infections are more often diagnosed, because there is more clinical suspicion and there are easy laboratory confirmations. Correct diagnosis is important because an early medical treatment gives a better prognosis for visual acuity. In some cases, fungal infections are misdiagnosed and the antifungal treatment is delayed. PMID:22518339

Experience on a BWR plant diagnosis system

International Nuclear Information System (INIS)

Tanabe, A.; Kawai, K.; Hashimoto, Y.

1981-01-01

It is important to watch plant dynamics and equipment condition for avoiding a big transient or avoiding damage to a system by equipment failure. After the TMI accident the necessity of a diagnosis system has been recognized and such development activities have become of primary importance in many organizations. A diagnosis system has two kinds of function. One is the early detection of an anomaly before detection by a conventional instrumentation system. The other is appropriate instruction after alarm or scram has occurred. The authors have been developing the former system by a noise analysis technique and a feasibility study has been undertaken in recent years as a joint research programme of several electric power companies and the Toshiba Corporation. A prototype diagnosis system has been installed on a BWR plant in Japan. This diagnosis system concerns reactor core, jet pumps and three main control systems. Many data from normal operation have been accumulated using this system and a variation pattern of normal noise data is clarified. On this basis, anomally detection criteria have been determined using statistical decision theory. It is confirmed that this system performance is satisfactory, and that the system will be of great use for surveillance of core and control systems without artificial disturbances. (author)

Remote diagnosis system for control and instrumentation systems

International Nuclear Information System (INIS)

Ito, Tetsuo; Suzuki, Satoshi; Nagaoka, Yukio.

1990-01-01

Control and instrumentation (C and I) systems for nuclear power plants tend to consist of many distributed digital controllers connected with transmission networks. Important parts of the C and I systems are redundantly constructed so that the failure of a component does not readily have a critical effect on the plant operation. It is necessary, however, to localize the faulty component for establishing better availability and maintainability of the plant. To diagnose failure of the C and I systems effectively, a remote diagnosis system is required that diagnoses anomalies of their controllers remotely from a central control room and identifies the fault location. Various fault diagnosis methods that apply artificial intelligence have been proposed for electronic circuits. Their knowledge bases are classified into two categories. One is rule-based knowledge, describing relations between anomaly phenomena and causes. The other is structure-based knowledge, which represents the configuration and functions of diagnosed objects. Though the latter is more suitable for deep inference, it is difficult to use for describing the detailed structure of large-scaled digital C and I systems. Then, a fault diagnosis system was developed that uses both knowledge bases and offers substantial man/machine interface functions for practical use

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.

Utility of axial images in an early Alzheimer disease diagnosis support system (VSRAD)

International Nuclear Information System (INIS)

Goto, Masami; Aoki, Shigeki; Abe, Osamu

2006-01-01

In recent years, voxel-based morphometry (VBM) has become a popular tool for the early diagnosis of Alzheimer disease. The Voxel-Based Specific Regional Analysis System for Alzheimer's Disease (VSRAD), a VBM system that uses MRI, has been reported to be clinically useful. The able-bodied person database (DB) of VSRAD, which employs sagittal plane imaging, is not suitable for analysis by axial plane imaging. However, axial plane imaging is useful for avoiding motion artifacts from the eyeball. Therefore, we created an able-bodied person DB by axial plane imaging and examined its utility. We also analyzed groups of able-bodied persons and persons with dementia by axial plane imaging and reviewed the validity. After using the DB of axial plane imaging, the Z-score of the intrahippocampal region improved by 8 in 13 instances. In all brains, the Z-score improved by 13 in all instances. (author)

[Utility of axial images in an early Alzheimer disease diagnosis support system (VSRAD)].

Science.gov (United States)

Goto, Masami; Aoki, Shigeki; Abe, Osamu; Masumoto, Tomohiko; Watanabe, Yasushi; Satake, Yoshiroh; Nishida, Katsuji; Ino, Kenji; Yano, Keiichi; Iida, Kyohhito; Mima, Kazuo; Ohtomo, Kuni

2006-09-20

In recent years, voxel-based morphometry (VBM) has become a popular tool for the early diagnosis of Alzheimer disease. The Voxel-Based Specific Regional Analysis System for Alzheimer's Disease (VSRAD), a VBM system that uses MRI, has been reported to be clinically useful. The able-bodied person database (DB) of VSRAD, which employs sagittal plane imaging, is not suitable for analysis by axial plane imaging. However, axial plane imaging is useful for avoiding motion artifacts from the eyeball. Therefore, we created an able-bodied person DB by axial plane imaging and examined its utility. We also analyzed groups of able-bodied persons and persons with dementia by axial plane imaging and reviewed the validity. After using the DB of axial plane imaging, the Z-score of the intrahippocampal region improved by 8 in 13 instances. In all brains, the Z-score improved by 13 in all instances.

Development of knowledge-based operator support system for steam generator water leak events in FBR plants

International Nuclear Information System (INIS)

Arikawa, Hiroshi; Ida, Toshio; Matsumoto, Hiroyuki; Kishida, Masako

1991-01-01

A knowledge engineering approach to operation support system would be useful in maintaining safe and steady operation in nuclear plants. This paper describes a knowledge-based operation support system which assists the operators during steam generator water leak events in FBR plants. We have developed a real-time expert system. The expert system adopts hierarchical knowledge representation corresponding to the 'plant abnormality model'. A technique of signal validation which uses knowledge of symptom propagation are applied to diagnosis. In order to verify the knowledge base concerning steam generator water leak events in FBR plants, a simulator is linked to the expert system. It is revealed that diagnosis based on 'plant abnormality model' and signal validation using knowledge of symptom propagation could work successfully. Also, it is suggested that the expert system could be useful in supporting FBR plants operations. (author)

Social networks, social support mechanisms, and quality of life after breast cancer diagnosis.

Science.gov (United States)

Kroenke, Candyce H; Kwan, Marilyn L; Neugut, Alfred I; Ergas, Isaac J; Wright, Jaime D; Caan, Bette J; Hershman, Dawn; Kushi, Lawrence H

2013-06-01

We examined mechanisms through which social relationships influence quality of life (QOL) in breast cancer survivors. This study included 3,139 women from the Pathways Study who were diagnosed with breast cancer from 2006 to 2011 and provided data on social networks (the presence of a spouse or intimate partner, religious/social ties, volunteering, and numbers of close friends and relatives), social support (tangible support, emotional/informational support, affection, positive social interaction), and QOL, measured by the FACT-B, approximately 2 months post diagnosis. We used logistic models to evaluate associations between social network size, social support, and lower versus higher than median QOL scores. We further stratified by stage at diagnosis and treatment. In multivariate-adjusted analyses, women who were characterized as socially isolated had significantly lower FACT-B (OR = 2.18, 95 % CI: 1.72-2.77), physical well-being (WB) (OR = 1.61, 95 % CI: 1.27-2.03), functional WB (OR = 2.08, 95 % CI: 1.65-2.63), social WB (OR = 3.46, 95 % CI: 2.73-4.39), and emotional WB (OR = 1.67, 95 % CI: 1.33-2.11) scores and higher breast cancer symptoms (OR = 1.48, 95 % CI: 1.18-1.87) compared with socially integrated women. Each social network member independently predicted higher QOL. Simultaneous adjustment for social networks and social support partially attenuated associations between social networks and QOL. The strongest mediator and type of social support that was most predictive of QOL outcomes was "positive social interaction." However, each type of support was important depending on outcome, stage, and treatment status. Larger social networks and greater social support were related to higher QOL after a diagnosis of breast cancer. Effective social support interventions need to evolve beyond social-emotional interventions and need to account for disease severity and treatment status.

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

Directory of Open Access Journals (Sweden)

Yasuo Yamashita

2009-07-01

Full Text Available This paper reviews the basics and recent researches of computer-aided diagnosis (CAD systems for assisting neuroradiologists in detection of brain diseases, e.g., asymptomatic unruptured aneurysms, Alzheimer's disease, vascular dementia, and multiple sclerosis (MS, in magnetic resonance (MR images. The CAD systems consist of image feature extraction based on image processing techniques and machine learning classifiers such as linear discriminant analysis, artificial neural networks, and support vector machines. We introduce useful examples of the CAD systems in the neuroradiology, and conclude with possibilities in the future of the CAD systems for brain diseases in MR images.

[Intelligent systems tools in the diagnosis of acute coronary syndromes: A systemic review].

Science.gov (United States)

Sprockel, John; Tejeda, Miguel; Yate, José; Diaztagle, Juan; González, Enrique

2017-03-27

Acute myocardial infarction is the leading cause of non-communicable deaths worldwide. Its diagnosis is a highly complex task, for which modelling through automated methods has been attempted. A systematic review of the literature was performed on diagnostic tests that applied intelligent systems tools in the diagnosis of acute coronary syndromes. A systematic review of the literature is presented using Medline, Embase, Scopus, IEEE/IET Electronic Library, ISI Web of Science, Latindex and LILACS databases for articles that include the diagnostic evaluation of acute coronary syndromes using intelligent systems. The review process was conducted independently by 2 reviewers, and discrepancies were resolved through the participation of a third person. The operational characteristics of the studied tools were extracted. A total of 35 references met the inclusion criteria. In 22 (62.8%) cases, neural networks were used. In five studies, the performances of several intelligent systems tools were compared. Thirteen studies sought to perform diagnoses of all acute coronary syndromes, and in 22, only infarctions were studied. In 21 cases, clinical and electrocardiographic aspects were used as input data, and in 10, only electrocardiographic data were used. Most intelligent systems use the clinical context as a reference standard. High rates of diagnostic accuracy were found with better performance using neural networks and support vector machines, compared with statistical tools of pattern recognition and decision trees. Extensive evidence was found that shows that using intelligent systems tools achieves a greater degree of accuracy than some clinical algorithms or scales and, thus, should be considered appropriate tools for supporting diagnostic decisions of acute coronary syndromes. Copyright © 2017 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. All rights reserved.

Towards fault-tolerant decision support systems for ship operator guidance

DEFF Research Database (Denmark)

Nielsen, Ulrik Dam; Lajic, Zoran; Jensen, Jørgen Juncher

2012-01-01

Fault detection and isolation are very important elements in the design of fault-tolerant decision support systems for ship operator guidance. This study outlines remedies that can be applied for fault diagnosis, when the ship responses are assumed to be linear in the wave excitation. A novel num...

Diagnosis and treatment of participants of support groups for hypersexual disorder

Directory of Open Access Journals (Sweden)

Els Tierens

2014-09-01

Full Text Available Background: The aim of this study is to examine the extent to which members of support groups for hypersexual disorder meet the proposed criteria for hypersexual disorder of Kafka, how the diagnosis of hypersexual disorders is made and what treatments are currently given. Methods: In this non-interventional research survey, members of support groups for hypersexual disorder received a questionnaire in which the criteria for hypersexual disorder according to Kafka were included as well as the way the disease was diagnosed and treated. Results: The questionnaire was presented to 32 people but only 10 completed questionnaires were returned. Five of the ten respondents met the criteria of Kafka. For the other five respondents a hypersexual disorder was not confirmed but neither excluded. Only for three respondents the diagnosis was made by a professional healthcare worker. The treatment included – besides the support group in nine cases – also individual psychotherapy. Two respondents took a selective serotonin re-uptake inhibitor (SSRI, as recommended in the literature. Conclusions: The members of support groups for sex addiction were difficult to motivate for their participation. The way hypersexual disorders were diagnosed was far from optimal. Only two participants received the recommended medication.

Real-time multi-task operators support system

International Nuclear Information System (INIS)

Wang He; Peng Minjun; Wang Hao; Cheng Shouyu

2005-01-01

The development in computer software and hardware technology and information processing as well as the accumulation in the design and feedback from Nuclear Power Plant (NPP) operation created a good opportunity to develop an integrated Operator Support System. The Real-time Multi-task Operator Support System (RMOSS) has been built to support the operator's decision making process during normal and abnormal operations. RMOSS consists of five system subtasks such as Data Collection and Validation Task (DCVT), Operation Monitoring Task (OMT), Fault Diagnostic Task (FDT), Operation Guideline Task (OGT) and Human Machine Interface Task (HMIT). RMOSS uses rule-based expert system and Artificial Neural Network (ANN). The rule-based expert system is used to identify the predefined events in static conditions and track the operation guideline through data processing. In dynamic status, Back-Propagation Neural Network is adopted for fault diagnosis, which is trained with the Genetic Algorithm. Embedded real-time operation system VxWorks and its integrated environment Tornado II are used as the RMOSS software cross-development. VxGUI is used to design HMI. All of the task programs are designed in C language. The task tests and function evaluation of RMOSS have been done in one real-time full scope simulator. Evaluation results show that each task of RMOSS is capable of accomplishing its functions. (authors)

SENSORS FAULT DIAGNOSIS ALGORITHM DESIGN OF A HYDRAULIC SYSTEM

Directory of Open Access Journals (Sweden)

Matej ORAVEC

2017-06-01

Full Text Available This article presents the sensors fault diagnosis system design for the hydraulic system, which is based on the group of the three fault estimation filters. These filters are used for estimation of the system states and sensors fault magnitude. Also, this article briefly stated the hydraulic system state control design with integrator, which is important assumption for the fault diagnosis system design. The sensors fault diagnosis system is implemented into the Matlab/Simulink environment and it is verified using the controlled hydraulic system simulation model. Verification of the designed fault diagnosis system is realized by series of experiments, which simulates sensors faults. The results of the experiments are briefly presented in the last part of this article.

Uncertainty management, spatial and temporal reasoning, and validation of intelligent environmental decision support systems

Science.gov (United States)

Sànchez-Marrè, Miquel; Gilbert, Karina; Sojda, Rick S.; Steyer, Jean Philippe; Struss, Peter; Rodríguez-Roda, Ignasi; Voinov, A.A.; Jakeman, A.J.; Rizzoli, A.E.

2006-01-01

There are inherent open problems arising when developing and running Intelligent Environmental Decision Support Systems (IEDSS). During daily operation of IEDSS several open challenge problems appear. The uncertainty of data being processed is intrinsic to the environmental system, which is being monitored by several on-line sensors and off-line data. Thus, anomalous data values at data gathering level or even uncertain reasoning process at later levels such as in diagnosis or decision support or planning can lead the environmental process to unsafe critical operation states. At diagnosis level or even at decision support level or planning level, spatial reasoning or temporal reasoning or both aspects can influence the reasoning processes undertaken by the IEDSS. Most of Environmental systems must take into account the spatial relationships between the environmental goal area and the nearby environmental areas and the temporal relationships between the current state and the past states of the environmental system to state accurate and reliable assertions to be used within the diagnosis process or decision support process or planning process. Finally, a related issue is a crucial point: are really reliable and safe the decisions proposed by the IEDSS? Are we sure about the goodness and performance of proposed solutions? How can we ensure a correct evaluation of the IEDSS? Main goal of this paper is to analyse these four issues, review some possible approaches and techniques to cope with them, and study new trends for future research within the IEDSS field.

A Multi-Model Approach for System Diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Bækgaard, Mikkel Ask Buur

2007-01-01

A multi-model approach for system diagnosis is presented in this paper. The relation with fault diagnosis as well as performance validation is considered. The approach is based on testing a number of pre-described models and find which one is the best. It is based on an active approach......,i.e. an auxiliary input to the system is applied. The multi-model approach is applied on a wind turbine system....

Active fault diagnosis in closed-loop uncertain systems

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2006-01-01

Fault diagnosis of parametric faults in closed-loop uncertain systems by using an auxiliary input vector is considered in this paper, i.e. active fault diagnosis (AFD). The active fault diagnosis is based directly on the socalled fault signature matrix, related to the YJBK (Youla, Jabr, Bongiorno...... and Kucera) parameterization. Conditions are given for exact detection and isolation of parametric faults in closed-loop uncertain systems....

Development of distributed plant monitoring and diagnosis system at Monju

International Nuclear Information System (INIS)

Okusa, Kyoichi; Tamayama, Kiyoshi; Kitamura, Tomomi

2003-01-01

In a nuclear plant, it is required to detect an anomaly as early as possible and to inhibit adverse consequences. This requirement is especially important for a prototype Fast Breeder Reactor Monju. Therefore, a monitoring and diagnosis system is required to be developed for Monju plant equipments. In these days, such a monitoring and diagnosis system can be realized using Web technology with rationalized system resources due to the remarkable progress of computer network technology. Then, we developed a Web based platform for the monitoring and diagnosis system of Monju. Distributed architecture, standardization and highly flexible system structure have been taken account of in the development. This newly developed platform and prototype monitoring and diagnosis systems have been validated. Prototype monitoring and diagnosis systems on the platform acquire Monju plant data and display the data on client computers using Monju intranet with acceptable delay times. The prototype monitoring and diagnosis systems for Monju have been developed on the platform and the whole system has been validated. (author)

Social networks, social support mechanisms, and quality of life after breast cancer diagnosis

Science.gov (United States)

Kroenke, Candyce H; Kwan, Marilyn L.; Neugut, Alfred I.; Ergas, Isaac J.; Wright, Jaime D.; Caan, Bette J.; Hershman, Dawn; Kushi, Lawrence H.

2013-01-01

Purpose We examined mechanisms through which social relationships influence quality of life (QOL) in breast cancer survivors. Methods This study included 3,139 women from the Pathways Study who were diagnosed with breast cancer from 2006-2011 and provided data on social networks (presence of spouse or intimate partner, religious/social ties, volunteering, and numbers of close friends and relatives), social support (tangible, emotional/informational, affection, positive social interaction), and quality of life (QOL), measured by the FACT-B, approximately two months post-diagnosis. We used logistic models to evaluate associations between social network size, social support, and lower vs. higher than median QOL scores. We further stratified by stage at diagnosis and treatment. Results In multivariate-adjusted analyses, women who were characterized as socially isolated had significantly lower FACT-B (OR=2.18, 95%CI:1.72-2.77), physical well-being (WB) (OR=1.61, 95%CI:1.27-2.03), functional WB (OR=2.08, 95%CI:1.65-2.63), social WB (OR=3.46, 95%CI:2.73-4.39), and emotional WB (OR=1.67, 95%CI:1.33-2.11) scores and higher breast cancer symptoms (OR=1.48, 95%CI:1.18-1.87), compared with socially integrated women. Each social network member independently predicted higher QOL. Simultaneous adjustment for social networks and social support partially attenuated associations between social networks and QOL. The strongest mediator and type of social support that was most predictive of QOL outcomes was “positive social interaction”. However, each type of support was important depending on outcome, stage, and treatment status. Conclusions Larger social networks and greater social support were related to higher QOL after a diagnosis of breast cancer. Effective social support interventions need to evolve beyond social-emotional interventions and need to account for disease severity and treatment status. PMID:23657404

Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system

International Nuclear Information System (INIS)

Kavitha, M S; Asano, Akira; Taguchi, Akira; Kurita, Takio; Sanada, Mitsuhiro

2012-01-01

Early diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD. We employed our newly adopted SVM method for continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify women with low BMD or osteoporosis. The original X-ray image was enhanced, cortical boundaries were determined, distances among the upper and lower boundaries were evaluated and discrimination was performed by a radial basis function. We evaluated the diagnostic efficacy of this newly developed method for identifying women with low BMD (BMD T-score of -1.0 or less) at the lumbar spine and femoral neck in 100 postmenopausal women (≥50 years old) with no previous diagnosis of osteoporosis. Sixty women were used for system training, and 40 were used in testing. The sensitivity and specificity using RBF kernel-SVM method for identifying women with low BMD were 90.9% [95% confidence interval (CI), 85.3-96.5] and 83.8% (95% CI, 76.6-91.0), respectively at the lumbar spine and 90.0% (95% CI, 84.1-95.9) and 69.1% (95% CI, 60.1-78.6), respectively at the femoral neck. The sensitivity and specificity for identifying women with low BMD at either the lumbar spine or femoral neck were 90.6% (95% CI, 92.0-100) and 80.9% (95% CI, 71.0-86.9), respectively. Our results suggest that the newly developed system with the SVM method would be useful for identifying postmenopausal women with low skeletal BMD

Expert systems for real-time monitoring and fault diagnosis

Science.gov (United States)

Edwards, S. J.; Caglayan, A. K.

1989-01-01

Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and fault diagnosis applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and fault diagnosis systems, and their applications to new real-time hardware fault diagnosis and monitoring systems for aircraft.

Evaluation of computer-aided detection and diagnosis systems.

Science.gov (United States)

Petrick, Nicholas; Sahiner, Berkman; Armato, Samuel G; Bert, Alberto; Correale, Loredana; Delsanto, Silvia; Freedman, Matthew T; Fryd, David; Gur, David; Hadjiiski, Lubomir; Huo, Zhimin; Jiang, Yulei; Morra, Lia; Paquerault, Sophie; Raykar, Vikas; Samuelson, Frank; Summers, Ronald M; Tourassi, Georgia; Yoshida, Hiroyuki; Zheng, Bin; Zhou, Chuan; Chan, Heang-Ping

2013-08-01

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in

Omnidirectional regeneration (ODR) of proximity sensor signals for robust diagnosis of journal bearing systems

Science.gov (United States)

Jung, Joon Ha; Jeon, Byung Chul; Youn, Byeng D.; Kim, Myungyon; Kim, Donghwan; Kim, Yeonwhan

2017-06-01

Some anomaly states of journal bearing rotor systems are direction-oriented (e.g., rubbing, misalignment). In these situations, vibration signals vary according to the direction of the sensors and the health state. This makes diagnosis difficult with traditional diagnosis methods. This paper proposes an omnidirectional regeneration method to develop a robust diagnosis algorithm for rotor systems. The proposed method can generate vibration signals in arbitrary directions without using extra sensors. In this method, signals are generated around the entire circumference of the rotor to consider all possible directions. Then, the directionality of each state is proved by mathematically and is evaluated using a proposed metric. When a directional state is determined, the classification is carried out on all of the generated signals. When a non-directional state is found, the classification is performed on only one of the generated signals to minimize computational load without sacrificing accuracy. The proposed ODR method was validated using experimental data. The classification results show that the proposed method generally outperforms the conventional classification method. The results support the proposed concept of using ODR signals in diagnosis procedures for journal bearing systems.

Plant experience with an expert system for alarm diagnosis

International Nuclear Information System (INIS)

Gimmy, K.L.

1986-01-01

An expert system called Diagnosis of Multiple Alarms (DMA) is in routine use at four nuclear reactors operated by the DuPont Company. The system is wired to plant alarm annunciators and does event-tree analysis to see if a pattern exists. Any diagnosis is displayed to the plant operator and the corrective procedure to be followed is also identified. The display is automatically superseded if a higher priority diagnosis is made. The system is integrated with operator training and procedures. Operating results have been positive. DMA has diagnosed several hard-to-locate small leaks. There have been some false diagnosis, and realistic plant environments must be considered in such expert systems. 2 refs., 5 figs

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.

Dementia diagnosis and post-diagnostic support in Scottish rural communities: experiences of people with dementia and their families.

Science.gov (United States)

Innes, Anthea; Szymczynska, Paulina; Stark, Cameron

2014-03-01

This paper explores the reported difficulties and satisfactions with diagnostic processes and post-diagnostic support offered to people with dementia and their families living in the largest remote and rural region in Scotland. A consultation with 18 participants, six people with dementia and 12 family members, was held using semi-structured interviews between September and November 2010. Three points in the diagnostic process were explored: events and experiences pre-diagnosis; the experience of the diagnostic process; and post-diagnostic support. Experiences of people with dementia and their carers varied at all three points in the diagnostic process. Participant experiences in this study suggest greater efforts are required to meet Government diagnosis targets and that post-diagnostic support needs to be developed and monitored to ensure that once a diagnosis is given people are well-supported. Without post-diagnostic provision Government targets for diagnosis are just that, quota targets, rather than a means to improve service experiences.

Diagnosis of the Main Busbar II Panel Components Ageing of RSG-GAS Electrical System by Using Infrared Thermography

International Nuclear Information System (INIS)

Teguh Sulistyo; Kiswanto; Roziq Himawan; Ari Satmoko

2007-01-01

To support the operation of RSG-GAS safely, the diagnosis of the ageing of main busbar II BHD/BHE/BHF panel components of RSG-GAS electrical system have been done. By using infrared thermography type Thermo Tracer TH9100PM VI/PW VI. The results of the diagnosis showed that some of the components under degradation with various rate. It can cause the system failure. By understanding the components ageing degradation mechanism and performing the preventive and predictive maintenance and safety of RSG-GAS electrical system earlier, the possibility of accident can be avoided. (author)

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.

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

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.

Development of expert system for fault diagnosis and restoration at substations

Energy Technology Data Exchange (ETDEWEB)

Choo, Jin Boo; Kwon, Tae Won; Yoon, Yong Beum; Park, Sung Taek [Korea Electric Power Corp. (KEPCO), Taejon (Korea, Republic of). Research Center; Park, Young Moon; Lee, Heung Jae [Electrical Engineering and Science Research Institute (Korea, Republic of)

1996-12-31

When a fault occurs in power systems, the operators have to make precise judgements on the situation and take appropriate actions rapidly to protect the system and minimize the black-out area. However, the larger and the more complex the power systems become, the more difficult it becomes to expect the effective actions of human operators. Therefore, it is a very important issue to support the operators of the local power systems in the case of various faults. We develop an expert system for fault diagnosis and reconfiguration of local power system. The expert system has a capability of identifying the location and the type of faults, the black-out area, and an appropriate reconfiguration procedure for re-energizing or minimizing the service interruption (author). 35 refs., 45 figs.

Development of expert system for fault diagnosis and restoration at substations

Energy Technology Data Exchange (ETDEWEB)

Choo, Jin Boo; Kwon, Tae Won; Yoon, Yong Beum; Park, Sung Taek [Korea Electric Power Corp. (KEPCO), Taejon (Korea, Republic of). Research Center; Park, Young Moon; Lee, Heung Jae [Electrical Engineering and Science Research Institute (Korea, Republic of)

1995-12-31

When a fault occurs in power systems, the operators have to make precise judgements on the situation and take appropriate actions rapidly to protect the system and minimize the black-out area. However, the larger and the more complex the power systems become, the more difficult it becomes to expect the effective actions of human operators. Therefore, it is a very important issue to support the operators of the local power systems in the case of various faults. We develop an expert system for fault diagnosis and reconfiguration of local power system. The expert system has a capability of identifying the location and the type of faults, the black-out area, and an appropriate reconfiguration procedure for re-energizing or minimizing the service interruption (author). 35 refs., 45 figs.

Image processing for medical diagnosis of human organs

International Nuclear Information System (INIS)

Tamura, Shin-ichi

1989-01-01

The report first describes expectations and needs for diagnostic imaging in the field of clinical medicine, radiation medicine in particular, viewed by the author as an image processing expert working at a medical institute. Then, medical image processing techniques are discussed in relation to advanced information processing techniques that are currently drawing much attention in the field of engineering. Finally, discussion is also made of practical applications of image processing techniques to diagnosis. In the field of clinical diagnosis, advanced equipment such as PACS (picture archiving and communication system) has come into wider use, and efforts have been made to shift from visual examination to more quantitative and objective diagnosis by means of such advanced systems. In clinical medicine, practical, robust systems are more useful than sophisticated ones. It is difficult, though important, to develop completely automatized diagnostic systems. The urgent, realistic goal, therefore, is to develop effective diagnosis support systems. In particular, operation support systems equipped with three-dimensional displays will be very useful. (N.K.)

Water chemistry diagnosis system for nuclear power plants

International Nuclear Information System (INIS)

Igarashi, Hiroo; Koya, Hiroshi; Osumi, Katsumi.

1990-01-01

The water quality control for the BWRs in Japan has advanced rapidly recently, and as to the dose reduction due to the decrease of radioactivity, Japan takes the position leading the world. In the background of the advanced water quality control like this and the increase of nuclear power plants in operation, the automation of arranging a large quantity of water quality control information and the heightening of its reliability have been demanded. Hitachi group developed the water quality synthetic control system which comprises the water quality data management system to process a large quantity of water quality data with a computer and the water quality diagnosis system to evaluate the state of operation of the plants by the minute change of water quality and to carry out the operational guide in the aspect of water quality control. To this water quality diagnosis system, high speed fuzzy inference is applied in order to do rapid diagnosis with fuzzy data. The trend of development of water quality control system, the construction of the water quality synthetic control system, the configuration of the water quality diagnosis system and the development of algorithm and the improvement of the reliability of maintenance are reported. (K.I.)

Computer Aided Diagnosis System for Early Lung Cancer Detection

Directory of Open Access Journals (Sweden)

Fatma Taher

2015-11-01

Full Text Available Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. This was the motivation behind the design and the development of a new computer aided diagnosis (CAD system for early detection of lung cancer based on the analysis of sputum color images. The proposed CAD system encompasses four main processing steps. First is the preprocessing step which utilizes a Bayesian classification method using histogram analysis. Then, in the second step, mean shift segmentation is applied to segment the nuclei from the cytoplasm. The third step is the feature analysis. In this step, geometric and chromatic features are extracted from the nucleus region. These features are used in the diagnostic process of the sputum images. Finally, the diagnosis is completed using an artificial neural network and support vector machine (SVM for classifying the cells into benign or malignant. The performance of the system was analyzed based on different criteria such as sensitivity, specificity and accuracy. The evaluation was carried out using Receiver Operating Characteristic (ROC curve. The experimental results demonstrate the efficiency of the SVM classifier over other classifiers, with 97% sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

Energy Technology Data Exchange (ETDEWEB)

Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

2010-12-15

The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

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

Diagnosis of wind turbine rotor system

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Mirzaei, Mahmood; Henriksen, Lars Christian

2016-01-01

is based on available standard sensors on wind turbines. The method can be used both on-line as well as off-line. Faults or changes in the rotor system will result in asymmetries, which can be monitored and diagnosed. This can be done by using the multi-blade coordinate transformation. Changes in the rotor......This paper describes a model free method for monitoring and fault diagnosis of the elements in a rotor system for a wind turbine. The diagnosis as well as the monitoring is done without using any model of the wind turbine and the applied controller or a description of the wind profile. The method...

Development of 4D jaw movement visualization system for dental diagnosis support

Science.gov (United States)

Aoki, Yoshimitsu; Terajima, Masahiko; Nakasima, Akihiko

2004-10-01

A person with an asymmetric morphology of maxillofacial skeleton reportedly possesses an asymmetric jaw function and the risk to express temporomandibular disorder is high. A comprehensive analysis from the point of view of both the morphology and the function such as maxillofacial or temporomandibular joint morphology, dental occlusion, and features of mandibular movement pathways is essential. In this study, the 4D jaw movement visualization system was developed to visually understand the characteristic jaw movement, 3D maxillofacial skeleton structure, and the alignment of the upper and lower teeth of a patient. For this purpose, the 3D reconstructed images of the cranial and mandibular bones, obtained by computed tomography, were measured using a non-contact 3D measuring device, and the obtained morphological images of teeth model were integrated and activated on the 6 DOF jaw movement data. This system was experimentally applied and visualized in a jaw deformity patient and its usability as a clinical diagnostic support system was verified.

Application of ENN-1 for Fault Diagnosis of Wind Power Systems

Directory of Open Access Journals (Sweden)

Meng-Hui Wang

2012-01-01

Full Text Available Maintaining a wind turbine and ensuring secure is not easy because of long-term exposure to the environment and high installation locations. Wind turbines need fully functional condition-monitoring and fault diagnosis systems that prevent accidents and reduce maintenance costs. This paper presents a simulator design for fault diagnosis of wind power systems and further proposes some fault diagnosis technologies such as signal analysis, feature selecting, and diagnosis methods. First, this paper uses a wind power simulator to produce fault conditions and features from the monitoring sensors. Then an extension neural network type-1- (ENN-1- based method is proposed to develop the core of the fault diagnosis system. The proposed system will benefit the development of real fault diagnosis systems with testing models that demonstrate satisfactory results.

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

Science.gov (United States)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2015-01-01

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

Safety parameter display system: an operator support system for enhancement of safety in Indian PHWRs

International Nuclear Information System (INIS)

Subramaniam, K.; Biswas, T.

1994-01-01

Ensuring operational safety in nuclear power plants is important as operator errors are observed to contribute significantly to the occurrence of accidents. Computerized operator support systems, which process and structure information, can help operators during both normal and transient conditions, and thereby enhance safety and aid effective response to emergency conditions. An important operator aid being developed and described in this paper, is the safety parameter display system (SPDS). The SPDS is an event-independent, symptom-based operator aid for safety monitoring. Knowledge-based systems can provide operators with an improved quality of information. An information processing model of a knowledge based operator support system (KBOSS) developed for emergency conditions using an expert system shell is also presented. The paper concludes with a discussion of the design issues involved in the use of a knowledge based systems for real time safety monitoring and fault diagnosis. (author). 8 refs., 4 figs., 1 tab

Supporting the Support System: How Assessment and Communication Can Help Patients and Their Support Systems.

Science.gov (United States)

Harkey, Jane; Young, Jared; Carter, Jolynne Jo; Demoratz, Michael

The benefits of having a support system, such as social relationships with close friends and family, have been well documented for patients with serious health issues. As scientific evidence has shown, individuals who have the lowest level of involvement in social relationships face a greater mortality risk. Support systems, however, are not infallible. Relationship stress can have a negative impact on people-patient and caregiver alike-behaviorally, psychosocially, and physiologically. The purpose of this article is to encourage case managers who take a patient-centered approach to also consider the existence and extent of the support system, as well as any stresses or tensions that are observable within the support system. Although the case manager is ethically obliged to advocate for the individual receiving case management services, that advocacy can be extended to the support system for the good of all. This discussion applies to numerous case management practices and work settings including (but not limited to) hospital-based case management, home health, geriatrics, catastrophic case management, mental health, palliative care, and end of life/hospice. As part of the assessment phase of the case management process, case managers determine the extent of the patient's support system or social support network such as family and close friends. Although their advocacy is primarily for the patient receiving case management services, case managers also become aware of the needs of the support system members as they face their loved one's serious illness, severe injury, geriatric care demands, or end of life. Case managers can use their communication skills, especially motivational interviewing, with patients and their support systems to identify stresses and issues that can impact the pursuit of health goals. In addition, case managers ensure that individuals and their support systems are kept informed such as about the health condition, stage of disease, plan of

[Relation of psychological distress after diagnosis of gastric cancer at a cancer screening center with psychological support from public health nurses and family members].

Science.gov (United States)

Fukui, Sakiko; Ozawa, Harumi

2003-07-01

The objectives of this study were to examine the degree of psychological distress during the first 6 months after diagnosis of gastric cancer and investigate the relation to psychological support from public health nurses and family members. One hundred and five patients with stomach, colorectal, or esophagus cancer were mailed a questionnaire. They were asked questions concerning the level of shock on the day of diagnosis, at 1-week after the diagnosis, and at 6 months post diagnosis. In addition, their physical and psychological status was assessed at the 6-month time point. They were also asked about perceived psychological support from public health nurses and family members. The relation between psychological distress and such psychological support was then assessed using multiple regression analyses. The levels of shock on the day of diagnosis and after 1-week were both significantly related to the psychological support from public health nurses. Physical and psychological status at 6 months post diagnosis was significantly related to the level of psychological support from the patient's family members. The study revealed that psychological support from public health nurses improves the level of patient psychological distress during the first 1 week after the cancer diagnosis. Psychological support from family members facilitates the physical and psychological adjustment at 6 months post diagnosis. The results indicate that psychological support is important just after cancer diagnosis and for longer term adjustment, pointing to a major role of health care professionals alleviating problems associated with cancer diagnosis.

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.

A study on the applications of expert systems and neural networks for the development of operator support systems in nuclear power plants

International Nuclear Information System (INIS)

Cheon, Se Woo

1993-02-01

In order to assist operators in effectively maintaining plant safety and to enhance plant availability, the need to develop operator support systems is growing to increase. The application of both expert system and neural network technologies to the operator support has the potential to increase the performance of these systems. A prototype integrated operator support system, called NSSS-DS, has been developed for multiple alarm processing, plant trip diagnosis, and the failure diagnosis of three main systems (a rod control system, reactor coolant pumps (RCPs) and a pressurizer) in the primary side of the Kori-2 nuclear power plant. This system diagnoses system malfunction quickly and offers appropriate guidance to operators. The system uses rule-based deduction with certainty factor operation. Diagnosis is performed using an establish-refine inference strategy. This strategy is to match a set of symptoms with a specific malfunction hypothesis in a predetermined structure of possible hypotheses. The diagnostic symptoms include alarms, indication lamps, parameter values and valve lineup that can be acquired at a main control room. The overall plant-wide diagnosis is performed at the main control part which can process multiple alarms and diagnose possible failure modes and failed systems in the plant. The method of alarm processing is the object-oriented approach in which each alarm can be represented as an active data element, an object. The alarm processing is performed using alarm processing meta rules and alarm processing frames. Also, the diagnosis of a plant trip can be performed at the main control part. The specific diagnosis of the three main systems can be performed followed by the diagnostic results of the main control part. The system also provides follow-up treatments to the operators. The application to these systems is described from the point of view of diagnostic strategies. For the applications of the neural network technology, two feasibility

An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis

Directory of Open Access Journals (Sweden)

Jian-Hua Zhong

2016-01-01

Full Text Available Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM, to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.

Decision support system for diagnosis and treatment of hearing disorders the case of tinnitus

CERN Document Server

Tarnowska, Katarzyna A; Jastreboff, Pawel J

2017-01-01

The book presents a knowledge discovery based approach to build a recommender system supporting a physician in treating tinnitus patients with the highly successful method called Tinnitus Retraining Therapy. It describes experiments on extracting novel knowledge from the historical dataset of patients treated by Dr. P. Jastreboff so that to better understand factors behind therapy's effectiveness and better personalize treatments for different profiles of patients. The book is a response for a growing demand of an advanced data analytics in the healthcare industry in order to provide better care with the data driven decision-making solutions. The potential economic benefits of applying computerized clinical decision support systems include not only improved efficiency in health care delivery (by reducing costs, improving quality of care and patient safety), but also enhancement in treatment's standardization, objectivity and availability in places of scarce expert's knowledge on this difficult to treat hearin...

Detector design for active fault diagnosis in closed-loop systems

DEFF Research Database (Denmark)

Sekunda, André Krabdrup; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2018-01-01

Fault diagnosis of closed-loop systems is extremely relevant for high-precision equipment and safety critical systems. Fault diagnosis is usually divided into 2 schemes: active and passive fault diagnosis. Recent studies have highlighted some advantages of active fault diagnosis based on dual Youla......-Jabr-Bongiorno-Kucera parameters. In this paper, a method for closed-loop active fault diagnosis based on statistical detectors is given using dual Youla-Jabr-Bongiorno-Kucera parameters. The goal of this paper is 2-fold. First, the authors introduce a method for measuring a residual signal subject to white noise. Second...

The on-line beam control and diagnosis system of TARN

International Nuclear Information System (INIS)

Takanaka, M.; Watanabe, S.; Chiba, K.; Katayama, T.; Noda, A.

1982-04-01

The computer network in TARN is composed of a central main frame computer, two different minicomputers and several microprocessors. It has been used for the beam control and the beam diagnosis; support for adjustment of elements of the transport line and the ring, generation of RF voltage function, measurement of beam profile at RF stacking, on-line measurement of ν:value, and observation of Schottky signal. By the use of this computer system, the operation of TARN has been effectively and steadily performed, and additionally it has contributed to measuring the beam characteristics precisely in the ring. (author)

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

Data monitoring system of technical diagnosis system for EAST

International Nuclear Information System (INIS)

Qian Jing; Weng Peide; Chen Zhuomin; Wu Yu; Xi Weibin; Luo Jiarong

2010-01-01

Technical diagnosis system (TDS) is an important subsystem to monitor status parameters of EAST (experimental advanced superconducting tokamak). The upgraded TDS data monitoring system is comprised of management floor, monitoring floor and field floor.. Security protection, malfunction record and analysis are designed to make the system stable, robust and friendly. During the past EAST campaigns, the data monitoring system has been operated reliably and stably. The signal conditioning system and software architecture are described. (authors)

failures diagnosis. Theory and practice for industrial systems

International Nuclear Information System (INIS)

Zwingelstein, G.

1995-01-01

Failure diagnosis methods represent appreciable tools for the maintenance and the improvement of availability and safety in complex industrial installations. The industrial diagnosis can be assimilated to a deterministic causality relation between the cause and the effect. This book describes the methodology associated to the resolution of the diagnosis problem applied to complex industrial system failures, and evaluates the principles of the main diagnosis methods. The introduction presents the terminology and norms used in the industry to situate the diagnosis context in the possession cost of a product. After a formulation of the diagnosis in the form of the resolution of inverse problems, the author gives details about the inductive and deductive methods and about internal and external diagnosis methods. Each method is illustrated with examples taken in the industry with recommendations about their operating limitations. Finally, a guideline summarizes the principal criteria for the selection of an industrial diagnosis method according to the available informations. (J.S.). 168 refs., 294 figs., 22 tabs., 1 annexe

[A computer-aided image diagnosis and study system].

Science.gov (United States)

Li, Zhangyong; Xie, Zhengxiang

2004-08-01

The revolution in information processing, particularly the digitizing of medicine, has changed the medical study, work and management. This paper reports a method to design a system for computer-aided image diagnosis and study. Combined with some good idea of graph-text system and picture archives communicate system (PACS), the system was realized and used for "prescription through computer", "managing images" and "reading images under computer and helping the diagnosis". Also typical examples were constructed in a database and used to teach the beginners. The system was developed by the visual developing tools based on object oriented programming (OOP) and was carried into operation on the Windows 9X platform. The system possesses friendly man-machine interface.

A fundamental study on nuclear power plant diagnosis system

International Nuclear Information System (INIS)

Yoshimura, Sei-ichi; Fujimoto, Junzo

1987-01-01

Diagnosis of nuclear power plant is a large application field of knowledge engineering. But, the study examples are few and the diagnosis method is not established yet. This report describes the diagnosis method using cross correlation coefficients and describes the knowledge acquisition method of undefined transients in order to enhance the system performance. The usefulness of the system was verified by putting some data into the system. Main results are as follows. (1) Diagnosis method. Some transients are selected by the first judgement and one of them is identified by the second judgement using the cross correlation. (2) Knowledge aquisition method. When putting new data into the knowledge-base, the system indicates the inconsistency by arranging the aquired data, and the operators input new transient names and corresponding manipulation methods after analyzing the indicated results. (3) Usefulness of the system. Freedwater controller failures(2 transients), 2 recirculation pumps trip and a dummy datum combined 2 transients(one is feedwater controller failure and one is 2 recirculation pumps trip) were put into the system. It was proved that the system identified the transients correctly and it indicated the first hit and the inconsisency of the transients in the course of knowledge acquisition. (author)

Information system for diagnosis of respiratory system diseases

Science.gov (United States)

Abramov, G. V.; Korobova, L. A.; Ivashin, A. L.; Matytsina, I. A.

2018-05-01

An information system is for the diagnosis of patients with lung diseases. The main problem solved by this system is the definition of the parameters of cough fragments in the monitoring recordings using a voice recorder. The authors give the recognition criteria of recorded cough moments, audio records analysis. The results of the research are systematized. The cough recognition system can be used by the medical specialists to diagnose the condition of the patients and to monitor the process of their treatment.

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...

Multilevel flow models studio: human-centralized development for operation support system

International Nuclear Information System (INIS)

Zhou Yangping; Hidekazu Yoshikawa; Liu Jingquan; Yang Ming; Ouyang Jun

2005-01-01

Computerized Operation Support Systems (COSS), integrating Artificial Intelligence, Multimedia and Network Technology, are now being proposed for reducing operator's cognitive load for process operation. This study proposed a Human-Centralized Development (HCD) that COSS can be developed and maintained independently, conveniently and flexibly by operator and expert of industry system with little expertise on software development. A graphical interface system for HCD, Multilevel Flow Models Studio (MFMS), is proposed for development assistance of COSS. An Extensible Markup Language based file structure is designed to represent the Multilevel Flow Models (MFM) model for the target system. With a friendly graphical interface, MFMS mainly consists of two components: 1) an editor to intelligently assist user establish and maintain the MFM model; 2) an executor to implement the application for monitoring, diagnosis and operational instruction in terms of the established MFM model. A prototype MFMS system has been developed and applied to construct a trial operation support system for a Nuclear Power Plant simulated by RELAP5/MOD2. (authors)

Fault Diagnosis of Power Systems Using Intelligent Systems

Science.gov (United States)

Momoh, James A.; Oliver, Walter E. , Jr.

1996-01-01

The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time Al-based fault diagnosis tool. Such a tool will allow NASA ground controllers to re-establish a normal or near-normal degraded operating state of the EPS (a DC power system) for Space Station Alpha by isolating the faulted branches and loads of the system. And after isolation, re-energizing those branches and loads that have been found not to have any faults in them. A proposed solution involves using the Fault Diagnosis Intelligent System (FDIS) to perform near-real time fault diagnosis of Alpha's EPS by downloading power transient telemetry at fault-time from onboard data loggers. The FDIS uses an ANN clustering algorithm augmented with a wavelet transform feature extractor. This combination enables this system to perform pattern recognition of the power transient signatures to diagnose the fault type and its location down to the orbital replaceable unit. FDIS has been tested using a simulation of the LeRC Testbed Space Station Freedom configuration including the topology from the DDCU's to the electrical loads attached to the TPDU's. FDIS will work in conjunction with the Power Management Load Scheduler to determine what the state of the system was at the time of the fault condition. This information is used to activate the appropriate diagnostic section, and to refine if necessary the solution obtained. In the latter case, if the FDIS reports back that it is equally likely that the faulty device as 'start tracker #1' and 'time generation unit,' then based on a priori knowledge of the system's state, the refined solution would be 'star tracker #1' located in cabinet ITAS2. It is concluded from the present studies that artificial intelligence diagnostic abilities are improved with the addition of the wavelet transform, and that when such a system such as FDIS is coupled to the Power Management Load Scheduler, a faulty device can be located and isolated

CT diagnosis of congenital anomalies of the central nervous system

International Nuclear Information System (INIS)

Mori, Koreaki

1980-01-01

In the diagnosis of central nervous system congenital anomalies, understanding of embryology of the central nervous system and pathophysiology of each anomaly are essential. It is important for clinical approach to central nervous system congenital anomalies to evaluate the size of the head and tention of the anterior fontanelle. Accurate diagnosis of congenital anomalies depends on a correlation of CT findings to clinical pictures. Clinical diagnosis of congenital anomalies should include prediction of treatability and prognosis, in addition to recognition of a disease. (author)

Diagnosis of feline infectious peritonitis: Update on evidence supporting available tests.

Science.gov (United States)

Tasker, Séverine

2018-03-01

Practical relevance: Feline coronavirus (FCoV) infection is very common in cats, usually causing only mild intestinal signs such as diarrhoea. Up to 10% of FCoV infections, however, result in the fatal disease feline infectious peritonitis (FIP). Clinical challenges: Obtaining a definitive diagnosis of FIP based on non-invasive approaches is difficult. Confirmation of the disease relies on finding appropriate cytological or histopathological changes in association with positive immunostaining for FCoV antigen. In FIP cases with effusions, cytology and immunostaining on effusion samples can be relatively easy to perform; otherwise obtaining diagnostic samples is more challenging and collection of biopsies from tissues with gross lesions is necessary. In the absence of a definitive diagnosis, a high index of suspicion of FIP may be obtained from the cat's signalment and history, combined with findings on clinical examination and laboratory test results. If largely consistent with FIP, these can be used as a basis for discussion with the owner about whether additional, more invasive, diagnostic tests are warranted. In some cases it may be that euthanasia is discussed as an alternative to pursuing a definitive diagnosis ante-mortem, especially if financial limitations exist or where there are concerns over a cat's ability to tolerate invasive diagnostic procedures. Ideally, the diagnosis should be confirmed in such patients from samples taken at post-mortem examination. Global importance: FIP occurs wherever FCoV infection is present in cats, which equates to most parts of the world. Evidence base: This review provides a comprehensive overview of how to approach the diagnosis of FIP, focusing on the tests available to the veterinary practitioner and recently published evidence supporting their use.

An expert system in medical diagnosis

International Nuclear Information System (INIS)

Raboanary, R.; Raoelina Andriambololona; Soffer, J.; Raboanary, J.

2001-01-01

Health problem is still a crucial one in some countries. It is so important that it becomes a major handicap in economic and social development. In order to solve this problem, we have conceived an expert system that we called MITSABO, which means TO HEAL, to help the physicians to diagnose tropical diseases. It is clear that by extending the data base and the knowledge base, we can extend the application of the software to more general areas. In our expert system, we used the concept of 'self organization' of neural network based on the determination of the eigenvalues and the eigenvectors associated to the correlation matrix XX t . The projection of the data on the two first eigenvectors gives a classification of the diseases which is used to get a first approach in the diagnosis of the patient. This diagnosis is improved by using an expert system which is built from the knowledge base.

Tuberculosis-Diagnostic Expert System: an architecture for translating patients information from the web for use in tuberculosis diagnosis.

Science.gov (United States)

Osamor, Victor C; Azeta, Ambrose A; Ajulo, Oluseyi O

2014-12-01

Over 1.5-2 million tuberculosis deaths occur annually. Medical professionals are faced with a lot of challenges in delivering good health-care with unassisted automation in hospitals where there are several patients who need the doctor's attention. To automate the pre-laboratory screening process against tuberculosis infection to aid diagnosis and make it fast and accessible to the public via the Internet. The expert system we have built is designed to also take care of people who do not have access to medical experts, but would want to check their medical status. A rule-based approach has been used, and unified modeling language and the client-server architecture technique were applied to model the system and to develop it as a web-based expert system for tuberculosis diagnosis. Algorithmic rules in the Tuberculosis-Diagnosis Expert System necessitate decision coverage where tuberculosis is either suspected or not suspected. The architecture consists of a rule base, knowledge base, and patient database. These units interact with the inference engine, which receives patient' data through the Internet via a user interface. We present the architecture of the Tuberculosis-Diagnosis Expert System and its implementation. We evaluated it for usability to determine the level of effectiveness, efficiency and user satisfaction. The result of the usability evaluation reveals that the system has a usability of 4.08 out of a scale of 5. This is an indication of a more-than-average system performance. Several existing expert systems have been developed for the purpose of supporting different medical diagnoses, but none is designed to translate tuberculosis patients' symptomatic data for online pre-laboratory screening. Our Tuberculosis-Diagnosis Expert System is an effective solution for the implementation of the needed web-based expert system diagnosis. © The Author(s) 2013.

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.

Decision Support System for Age-Related Macular Degeneration Using Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Mostafa Langarizadeh

2017-09-01

Full Text Available Introduction: Age-related macular degeneration (AMD is one of the major causes of visual loss among the elderly. It causes degeneration of cells in the macula. Early diagnosis can be helpful in preventing blindness. Drusen are the initial symptoms of AMD. Since drusen have a wide variety, locating them in screening images is difficult and time-consuming. An automated digital fundus photography-based screening system help overcome such drawbacks. The main objective of this study was to suggest a novel method to classify AMD and normal retinal fundus images. Materials and Methods: The suggested system was developed using convolutional neural networks. Several methods were adopted for increasing data such as horizontal reflection, random crop, as well as transfer and combination of such methods. The suggested system was evaluated using images obtained from STARE database and a local dataset. Results: The local dataset contained 3195 images (2070 images of AMD suspects and 1125 images of healthy retina and the STARE dataset comprised of 201 images (105 images of AMD suspects and 96 images of healthy retina. According to the results, the accuracies of the local and standard datasets were 0.95 and 0.81, respectively. Conclusion: Diagnosis and screening of AMD is a time-consuming task for specialists. To overcome this limitation, we attempted to design an intelligent decision support system for the diagnosis of AMD fundus using retina images. The proposed system is an important step toward providing a reliable tool for supervising patients. Early diagnosis of AMD can lead to timely access to treatment.

The Evolution of Devices and Systems Supporting Rehabilitation of Lower Limbs

Science.gov (United States)

Olinski, M.; Lewandowski, B.; Gronowicz, A.

2015-02-01

This paper presents the process of development, as well as examples of devices and systems supporting rehabilitation of the human lower extremities, developed independently over the years in many parts of the world. Particular emphasis was placed on indicating, which major groups of devices supporting kinesitherapy of the lower limbs can be distinguished, what are the important advantages and disadvantages of particular types of solutions, as well as what directions currently dominating in development of rehabilitation systems may be specified. A deeper analysis and comparison of several selected systems was also conducted, resulting in gathering the outcomes in two tables. They focused on a few features of mechanical design, especially the devices' kinematic structures, and devices' additional functions associated with, among others, interaction, as well as diagnosis of the limb's state and the progress of rehabilitation.

Data-driven process monitoring and diagnosis with support vector data description

OpenAIRE

Tafazzoli Moghaddam, Esmaeil

2011-01-01

This thesis targets the problem of fault diagnosis of industrial processes with data-drivenapproaches. In this context, a class of problems are considered in which the only informationabout the process is in the form of data and no model is available due to complexity of theprocess. Support vector data description is a kernel based method recently proposed in the fieldof pattern recognition and it is known for its powerful capabilities in nonlinear data classificationwhich can be exploited in...

Diagnosis of nutrient imbalances with vector analysis in agroforestry systems.

Science.gov (United States)

Isaac, Marney E; Kimaro, Anthony A

2011-01-01

Agricultural intensification has had unintended environmental consequences, including increased nutrient leaching and surface runoff and other agrarian-derived pollutants. Improved diagnosis of on-farm nutrient dynamics will have the advantage of increasing yields and will diminish financial and environmental costs. To achieve this, a management support system that allows for site-specific rapid evaluation of nutrient production imbalances and subsequent management prescriptions is needed for agroecological design. Vector diagnosis, a bivariate model to depict changes in yield and nutritional response simultaneously in a single graph, facilitates identification of nutritional status such as growth dilution, deficiency, sufficiency, luxury uptake, and toxicity. Quantitative data from cocoa agroforestry systems and pigeonpea intercropping trials in Ghana and Tanzania, respectively, were re-evaluated with vector analysis. Relative to monoculture, biomass increase in cocoa ( L.) under shade (35-80%) was accompanied by a 17 to 25% decline in P concentration, the most limiting nutrient on this site. Similarly, increasing biomass with declining P concentrations was noted for pigeonpea [ (L). Millsp.] in response to soil moisture availability under intercropping. Although vector analysis depicted nutrient responses, the current vector model does not consider non-nutrient resource effects on growth, such as ameliorated light and soil moisture, which were particularly active in these systems. We revisit and develop vector analysis into a framework for diagnosing nutrient and non-nutrient interactions in agroforestry systems. Such a diagnostic technique advances management decision-making by increasing nutrient precision and reducing environmental issues associated with agrarian-derived soil contamination. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

Directory of Open Access Journals (Sweden)

Chin-Tsung Hsieh

2014-01-01

Full Text Available The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

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.

Global and local consistencies in distributed fault diagnosis for discrete-event systems

NARCIS (Netherlands)

Su, R.; Wonham, W.M.

2005-01-01

In this paper, we present a unified framework for distributed diagnosis. We first introduce the concepts of global and local consistency in terms of supremal global and local supports, then present two distributed diagnosis problems based on them. After that, we provide algorithms to achieve

Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping

Science.gov (United States)

2012-01-01

Background Meningitis is characterized by an inflammation of the meninges, or the membranes surrounding the brain and spinal cord. Early diagnosis and treatment is crucial for a positive outcome, yet identifying meningitis is a complex process involving an array of signs and symptoms and multiple causal factors which require novel solutions to support clinical decision-making. In this work, we explore the potential of fuzzy cognitive map to assist in the modeling of meningitis, as a support tool for physicians in the accurate diagnosis and treatment of the condition. Methods Fuzzy cognitive mapping (FCM) is a method for analysing and depicting human perception of a given system. FCM facilitates the development of a conceptual model which is not limited by exact values and measurements and thus is well suited to representing relatively unstructured knowledge and associations expressed in imprecise terms. A team of doctors (physicians), comprising four paediatricians, was formed to define the multifarious signs and symptoms associated with meningitis and to identify risk factors integral to its causality, as indicators used by clinicians to identify the presence or absence of meningitis in patients. The FCM model, consisting of 20 concept nodes, has been designed by the team of paediatricians in collaborative dialogue with the research team. Results The paediatricians were supplied with a form containing various input parameters to be completed at the time of diagnosing meningitis among infants and children. The paediatricians provided information on a total of 56 patient cases amongst children whose age ranged from 2 months to 7 years. The physicians’ decision to diagnose meningitis was available for each individual case which was used as the outcome measure for evaluating the model. The FCM was trained using 40 cases with an accuracy of 95%, and later 16 test cases were used to analyze the accuracy and reliability of the model. The system produced the results

Automatic fault diagnosis in PV systems with distributed MPPT

International Nuclear Information System (INIS)

Solórzano, J.; Egido, M.A.

2013-01-01

Highlights: • An automatic failure diagnosis procedure for PV systems with DMPPT is presented. • The different failures diagnosed and their effects on the PV systems are described. • No use of irradiance and temperature sensors decreasing the cost of the system. • Voltage and current analysis to diagnose different failures. • Hot-spots, localized dirt, shading, module degradation and cable losses diagnosis. - Abstract: This work presents a novel procedure for fault diagnosis in PV systems with distributed maximum power point tracking at module level—power optimizers (DC/DC) or micro-inverters (DC/AC). Apart from the power benefits obtained when an irregular irradiance distribution is present, this type of systems permit the monitoring of the PV plant parameters at the module level: voltage and current at the working power point. With these parameters, a prototype diagnosis tool has been developed in Matlab and it has been experimentally verified in a real rooftop PV generator by applying different failures. The tool can diagnose the following failures: fixed object shading (with distance estimation), localized dirt, generalized dirt, possible hot-spots, module degradation and excessive losses in DC cables. In addition, it alerts the user of the power losses produced by each failure and classifies the failures by their severity. This system does not require the use of irradiance or temperature sensors, except for the generalized dirt failure, reducing the cost of installation, especially important in small PV systems

Computerized Operator Support System – Phase II Development

Energy Technology Data Exchange (ETDEWEB)

Ulrich, Thomas A.; Boring, Ronald L.; Lew, Roger T.; Thomas, Kenneth D.

2015-02-01

A computerized operator support system (COSS) prototype for nuclear control room process control is proposed and discussed. The COSS aids operators in addressing rapid plant upsets that would otherwise result in the shutdown of the power plant and interrupt electrical power generation, representing significant costs to the owning utility. In its current stage of development the prototype demonstrates four advanced functions operators can use to more efficiently monitor and control the plant. These advanced functions consist of: (1) a synthesized and intuitive high level overview display of system components and interrelations, (2) an enthalpy-based mathematical chemical and volume control system (CVCS) model to detect and diagnose component failures, (3) recommended strategies to mitigate component failure effects and return the plant back to pre-fault status, and (4) computer-based procedures to walk the operator through the recommended mitigation actions. The COSS was demonstrated to a group of operators and their feedback was collected. The operators responded positively to the COSS capabilities and features and indicated the system would be an effective operator aid. The operators also suggested several additional features and capabilities for the next iteration of development. Future versions of the COSS prototype will include additional plant systems, flexible computer-based procedure presentation formats, and support for simultaneous component fault diagnosis and dual fault synergistic mitigation action strategies to more efficiently arrest any plant upsets.

Fault diagnosis for discrete event systems: Modelling and verification

International Nuclear Information System (INIS)

Simeu-Abazi, Zineb; Di Mascolo, Maria; Knotek, Michal

2010-01-01

This paper proposes an effective way for diagnosis of discrete-event systems using a timed-automaton. It is based on the model-checking technique, thanks to time analysis of the timed model. The paper proposes a method to construct all the timed models and details the different steps used to obtain the diagnosis path. A dynamic model with temporal transitions is proposed in order to model the system. By 'dynamical model', we mean an extension of timed automata for which the faulty states are identified. The model of the studied system contains the faultless functioning states and all the faulty states. Our method is based on the backward exploitation of the dynamic model, where all possible reverse paths are searched. The reverse path is the connection of the faulty state to the initial state. The diagnosis method is based on the coherence between the faulty occurrence time and the reverse path length. A real-world batch process is used to demonstrate the modelling steps and the proposed backward time analysis method to reach the diagnosis results.

LORD: a phenotype-genotype semantically integrated biomedical data tool to support rare disease diagnosis coding in health information systems.

Science.gov (United States)

Choquet, Remy; Maaroufi, Meriem; Fonjallaz, Yannick; de Carrara, Albane; Vandenbussche, Pierre-Yves; Dhombres, Ferdinand; Landais, Paul

Characterizing a rare disease diagnosis for a given patient is often made through expert's networks. It is a complex task that could evolve over time depending on the natural history of the disease and the evolution of the scientific knowledge. Most rare diseases have genetic causes and recent improvements of sequencing techniques contribute to the discovery of many new diseases every year. Diagnosis coding in the rare disease field requires data from multiple knowledge bases to be aggregated in order to offer the clinician a global information space from possible diagnosis to clinical signs (phenotypes) and known genetic mutations (genotype). Nowadays, the major barrier to the coding activity is the lack of consolidation of such information scattered in different thesaurus such as Orphanet, OMIM or HPO. The Linking Open data for Rare Diseases (LORD) web portal we developed stands as the first attempt to fill this gap by offering an integrated view of 8,400 rare diseases linked to more than 14,500 signs and 3,270 genes. The application provides a browsing feature to navigate through the relationships between diseases, signs and genes, and some Application Programming Interfaces to help its integration in health information systems in routine.

Phronesis, a diagnosis and recovery tool for system administrators

International Nuclear Information System (INIS)

Haen, C; Barra, V; Bonaccorsi, E; Neufeld, N

2014-01-01

The LHCb experiment relies on the Online system, which includes a very large and heterogeneous computing cluster. Ensuring the proper behavior of the different tasks running on the more than 2000 servers represents a huge workload for the small operator team and is a 24/7 task. At CHEP 2012, we presented a prototype of a framework that we designed in order to support the experts. The main objective is to provide them with steadily improving diagnosis and recovery solutions in case of misbehavior of a service, without having to modify the original applications. Our framework is based on adapted principles of the Autonomic Computing model, on Reinforcement Learning algorithms, as well as innovative concepts such as Shared Experience. While the submission at CHEP 2012 showed the validity of our prototype on simulations, we here present an implementation with improved algorithms and manipulation tools, and report on the experience gained with running it in the LHCb Online system.

Remote diagnosis as used for mechanized parking systems

Science.gov (United States)

Humberg, Heinz; Maeder, Hans Friedrich; Will, Frank

1992-10-01

The new possibilities offered by worldwide data transmission networks, which are being used for the remote diagnosis of mechanized parking systems are discussed. This has led to a reduction in service costs for systems installed in Asia and elsewhere. The principles of the mechanized multistorey car park and their control concept are described. The parking facilities are fully geared up for remote diagnosis, the key functions of which are: data collection, data storage, data transmission, and data evaluation. The reports transmitted from the parking facility are analyzed using an evaluation system. The objectives are to detect impending component failures and to quickly identify the causes of irregularities which have occurred. The evaluation system can be easily adapted for other applications.

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.

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...

Monodetector system for diagnosis (DETEC)

International Nuclear Information System (INIS)

Alonso Abad, D.; Fernandez Paz, J.L.; Lopez Torres, O.M. and others

1997-01-01

Several clinical searches can be done using The Single Probe Diagnosis System: Thyroid uptake, Eritroferrocinetic studies, Studies of survival of hematite's, Studies of peripheral vascular diseases , Studies of gastric emptying time. The system can be set spectrometric parameters for several radionuclides ( 131I , 125I , 99mT c, 59F e, 51C r, 57G a, 57C o) used in Nuclear Medicine by itself. It is a unit made of a mechanical structure and a detection-measured system based in a Z80 microprocessor. Data obtained are processed and can be printed or sent to a P C by RS-232 protocol

New scoring system for intra-abdominal injury diagnosis after blunt trauma.

Science.gov (United States)

Shojaee, Majid; Faridaalaee, Gholamreza; Yousefifard, Mahmoud; Yaseri, Mehdi; Arhami Dolatabadi, Ali; Sabzghabaei, Anita; Malekirastekenari, Ali

2014-01-01

An accurate scoring system for intra-abdominal injury (IAI) based on clinical manifestation and examination may decrease unnecessary CT scans, save time, and reduce healthcare cost. This study is designed to provide a new scoring system for a better diagnosis of IAI after blunt trauma. This prospective observational study was performed from April 2011 to October 2012 on patients aged above 18 years and suspected with blunt abdominal trauma (BAT) admitted to the emergency department (ED) of Imam Hussein Hospital and Shohadaye Hafte Tir Hospital. All patients were assessed and treated based on Advanced Trauma Life Support and ED protocol. Diagnosis was done according to CT scan findings, which was considered as the gold standard. Data were gathered based on patient's history, physical exam, ultrasound and CT scan findings by a general practitioner who was not blind to this study. Chi-square test and logistic regression were done. Factors with significant relationship with CT scan were imported in multivariate regression models, where a coefficient (β) was given based on the contribution of each of them. Scoring system was developed based on the obtained total β of each factor. Altogether 261 patients (80.1% male) were enrolled (48 cases of IAI). A 24-point blunt abdominal trauma scoring system (BATSS) was developed. Patients were divided into three groups including low (scoretool for BAT detection and has the potential to reduce unnecessary CT scan and cut unnecessary costs.

General knowledge structure for diagnosis

International Nuclear Information System (INIS)

Steinar Brendeford, T.

1996-01-01

At the OECD Halden Reactor Project work has been going on for several years in the field of automatic fault diagnosis for nuclear power plants. Continuing this work, studies are now carried out to combine different diagnostic systems within the same framework. The goal is to establish a general knowledge structure for diagnosis applied to a NPP process. Such a consistent and generic storage of knowledge will lighten the task of combining different diagnosis techniques. An integration like this is expected to increase the robustness and widen the scope of the diagnosis. Further, verification of system reliability and on-line explanations of hypotheses can be helped. Last but not least there is a potential in reuse of both specific and generic knowledge. The general knowledge framework is also a prerequisite for a successful integration of computerized operator support systems within the process supervision and control complex. Consistency, verification and reuse are keywords also in this respect. Systems that should be considered for integration are; automatic control, computerized operator procedures, alarm - and alarm filtering, signal validation, diagnosis and condition based maintenance. This paper presents three prototype diagnosis systems developed at the OECD Halden Reactor Project. A software arrangement for process simulation with these three systems attached in parallel is briefly described. The central part of this setup is a 'blackboard' system to be used for representing shared knowledge. Examples of such knowledge representations are included in the paper. The conclusions so far in this line of work are only tentative. The studies of existing methodologies for diagnosis, however, show a potential for several generalizations to be made in knowledge representation and use. (author). 14 refs, 6 figs

General knowledge structure for diagnosis

Energy Technology Data Exchange (ETDEWEB)

Steinar Brendeford, T [Institutt for Energiteknikk, Halden (Norway). OECD Halden Reaktor Projekt

1997-12-31

At the OECD Halden Reactor Project work has been going on for several years in the field of automatic fault diagnosis for nuclear power plants. Continuing this work, studies are now carried out to combine different diagnostic systems within the same framework. The goal is to establish a general knowledge structure for diagnosis applied to a NPP process. Such a consistent and generic storage of knowledge will lighten the task of combining different diagnosis techniques. An integration like this is expected to increase the robustness and widen the scope of the diagnosis. Further, verification of system reliability and on-line explanations of hypotheses can be helped. Last but not least there is a potential in reuse of both specific and generic knowledge. The general knowledge framework is also a prerequisite for a successful integration of computerized operator support systems within the process supervision and control complex. Consistency, verification and reuse are keywords also in this respect. Systems that should be considered for integration are; automatic control, computerized operator procedures, alarm - and alarm filtering, signal validation, diagnosis and condition based maintenance. This paper presents three prototype diagnosis systems developed at the OECD Halden Reactor Project. A software arrangement for process simulation with these three systems attached in parallel is briefly described. The central part of this setup is a `blackboard` system to be used for representing shared knowledge. Examples of such knowledge representations are included in the paper. The conclusions so far in this line of work are only tentative. The studies of existing methodologies for diagnosis, however, show a potential for several generalizations to be made in knowledge representation and use. (author). 14 refs, 6 figs.

Active fault diagnosis in closed-loop systems

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2005-01-01

Active fault diagnosis (AFD) of parametric faults is considered in connection with closed loop feedback systems. AFD involves auxiliary signals applied on the closed loop system. A fault signature matrix is introduced in connection with AFD and it is shown that if a limited number of faults can...

Water quality diagnosis system

International Nuclear Information System (INIS)

Nagase, Makoto; Asakura, Yamato; Sakagami, Masaharu

1989-01-01

By using a model representing a relationship between the water quality parameter and the dose rate in primary coolant circuits of a water cooled reactor, forecasting for the feature dose rate and abnormality diagnosis for the water quality are conducted. The analysis model for forecasting the reactor water activity or the dose rate receives, as the input, estimated curves for the forecast Fe, Ni, Co concentration in feedwater or reactor water pH, etc. from the water quality data in the post and forecasts the future radioactivity or dose rate in the reactor water. By comparing the result of the forecast and the setting value such as an aimed value, it can be seen whether the water quality at present or estimated to be changed is satisfactory or not. If the quality is not satisfactory, it is possible to take an early countermeasure. Accordingly, the reactor water activity and the dose rate can be kept low. Further, the basic system constitution, diagnosis algorithm, indication, etc. are identical between BWR and PWR reactors, except for only the difference in the mass balance. (K.M.)

Development of maintenance support system using portable device and mobile agent

International Nuclear Information System (INIS)

Sato, Hisashi; Ito, Yo; Takahashi, Makoto; Kitamura, Masaharu; Ohi, Tadashi; Wu, Wei

2004-01-01

The framework of intelligent support system for the maintenance of nuclear power plant is proposed in this paper with emphasis on the combined use of a portable device and intelligent information processing. The purpose of this system is the realization of flexible inspection process and effective diagnosis process to be performed on-site. The prototype system has been implemented for the experimental facility with mobile-agent technology and PDA (personal digital assistant) to show the basic functionality of the proposed framework. The results of the scenario-based and function-based evaluation showed that the proposed framework is effective for the data management for the maintenance activities. (author)

Portable multispectral imaging system for oral cancer diagnosis

Science.gov (United States)

Hsieh, Yao-Fang; Ou-Yang, Mang; Lee, Cheng-Chung

2013-09-01

This study presents the portable multispectral imaging system that can acquire the image of specific spectrum in vivo for oral cancer diagnosis. According to the research literature, the autofluorescence of cells and tissue have been widely applied to diagnose oral cancer. The spectral distribution is difference for lesions of epithelial cells and normal cells after excited fluorescence. We have been developed the hyperspectral and multispectral techniques for oral cancer diagnosis in three generations. This research is the third generation. The excited and emission spectrum for the diagnosis are acquired from the research of first generation. The portable system for detection of oral cancer is modified for existing handheld microscope. The UV LED is used to illuminate the surface of oral cavity and excite the cells to produce fluorescent. The image passes through the central channel and filters out unwanted spectrum by the selection of filter, and focused by the focus lens on the image sensor. Therefore, we can achieve the specific wavelength image via fluorescence reaction. The specificity and sensitivity of the system are 85% and 90%, respectively.

Patient support systems

International Nuclear Information System (INIS)

Braden, A.B.; McBride, T.R.; Styblo, D.J.; Taylor, S.K.; Richey, J.B.

1979-01-01

A patient support system for use in computerized tomography (CT) is described. The system is particularly useful for CT scanning of the brain and also of the abdominal area. The support system consists of two moveable tables which may be translated into position for X-ray scanning of the patient's body and which may be translated incrementally and automatically to obtain scans at adjacent locations. For use with brain scans, the second table is replaced by a detachable restraint assembly which is described in detail. The support system is so designed that only a small volume of low density material will intercept the X-ray beam. (UK)

Support vector machine based diagnostic system for breast cancer using swarm intelligence.

Science.gov (United States)

Chen, Hui-Ling; Yang, Bo; Wang, Gang; Wang, Su-Jing; Liu, Jie; Liu, Da-You

2012-08-01

Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in

[Central nervous system involvement in systemic lupus erythematosus - diagnosis and therapy].

Science.gov (United States)

Szmyrka, Magdalena

Nervous system involvement in lupus belongs to its severe complications and significantly impacts its prognosis. Neuropsychiatric lupus includes 19 disease manifestations concerning both central and peripheral nervous system. This paper presents clinical aspects of central nervous system involvement in lupus. It reviews its epidemiology, risk factors and principles of diagnosis and therapy.

Diagnosis of dynamic systems based on explicit and implicit behavioural models: an application to gas turbines in Esprit Project Tiger

Energy Technology Data Exchange (ETDEWEB)

Trave-Massuyes, L. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Milne, R.

1995-12-31

We are interested in the monitoring and diagnosis of dynamic systems. In our work, we are combining explicit temporal models of the behaviour of a dynamic system with implicit behavioural models supporting model based approaches. This work is drive by the needs of and applied to, two gas turbines of very different size and power. In this paper we describe the problems of building systems for these domains and illustrate how we have developed a system where these two approaches complement each other to provide a comprehensive fault detection and diagnosis system. We also explore the strengths and weaknesses of each approach. The work described here is currently working continuously, on line to a gas turbine in a major chemical plant. (author) 24 refs.

Diagnosis of dynamic systems based on explicit and implicit behavioural models: an application to gas turbines in Esprit Project Tiger

Energy Technology Data Exchange (ETDEWEB)

Trave-Massuyes, L [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Milne, R

1996-12-31

We are interested in the monitoring and diagnosis of dynamic systems. In our work, we are combining explicit temporal models of the behaviour of a dynamic system with implicit behavioural models supporting model based approaches. This work is drive by the needs of and applied to, two gas turbines of very different size and power. In this paper we describe the problems of building systems for these domains and illustrate how we have developed a system where these two approaches complement each other to provide a comprehensive fault detection and diagnosis system. We also explore the strengths and weaknesses of each approach. The work described here is currently working continuously, on line to a gas turbine in a major chemical plant. (author) 24 refs.

Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

Directory of Open Access Journals (Sweden)

Hong-Chan Chang

2014-01-01

Full Text Available This study endeavors to develop a cloud monitoring system for solar plants. This system incorporates numerous subsystems, such as a geographic information system, an instantaneous power-consumption information system, a reporting system, and a failure diagnosis system. Visual C# was integrated with ASP.NET and SQL technologies for the proposed monitoring system. A user interface for database management system was developed to enable users to access solar power information and management systems. In addition, by using peer-to-peer (P2P streaming technology and audio/video encoding/decoding technology, real-time video data can be transmitted to the client end, providing instantaneous and direct information. Regarding smart failure diagnosis, the proposed system employs the support vector machine (SVM theory to train failure mathematical models. The solar power data are provided to the SVM for analysis in order to determine the failure types and subsequently eliminate failures at an early stage. The cloud energy-management platform developed in this study not only enhances the management and maintenance efficiency of solar power plants but also increases the market competitiveness of solar power generation and renewable energy.

An intelligent medical system for diagnosis of bone diseases

International Nuclear Information System (INIS)

Hatzilygeroudis, I.; Vassilakos, P.J.; Tsakalidis, A.

1994-01-01

In this paper, aspects of the design of an intelligent medical system for diagnosis of bone diseases that can be detected by scintigraphic images are presented. The system comprises three major parts: a user interface (UI), a database management system (DBMS), and an expert system (ES). The DBMS is used for manipulation of various patient data. A number of patient cases are selected as prototype and stored in separate database. Diagnosis is performed via the ES, called XBONE, based on patient data. Knowledge is represented via an integrated formalism that combines production rules and a neural network. This results in better representation, and facilitates knowledge acquisition and maintenance. (authors)

Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

Directory of Open Access Journals (Sweden)

Cheng-Wei Fei

2014-01-01

Full Text Available To improve the diagnosis capacity of rotor vibration fault in stochastic process, an effective fault diagnosis method (named Process Power Spectrum Entropy (PPSE and Support Vector Machine (SVM (PPSE-SVM, for short method was proposed. The fault diagnosis model of PPSE-SVM was established by fusing PPSE method and SVM theory. Based on the simulation experiment of rotor vibration fault, process data for four typical vibration faults (rotor imbalance, shaft misalignment, rotor-stator rubbing, and pedestal looseness were collected under multipoint (multiple channels and multispeed. By using PPSE method, the PPSE values of these data were extracted as fault feature vectors to establish the SVM model of rotor vibration fault diagnosis. From rotor vibration fault diagnosis, the results demonstrate that the proposed method possesses high precision, good learning ability, good generalization ability, and strong fault-tolerant ability (robustness in four aspects of distinguishing fault types, fault severity, fault location, and noise immunity of rotor stochastic vibration. This paper presents a novel method (PPSE-SVM for rotor vibration fault diagnosis and real-time vibration monitoring. The presented effort is promising to improve the fault diagnosis precision of rotating machinery like gas turbine.

Concept of operator support system based on cognitive simulation

International Nuclear Information System (INIS)

Sasou, Kunihide; Takano, Kenichi

1999-01-01

Hazardous technologies such chemical plants, nuclear power plants, etc. have introduced multi-layered defenses to prevent accidents. One of those defenses is experienced operators in control rooms. Once an abnormal condition occurs, they are the front line people to cope with it. Therefore, operators' quick recognition of the plant conditions and fast decision making on responses are quite important for trouble shooting. In order to help operators to deal with abnormalities in process plants, lots of efforts had been done to develop operator support systems since early 1980s (IAEA, 1993). However, the boom in developing operator support systems has slumped due to the limitations of knowledge engineering, artificial knowledge, etc (Yamamoto, 1998). The limitations had also biased the focus of the system development to abnormality detection, root cause diagnosis, etc (Hajek, Hashemi, Sharma and Chandrasekaran, 1986). Information or guidance about future plant behavior and strategies/tactics to deal with abnormal events are important and helpful for operators but researches and development of those systems made a belated start. Before developing these kinds of system, it is essential to understand how operators deal with abnormalities. CRIEPI has been conducting a project to develop a computer system that simulates behavior of operators dealing with abnormal operating conditions in a nuclear power plant. This project had two stages. In the first stage, the authors developed a prototype system that simulates behavior of a team facing abnormal events in a very simplified power plant (Sasou, Takano and Yoshimura, 1995). In the second stage, the authors applied the simulation technique developed in the first stage to construct a system to simulate a team's behavior in a nuclear power plant. This paper briefly summarizes the simulation system developed in the second stage, main mechanism for the simulation and the concept of an operator support system based on this

Fault diagnosis for dynamic power system

International Nuclear Information System (INIS)

Thabet, A.; Abdelkrim, M.N.; Boutayeb, M.; Didier, G.; Chniba, S.

2011-01-01

The fault diagnosis problem for dynamic power systems is treated, the nonlinear dynamic model based on a differential algebraic equations is transformed with reduced index to a simple dynamic model. Two nonlinear observers are used for generating the fault signals for comparison purposes, one of them being an extended Kalman estimator and the other a new extended kalman filter with moving horizon with a study of convergence based on the choice of matrix of covariance of the noises of system and measurements. The paper illustrates a simulation study applied on IEEE 3 buses test system.

Development and quantitative effect estimation of an integrated decision support system to aid operator's cognitive activities for NPP advanced main control rooms

International Nuclear Information System (INIS)

Lee, Seung Jun

2007-02-01

As digital and computer technologies have grown, human-machine interfaces (HMIs) have evolved. In safety critical systems, especially in nuclear power plants (NPPs), HMIs are important for reducing operational costs, for reducing the number of necessary operators, and for reducing the probability of accident occurrence. Efforts have been made to improve main control room (MCR) interface design and to develop automation or support systems to ensure convenient operation and maintenance. In this paper, an integrated decision support system to aid the cognitive activities of operators is proposed for advanced MCRs in future NPPs. The proposed system supports not merely a particular task, but also the entire operation process based on a human cognitive process model. It supports the operator's entire cognitive process by integrating decision support systems that support each cognitive activity. In this paper, the operator's operation processes are analyzed based on a human cognitive process model and appropriate support systems that support each activity of the human cognitive process are suggested. Two decision support systems were developed in this paper. The first one is the fault diagnosis advisory system (FDAS) which detects faults and diagnoses them. The FDAS provides a list of possible faults and expected causes to operators. It was implemented using two kinds of neural networks for more reliable diagnosis results. The second system is the multifunctional operator support system for operation guidance, which includes the FDAS and the operation guidance system. The operation guidance system is to prevent operator's commission errors and omission errors. Furthermore, the effect of the proposed system was estimated because to evaluate decision support systems in order to validate their efficiency is as important as to design highly reliable decision support systems. The effect estimations were performed theoretically and experimentally. The Bayesian

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.

Combination of artificial intelligence and procedural language programs in a computer application system supporting nuclear reactor operations

International Nuclear Information System (INIS)

Town, G.G.; Stratton, R.C.

1985-01-01

A computer application system is described which provides nuclear reactor power plant operators with an improved decision support system. This system combines traditional computer applications such as graphics display with artificial intelligence methodologies such as reasoning and diagnosis so as to improve plant operability. This paper discusses the issues, and a solution, involved with the system integration of applications developed using traditional and artificial intelligence languages

Combination of artificial intelligence and procedural language programs in a computer application system supporting nuclear reactor operations

International Nuclear Information System (INIS)

Stratton, R.C.; Town, G.G.

1985-01-01

A computer application system is described which provides nuclear reactor power plant operators with an improved decision support system. This system combines traditional computer applications such as graphics display with artifical intelligence methodologies such as reasoning and diagnosis so as to improve plant operability. This paper discusses the issues, and a solution, involved with the system integration of applications developed using traditional and artificial intelligence languages

Design of a fault diagnosis system for next generation nuclear power plants

International Nuclear Information System (INIS)

Zhao, K.; Upadhyaya, B.R.; Wood, R.T.

2004-01-01

A new design approach for fault diagnosis is developed for next generation nuclear power plants. In the nuclear reactor design phase, data reconciliation is used as an efficient tool to determine the measurement requirements to achieve the specified goal of fault diagnosis. In the reactor operation phase, the plant measurements are collected to estimate uncertain model parameters so that a high fidelity model can be obtained for fault diagnosis. The proposed algorithm of fault detection and isolation is able to combine the strength of first principle model based fault diagnosis and the historical data based fault diagnosis. Principal component analysis on the reconciled data is used to develop a statistical model for fault detection. The updating of the principal component model based on the most recent reconciled data is a locally linearized model around the current plant measurements, so that it is applicable to any generic nonlinear systems. The sensor fault diagnosis and process fault diagnosis are decoupled through considering the process fault diagnosis as a parameter estimation problem. The developed approach has been applied to the IRIS helical coil steam generator system to monitor the operational performance of individual steam generators. This approach is general enough to design fault diagnosis systems for the next generation nuclear power plants. (authors)

An intelligent medical system for diagnosis of bone diseases

Energy Technology Data Exchange (ETDEWEB)

Hatzilygeroudis, I [University of Patras, School of Engineering, Department of Computer Engineering and Informatics, 26500 Patras, Greece (Greece); Vassilakos, P J [Regional University Hospital of Patras, Department of Nuclear Medicine, Patras Greece (Greece); Tsakalidis, A [Computer Technology Institute, P.O. Box 1122, 26110 Patras, Greece (Greece)

1994-12-31

In this paper, aspects of the design of an intelligent medical system for diagnosis of bone diseases that can be detected by scintigraphic images are presented. The system comprises three major parts: a user interface (UI), a database management system (DBMS), and an expert system (ES). The DBMS is used for manipulation of various patient data. A number of patient cases are selected as prototype and stored in separate database. Diagnosis is performed via the ES, called XBONE, based on patient data. Knowledge is represented via an integrated formalism that combines production rules and a neural network. This results in better representation, and facilitates knowledge acquisition and maintenance. (authors). 10 refs., 2 figs.

Support system, excavation arrangement, and process of supporting an object

Science.gov (United States)

Arnold, Bill W.

2017-08-01

A support system, an excavation arrangement, and a process of supporting an object are disclosed. The support system includes a weight-bearing device and a camming mechanism positioned below the weight-bearing device. A downward force on the weight-bearing device at least partially secures the camming mechanism to opposing surfaces. The excavation arrangement includes a borehole, a support system positioned within and secured to the borehole, and an object positioned on and supported by the support system. The process includes positioning and securing the support system and positioning the object on the weight-bearing device.

A method for making a glass supported system, such glass supported system, and the use of a glass support therefor

NARCIS (Netherlands)

Unnikrishnan, S.; Jansen, Henricus V.; Berenschot, Johan W.; Fazal, I.; Louwerse, M.C.; Mogulkoc, B.; Sanders, Remco G.P.; de Boer, Meint J.; Elwenspoek, Michael Curt

2008-01-01

The invention relates to a method for making a glass supported micro or nano system, comprising the steps of: i) providing a glass support; ii) mounting at least one system on at least one glass support; and iii) bonding the system to the glass support, such that the system is circumferentially

Development research of expert system for diagnosis of landslide

Energy Technology Data Exchange (ETDEWEB)

Yoshikawa, Toru; Soeda, Yoshio; Nakamura, Hirohisa [Kansai Electric Power Co. Inc., Osaka (Japan)

1989-03-25

Measures against landslides are based upon a judgment to be made by combined application of professional knowledge of the scientific fields such as topography and geology, etc. and Kansai Electric Power Co. tried to construct a technical support system for preliminary diagnosis of landslide with which field engineers can easily utilize expert knowledge and to which artificial intelligence (AI) is applied. This system is to diagnose preliminarily the existence of such a landslide-prone area which is likely to hamper the project concerned at its early stage and after examination, those considered to be appropriate for the purpose were selected from among the artificial intelligence tools already developed. And as the knowledge base, knowledge was arranged in order with regard to the common features of landslide-prone areas, classification of landslide spots, landslide-prone topography and confusing topography, and procedures as well as remarks to be taken in reading the landslide topography, and was transformed as rule in order to input as the knowledge base into a computer. The system used the aerial photography interpretation theory as the base for its expert knowledge base and the materials necessary therefore were confined to easily obtainable aerial photographs and topographical maps. The system was prepared with a general purpose personal computer. 4 figs., 1 tab.

Research and design of distributed intelligence fault diagnosis system in nuclear power plant

International Nuclear Information System (INIS)

Liu Yongkuo; Xie Chunli; Cheng Shouyu; Xia Hong

2011-01-01

In order to further reduce the misoperation after the faults occurring of nuclear power plant, according to the function distribution of nuclear power equipment and the distributed control features of digital instrument control system, a nuclear power plant distributed condition monitoring and fault diagnosis system was researched and designed. Based on decomposition-integrated diagnostic thinking, a fuzzy neural network and RBF neural network was presented to do the distributed local diagnosis and multi-source information fusion technology for the global integrated diagnosis. Simulation results show that the developed distributed status monitoring and fault diagnosis system can diagnose more typical accidents of PWR to provide effective diagnosis and operation information. (authors)

FY 2000 Report on the survey results. Survey on international cooperation for development of a system for supporting low-invasion surgical operations; 2000 nendo teishinshu shujutsu shien system kaihatsu ni kakawaru kokusai kyoryoku ni kansuru chosa hokokusho

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-03-01

The international Medical Treatment Symposium and Exhibition were held from July 20 to the end of August, 2000 in Hannover in Germany, in which the Japan's system for supporting low-invasion surgical operations was presented and information was exchanged for the system. A total of 50 to 100 neurosurgeons visited the exhibition, held as part of the symposium, every day from July 21 to 24. The system for supporting low-invasion surgical operations is rated as the most advanced one in the world. It attracted much attention, because a number of attendees applied for operating manipulator. The research trends seen in the symposium point to early diagnosis of diseases, finer surgical operations, low-invasion type operations, use of high-function diagnosis devices for improving operations, real-time imaging, and application of DNA diagnosis to pathological diagnosis, among others. Japan is leading the world in the technological areas of micromachines, robotics, high-vision and diagnosis. The bold researches, e.g., those on artificial visual and hearing systems to be embedded in the head, are pursued more extensively in the overseas countries. (NEDO)

Diagnosis of multi-agent systems and its application to public administration

NARCIS (Netherlands)

Boer, A.; van Engers, T.; Abramowicz, W.; Maciaszek, L.; Węcel, K.

2011-01-01

In this paper we present a model-based diagnosis view on the complex social systems in which large public administration organizations operate. The purpose of diagnosis as presented in this paper is to identify agent role instances that are not conforming to expectations in a multi-agent system

Deductive Error Diagnosis and Inductive Error Generalization for Intelligent Tutoring Systems.

Science.gov (United States)

Hoppe, H. Ulrich

1994-01-01

Examines the deductive approach to error diagnosis for intelligent tutoring systems. Topics covered include the principles of the deductive approach to diagnosis; domain-specific heuristics to solve the problem of generalizing error patterns; and deductive diagnosis and the hypertext-based learning environment. (Contains 26 references.) (JLB)

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.

Bond graphs for modelling, control and fault diagnosis of engineering systems

CERN Document Server

2017-01-01

This book presents theory and latest application work in Bond Graph methodology with a focus on: • Hybrid dynamical system models, • Model-based fault diagnosis, model-based fault tolerant control, fault prognosis • and also addresses • Open thermodynamic systems with compressible fluid flow, • Distributed parameter models of mechanical subsystems. In addition, the book covers various applications of current interest ranging from motorised wheelchairs, in-vivo surgery robots, walking machines to wind-turbines.The up-to-date presentation has been made possible by experts who are active members of the worldwide bond graph modelling community. This book is the completely revised 2nd edition of the 2011 Springer compilation text titled Bond Graph Modelling of Engineering Systems – Theory, Applications and Software Support. It extends the presentation of theory and applications of graph methodology by new developments and latest research results. Like the first edition, this book addresses readers in a...

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics.

Science.gov (United States)

Kuru, Kaya; Niranjan, Mahesan; Tunca, Yusuf; Osvank, Erhan; Azim, Tayyaba

2014-10-01

In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available. The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype-phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features. The proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5-9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (pbenefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes. Copyright © 2014. Published by Elsevier B.V.

LHCb: Phronesis, a diagnosis and recovery tool for system administrators

CERN Multimedia

Haen, C; Bonaccorsi, E; Neufeld, N

2013-01-01

The backbone of the LHCb experiment is the Online system, which is a very large and heterogeneous computing center. Making sure of the proper behavior of the many different tasks running on the more than 2000 servers represents a huge workload for the small expert-operator team and is a 24/7 task. At the occasion of CHEP 2012, we presented a prototype of a framework that we designed in order to support the experts. The main objective is to provide them with always improving diagnosis and recovery solutions in case of misbehavior of a service, without having to modify the original applications. Our framework is based on adapted principles of the Autonomic Computing model, on reinforcement learning algorithms, as well as innovative concepts such as Shared Experience. While the presentation made at CHEP 2012 showed the validity of our prototype on simulations, we here present a version with improved algorithms, manipulation tools, and report on experience with running it in the LHCb Online system.

A new method of knowledge processing for equipment diagnosis of nuclear power plants

International Nuclear Information System (INIS)

Fujii, M.; Fukumoto, A.; Tai, I.; Morioka, T.

1987-01-01

In this work, the authors complete the development of a new knowledge processing method and representation for equipment diagnosis of nuclear power plants and evaluate its functions by applying to the maintenance and diagnosis support system of the reactor instrumentation. This knowledge processing method system is based on the Cause Generation and Checking concept and has sufficient performance not only in the diagnosis function but also in the man-machine interfacing function. The maintenance and diagnosis support system based on this method leads to the possibility for users to diagnose various phenomena occurred in an objective equipment to the considerable extent by consulting with the system, even if they don't have enough knowledge. With this system, it becomes easy for operators or plant engineers to take immediate actions to counteract against the abnormality. The maintainability of the equipments is improved and MTTR (Mean Time To Repair) is expected to be shorter. This new knowledge processing method is proved to be suited for fault diagnosis of the equipments of nuclear power plants

Water quality diagnosis system for power plant

International Nuclear Information System (INIS)

Igarashi, Hiroo; Fukumoto, Toshihiko

1991-01-01

An AI diagnose system for the water quality control of a BWR type reactor is divided into a general diagnosing section for generally classifying the water quality conditions of the plant depending on a causal relation between the symptom of the water quality abnormality and its causes, generally diagnosing the position and the cause of the abnormality and ranking the items considered to be the cause, and a detail diagnosing section for a further diagnosis based on the result of the diagnosis in the former section. The general diagnosing section provides a plurality of threshold values showing the extent of the abnormality depending on the cause to the causal relation between the causes and the forecast events previously formed depending on the data of process sensors in the plant. Since the diagnosis for the abnormality and normality is given not only as an ON or OFF mode but also as the extent thereof, it can enter the detailed diagnosis in the most plausible order, based on a plurality of estimated causes, to enable to find the case and take a counter-measure in an early stage. (N.H.)

Diversity and consensus as key concepts for design of intelligent operator support system

International Nuclear Information System (INIS)

Kitamura, M.; Furukawa, H.; Sakuma, M.; Washio, T.

2004-01-01

A general framework and guiding principles for development of intelligent operator support system in nuclear plants are proposed in this paper. The main principle is to provide advisory information to the operators through consensus of multiple agents each of which can conduct operational decision- making by focusing on mutually dissimilar symptoms obtained from the plant. The applicability and credibility of the operator support system are expected to be significantly improved by implementing the proposed scheme. An efficient procedure for diversifying the symptom descriptions was developed together with a method for autonomous consensus formation among the agents. A prototype system was developed for the subtask of fault diagnosis by emulating multiple neural networks as the diagnostic agents. The advantage of the proposed methodology over the conventional ones was clearly demonstrated through numerical evaluations simulating anomalies in a pressurized water reactor. (author)

Design for testability and diagnosis at the system-level

Science.gov (United States)

Simpson, William R.; Sheppard, John W.

1993-01-01

The growing complexity of full-scale systems has surpassed the capabilities of most simulation software to provide detailed models or gate-level failure analyses. The process of system-level diagnosis approaches the fault-isolation problem in a manner that differs significantly from the traditional and exhaustive failure mode search. System-level diagnosis is based on a functional representation of the system. For example, one can exercise one portion of a radar algorithm (the Fast Fourier Transform (FFT) function) by injecting several standard input patterns and comparing the results to standardized output results. An anomalous output would point to one of several items (including the FFT circuit) without specifying the gate or failure mode. For system-level repair, identifying an anomalous chip is sufficient. We describe here an information theoretic and dependency modeling approach that discards much of the detailed physical knowledge about the system and analyzes its information flow and functional interrelationships. The approach relies on group and flow associations and, as such, is hierarchical. Its hierarchical nature allows the approach to be applicable to any level of complexity and to any repair level. This approach has been incorporated in a product called STAMP (System Testability and Maintenance Program) which was developed and refined through more than 10 years of field-level applications to complex system diagnosis. The results have been outstanding, even spectacular in some cases. In this paper we describe system-level testability, system-level diagnoses, and the STAMP analysis approach, as well as a few STAMP applications.

Expert environment for the development of nuclear power plants failure diagnosis systems

International Nuclear Information System (INIS)

Guido, P.N.; Oggianu, S.; Etchepareborda, A.; Fernandez, O.

1996-01-01

The present work explores some of the developing stages of an Expert Environment for plant failures Diagnosis Systems starting from Knowledge Based Systems. We present a prototype that carries out an inspection of anomalous symptoms and a diagnosis process based on a Plant Abnormality Model of a PHWR secondary system

A decision support system based on hybrid knowledge approach for nuclear power plant operation

International Nuclear Information System (INIS)

Yang, J.O.; Chang, S.H.

1991-01-01

This paper describes a diagnostic expert system, HYPOSS (Hybrid Knowledge Based Plant Operation Supporting System), which has been developed to support operators' decision making during the transients of nuclear power plant. HYPOSS adopts the hybrid knowledge approach which combines shallow and deep knowledge to couple the merits of both approaches. In HYPOSS, four types of knowledge are used according to the steps of diagnosis procedure: structural, functional, behavioral and heuristic knowledge. Frames and rules are adopted to represent the various knowledge types. Rule-based deduction and abduction are used for shallow and deep knowledge based reasoning respectively. The event-based operational guidelines are provided to the operator according to the diagnosed results

A study on diagnosis of Dysmenorrhea patients by Diagnosis System of Oriental Medicine

Directory of Open Access Journals (Sweden)

In Sun,Lee

2007-02-01

Full Text Available Purpose : This study was undertaken to make a diagnosis weakness and firmness (虛實 of Dysmenorrhea patients by diagnosis questionnaires system(Diagnosis System of Oriental Medicine-DSOM Methods : The subjects were 58 volunteers who was suffering for dysmenorrhea, employed using Measure of Menstrual Pain (MMP questionnaire. The had agreed to take part in this experiment, with didn't take any anodyne drugs. The MMP score by using 7 questions and the Menstrual Symptom Severity List(MSSL-D was measured before and after menstruation cycle. Results and Conclusions : The findings of this study were as follows; 1. We examined Pathogenic Factor's frequency of DSOM, Coldness(寒 was 45 persons 80.36%, Damp(濕 was 40 persons 71.43%, Heart(心 was 37 persons 66.07%, Heat syndrom(熱 was 9 persons 16.07%, insufficiency of Yang(陽虛 was 6 persons 10.71%. 2. We divided Dysmenorrhea patients into two groups(weakness and firmness by Results of DSOM, Firmness was 25 Persons 43.1%, Weakness was 23 persons 39.7%, Unknown was 10 persons 17.2%. 3. In estimation based on Measure of Menstrual Pain (MMP questionnaire Severe menstrual pain is weakness, Mild menstrual pain is Firmness. 4. In estimation of coldness and heat syndrom, Coldness was 40 persons 69.0%, Heat syndrom, was 2 persons 3.5%, Possess both coldness and heat syndrom was 9 persons 15.5%.

Incipient multiple fault diagnosis in real time with applications to large-scale systems

International Nuclear Information System (INIS)

Chung, H.Y.; Bien, Z.; Park, J.H.; Seon, P.H.

1994-01-01

By using a modified signed directed graph (SDG) together with the distributed artificial neutral networks and a knowledge-based system, a method of incipient multi-fault diagnosis is presented for large-scale physical systems with complex pipes and instrumentations such as valves, actuators, sensors, and controllers. The proposed method is designed so as to (1) make a real-time incipient fault diagnosis possible for large-scale systems, (2) perform the fault diagnosis not only in the steady-state case but also in the transient case as well by using a concept of fault propagation time, which is newly adopted in the SDG model, (3) provide with highly reliable diagnosis results and explanation capability of faults diagnosed as in an expert system, and (4) diagnose the pipe damage such as leaking, break, or throttling. This method is applied for diagnosis of a pressurizer in the Kori Nuclear Power Plant (NPP) unit 2 in Korea under a transient condition, and its result is reported to show satisfactory performance of the method for the incipient multi-fault diagnosis of such a large-scale system in a real-time manner

Developing Expert System for Tuberculosis Diagnose to Support Knowledge Sharing in the Era of National Health Insurance System

Science.gov (United States)

Lidya, L.

2017-03-01

National Health Insurance has been implemented since 1st January 2014. A number of new policies have been established including multilevel referral system. The multilevel referral system classified health care center into three levels, it determined that the flow of patient treatment should be started from first level health care center. There are 144 kind of diseases that must be treat in the first level which mainly consists of general physicians. Unfortunately, competence of the physician in the first level may not fulfil the standard competence yet. To improved the physisians knowledge, government has created many events to accelerate knowledge sharing. However, it still needs times and many resources to give significan results. Expert system is kind of software that provide consulting services to non-expert users in accordance with the area of its expertise. It can improved effectivity and efficiency of knowledge sharing and learning. This research was developed a model of TB diagnose expert system which comply with the standard procedure of TB diagnosis and regulation. The proposed expert system has characteristics as follows provide facility to manage multimedia clinical data, supporting the complexity of TB diagnosis (combine rule-based and case-based expert system), interactive interface, good usability, multi-platform, evolutionary.

Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers

Directory of Open Access Journals (Sweden)

N. V. Kolesov

2013-01-01

Full Text Available A method of fault diagnosis in dynamic systems based on a fuzzy approach is proposed. The new method possesses two basic specific features which distinguish it from the other known fuzzy methods based on the application of fuzzy logic and a bank of state observers. First, this method uses a bank of interacting observers instead of traditional independent observers. The second specific feature of the proposed method is the assumption that there is no strict boundary between the serviceable and disabled technical states of the system, which makes it possible to specify a decision making rule for fault diagnosis.

Systemic Lupus Erythematosus: Primary Care Approach to Diagnosis and Management.

Science.gov (United States)

Lam, Nguyet-Cam Vu; Ghetu, Maria V; Bieniek, Marzena L

2016-08-15

Systemic lupus erythematosus is an autoimmune disease that affects many systems, including the skin, musculoskeletal, renal, neuropsychiatric, hematologic, cardiovascular, pulmonary, and reproductive systems. Family physicians should be familiar with the manifestations of lupus to aid in early diagnosis, monitoring patients with mild disease, recognizing warning signs that require referral to a rheumatologist, and helping to monitor disease activity and treatment in patients with moderate to severe disease. The American College of Rheumatology has 11 classification criteria for lupus. If a patient meets at least four criteria, lupus can be diagnosed with 95% specificity and 85% sensitivity. All patients with lupus should receive education, counseling, and support. Hydroxychloroquine is the cornerstone of treatment because it reduces disease flares and other constitutional symptoms. Low-dose glucocorticoids can be used to treat most manifestations of lupus. The use of immunosuppressive and cytotoxic agents depends on the body systems affected. Patients with mild disease that does not involve major organ systems can be monitored by their family physician. Patients with increased disease activity, complications, or adverse effects from treatment should be referred to a rheumatologist. To optimize treatment, it is important that a rheumatologist coordinate closely with the patient's family physician to improve chronic care as well as preventive health services.

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

Transient pattern analysis for fault detection and diagnosis of HVAC systems

International Nuclear Information System (INIS)

Cho, Sung-Hwan; Yang, Hoon-Cheol; Zaheer-uddin, M.; Ahn, Byung-Cheon

2005-01-01

Modern building HVAC systems are complex and consist of a large number of interconnected sub-systems and components. In the event of a fault, it becomes very difficult for the operator to locate and isolate the faulty component in such large systems using conventional fault detection methods. In this study, transient pattern analysis is explored as a tool for fault detection and diagnosis of an HVAC system. Several tests involving different fault replications were conducted in an environmental chamber test facility. The results show that the evolution of fault residuals forms clear and distinct patterns that can be used to isolate faults. It was found that the time needed to reach steady state for a typical building HVAC system is at least 50-60 min. This means incorrect diagnosis of faults can happen during online monitoring if the transient pattern responses are not considered in the fault detection and diagnosis analysis

Data-driven design of fault diagnosis systems nonlinear multimode processes

CERN Document Server

Haghani Abandan Sari, Adel

2014-01-01

In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target...

A new remote-imaging diagnosis system at Komazawa University

International Nuclear Information System (INIS)

Shimada, Morio; Kohda, Eiichi; Yoshikawa, Kohki

2007-01-01

We developed a remote-imaging diagnosis system that links the highly experienced radiologists at Komazawa University with Fuji Electric Hospital, where no such radiologists are present. MRI or CT images from Fuji Electric hospital are transmitted to Komazawa University via private line (INS64). The radiologists at Komazawa University then read the MRI or CT images, and relay the results to Fuji Electric Hospital. We describe the advantages and disadvantages of this system. MRI or CT imaging data from 80 cases were used. The data were stored in the imaging system server at Fuji Electric Hospital and were evaluated by experienced radiologists at Komazawa University. The images were sent one by one to the diagnostic support system server at Komazawa University through the private INS64 line. We examined transmission time per case and the security of transmission. Transmission of MRI or CT images from the 80 cases required a mean duration of 63 minutes 30 seconds per image. The quality of all images was highly satisfactory. In addition, there was no evidence of weaknesses in security. A physician at Fuji Electric Hospital was able to readily explain to the patient the results of the images by referring to the findings written by a radiologist at Komazawa University. We were able to transmit MRI or CT images by using this system safely and readily. The primary disadvantage of this system was the slow transmission speed. This will be improved by upgrading to an optical fibers. (author)

Information System Engineering Supporting Observation, Orientation, Decision, and Compliant Action

Science.gov (United States)

Georgakopoulos, Dimitrios

The majority of today's software systems and organizational/business structures have been built on the foundation of solving problems via long-term data collection, analysis, and solution design. This traditional approach of solving problems and building corresponding software systems and business processes, falls short in providing the necessary solutions needed to deal with many problems that require agility as the main ingredient of their solution. For example, such agility is needed in responding to an emergency, in military command control, physical security, price-based competition in business, investing in the stock market, video gaming, network monitoring and self-healing, diagnosis in emergency health care, and many other areas that are too numerous to list here. The concept of Observe, Orient, Decide, and Act (OODA) loops is a guiding principal that captures the fundamental issues and approach for engineering information systems that deal with many of these problem areas. However, there are currently few software systems that are capable of supporting OODA. In this talk, we provide a tour of the research issues and state of the art solutions for supporting OODA. In addition, we provide specific examples of OODA solutions we have developed for the video surveillance and emergency response domains.

DESIGN OF EXPERT SYSTEM AS A SUPPORT TOOL FOR EARLY DIAGNOSIS OF PRIMARY HEADACHE

Directory of Open Access Journals (Sweden)

Zahwa Arsy Azzahra

2017-07-01

Conclusion. The design of early detection of primary headaches with the input parameters as mentioned before derived from the raw data as electronic medical records to be analyzed based on methods Naïve Bayes classifier resulted in the decision diagnosis of migraine, cluster and TTH have accuracy values by 92 %.

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

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.

A handheld computer-aided diagnosis system and simulated analysis

Science.gov (United States)

Su, Mingjian; Zhang, Xuejun; Liu, Brent; Su, Kening; Louie, Ryan

2016-03-01

This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming. Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results demonstrate our technology is very promising and expected to be used in clinical practice.

A practical approach to the diagnosis of systemic amyloidoses.

Science.gov (United States)

Fernández de Larrea, Carlos; Verga, Laura; Morbini, Patrizia; Klersy, Catherine; Lavatelli, Francesca; Foli, Andrea; Obici, Laura; Milani, Paolo; Capello, Gian Luca; Paulli, Marco; Palladini, Giovanni; Merlini, Giampaolo

2015-04-02

Accurate diagnosis of systemic amyloidosis is necessary both for assessing the prognosis and for delineating the appropriate treatment. It is based on histologic evidence of amyloid deposits and characterization of the amyloidogenic protein. We prospectively evaluated the diagnostic performance of immunoelectron microscopy (IEM) of abdominal fat aspirates from 745 consecutive patients with suspected systemic amyloidoses. All cases were extensively investigated with clinical and laboratory data, with a follow-up of at least 18 months. The 423 (56.8%) cases with confirmed systemic forms were used to estimate the diagnostic performance of IEM. Compared with Congo-red-based light microscopy, IEM was equally sensitive (75% to 80%) but significantly more specific (100% vs 80%; P 99% of the cases. IEM of abdominal fat aspirates is an effective tool in the routine diagnosis of systemic amyloidoses. © 2015 by The American Society of Hematology.

Diagnosis and Management of Systemic Sclerosis: A Practical Approach.

Science.gov (United States)

Lee, Jason J; Pope, Janet E

2016-02-01

Systemic sclerosis is a devastating multisystem rheumatologic condition that is characterized by autoimmunity, tissue fibrosis, obliterative vasculopathy and inflammation. Clinical presentation and course of the condition vary greatly, which complicates both diagnosis and corresponding treatment. In this regard, recent advances in disease understanding, both clinically and biochemically, have led to newer classification criteria for systemic sclerosis that are more inclusive than ever before. Still, significant disease modifying therapies do not yet exist for most patients. Therefore, organ-based management strategies are employed and research has been directed within this paradigm focusing on either the most debilitating symptoms, such as Raynaud's phenomenon, digital ulcers and cutaneous sclerosis, or life-threatening organ involvement such as interstitial lung disease and pulmonary arterial hypertension. The current trends in systemic sclerosis diagnosis, evidence-based treatment recommendations and potential future directions in systemic sclerosis treatment are discussed.

ICON: An artificial intelligence approach to radiologic differential diagnosis

International Nuclear Information System (INIS)

Swett, H.A.; Miller, P.L.

1986-01-01

ICON is a computer system, developed using artificial intelligence techniques, that is designed to help radiologists manage the large body of knowledge needed to perform differential diagnosis in radiology. The system's domain is lung disease in patients with lymphoproliferative disorders. The radiologist proposes a diagnostic hypothesis which he or she thinks explains the known clinical and chest radiographic findings. ICON responds with an English-language prose critique that discusses how and why the proposed diagnosis is or is not supported by the clinical literature and suggests further findings or clinical information that might make the diagnosis more secure

Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis.

Science.gov (United States)

Zarinabad, Niloufar; Meeus, Emma M; Manias, Karen; Foster, Katharine; Peet, Andrew

2018-05-02

Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care. The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data. The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors. Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters. MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians' skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments. ©Niloufar Zarinabad, Emma M Meeus, Karen Manias

Decision support systems

DEFF Research Database (Denmark)

Jørgensen, L.N.; Noe, E.; Langvad, A.M.

2007-01-01

system Crop Protection Online is widely used by advisors and as a learning tool for students. Although the system has been validated in many field trials over the years and has shown reliable results, the number of end-users among farmers has been relatively low during the last 10 years (approximately...... 1000 farmers). A sociological investigation of farmers' decision-making styles in the area of crop protection has shown that arable farmers can be divided into three major groups: (a) system-orientated farmers, (b) experience-based farmers and (c) advisory-orientated farmers. The information required...... by these three groups to make their decisions varies and therefore different ways of using decision support systems need to be provided. Decision support systems need to be developed in close dialogue and collaboration with user groups....

Method for fault diagnosis of digital control systems in nuclear power plant

International Nuclear Information System (INIS)

Suzuki, Satoshi; Nagaoka, Yukio; Ohga, Yukiharu; Ito, Tetsuo

1990-01-01

This paper presents a method for localizing faulty components of control systems by replaceable parts such as print boards and cables, in a large scale plant like a nuclear power plant. Most of today's control systems form a distributed configuration including many digital controllers interconnected by data communication networks. Usually, to localize the faulty components in nuclear plant control systems, suspected faulty components are narrowed down by executing manual tests to examine whether the objects are normal or abnormal based on design documents and personnel know-how, besides the uses of self-diagnosis functions built into the control systems. In the present method, procedures of various tests including the know-how and checking of self-diagnosis functions are provided as knowledge of tests. The tests to be executed is determined by considering failure probabilities of objects, and easiness and effectiveness of testing. Then, the suspects are narrowed down sequentially based on the test result. In checking feasibility of this diagnosis method for a simulated control system, intended faults are satisfactorily localized. This method is confirmed to be practicable for diagnosis of large scale digital control systems. (author)

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)

Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis

International Nuclear Information System (INIS)

Wang, Dong; Tse, Peter W; Guo, Wei; Miao, Qiang

2011-01-01

A novel method for enhancing gearbox fault diagnosis and prognosis is developed by fusion of multiple health indicators through support vector data description. First, the Comblet transform is used to identify gear residual error signals from the raw signal. Second, based on the observation of gear residual error signals, a total of 11 gear health indicators are identified, and are categorized into two types of indicators. The first and second types of indicators are for fault diagnosis and prognosis, respectively. The first type has six indicators, which are sensitive to impulsive signals triggered by anomalous impacts. The second type has five indicators, which are suitable for tracking degradation of faults. Third, through the support vector data description, the first six health indicators are fused into type one indicators for fault diagnosis. The remaining five indicators are fused into type two indicators for fault prognosis. Finally, a Gaussian kernel is designed to enhance the performance of type one and two indicators by optimal range of width size. The effectiveness of the proposed method is validated through experiments. The new method has been proven to be superior to methods that use unfused indicators individually

Technical diagnosis system for EAST tokamak

International Nuclear Information System (INIS)

Qian Jing; Weng, P.D.; Luo, J.R.; Chen, Z.M.; Wu, Y.

2010-01-01

Technical diagnosis system (TDS) is one of the important subsystems of EAST (experimental advanced superconducting tokamak) device, main function of which is to monitor status parameters in EAST device. Those status parameters include temperature of different positions of main components, resistance of each superconducting (SC) coils, joint resistance of SC coils and high-temperature superconducting (HTS) current leads, strain of cold-quality components endured force, and displacement and current of toroidal field (TF) coils in EAST device, which are analog input signals. In addition there are still some analog and digital output signals. The TDS monitors all of those signals in the period of EAST experiments. TDS data monitoring is described in detail for it plays important role during EAST campaign. And how to protect the SC magnet system during each plasma discharging is presented with data of temperature of coolant inlet and outlet of SC coils and feeders and cases of the TF coils and temperature in the upper and middle and bottom of the TF coil case. During construction of the TDS primary difficulties come from installation of Lakeshore Cernox temperature sensors, strain measurement of central solenoid coils support legs and installation of co-wound voltage sensors for quench detection. While during operation since the first commissioning big challenges are from temperature measurement changes in current leads and quench detection of PF coils. Those difficulties in both stages are introduced which are key to make the TDS reliable. Meanwhile analysis of experimental data like temperature as a back up to testify quench occurrence and stress on vacuum vessel thermal shield and vacuum vessel have also been discussed.

Representation and Use of Knowledge in Automatic Fault Diagnosis

International Nuclear Information System (INIS)

Brendeford, Tor S.

1996-01-01

The report relates activities performed within the ongoing project on Integrated Diagnosis Systems (IDS). A unifying aspect of the activities is representation of knowledge applied in diagnosis. New ways of representing such knowledge can improve the diagnoses, enable reuse, and facilitate consistent integration with other operator support systems. The tasks of the diagnostic process, and the roles of domain knowledge, are discussed in relation to different methods of diagnosis. Two primary methods of diagnosis are recognised in the report, model-based and association-based. Distinct differences of these two methods are identified as focus for integration. A methodology for specifying the design of diagnosis systems is reviewed. This methodology seems to provide a good theoretical basis for understanding problems of fault diagnosis. Qualitative and functional modelling methods are studied by application to a common example domain. The two specific techniques are found to be promising in relation to diagnosis. A software setup for simulated diagnosis is presented. This setup is to be used in the activity on knowledge representation, where a blackboard system is the central module of the setup. Presentations of process domain knowledge show the capabilities of the blackboard architecture and suggest schemes for integrated use of the information. The object-oriented architecture is also shown to serve the needs for presentation of diagnostic reasoning, which is a vital aspect when integrating different diagnosis methods. (author)

Fuzzy Based Decision Support System for Condition Assessment and Rating of Bridges

Science.gov (United States)

Srinivas, Voggu; Sasmal, Saptarshi; Karusala, Ramanjaneyulu

2016-09-01

In this work, a knowledge based decision support system has been developed to efficiently handle the issues such as distress diagnosis, assessment of damages and condition rating of existing bridges towards developing an exclusive and robust Bridge Management System (BMS) for sustainable bridges. The Knowledge Based Expert System (KBES) diagnoses the distresses and finds the cause of distress in the bridge by processing the data which are heuristic and combined with site inspection results, laboratory test results etc. The coupling of symbolic and numeric type of data has been successfully implemented in the expert system to strengthen its decision making process. Finally, the condition rating of the bridge is carried out using the assessment results obtained from the KBES and the information received from the bridge inspector. A systematic procedure has been developed using fuzzy mathematics for condition rating of bridges by combining the fuzzy weighted average and resolution identity technique. The proposed methodologies and the decision support system will facilitate in developing a robust and exclusive BMS for a network of bridges across the country and allow the bridge engineers and decision makers to carry out maintenance of bridges in a rational and systematic way.

Systematic Product Development of Control and Diagnosis Functionalities

Science.gov (United States)

Stetter, R.; Simundsson, A.

2017-01-01

In the scientific field of systematic product development a wide range of helpful methods, guidelines and tools were generated and published in recent years. Until now little special attention was given to design guidelines aiming at supporting product development engineers to design products that allow and support control or diagnosis functions. The general trend to ubiquitous computing and the first development steps towards cognitive systems as well as a general trend toward higher product safety, reliability and reduced total cost of ownership (TCO) in many engineering fields lead to a higher importance of control and diagnosis. In this paper a first attempt is made to formulate general valid guidelines how products can be developed in order to allow and to achieve effective and efficient control and diagnosis. The guidelines are elucidated on the example of an automated guided vehicle. One main concern of this paper is the integration of control and diagnosis functionalities into the development of complete systems which include mechanical, electrical and electronic subsystems. For the development of such systems the strategies, methods and tools of systematic product development have attracted significant attention during the last decades. Today, the functionality and safety of most products is to a large degree dependent on control and diagnosis functionalities. Still, there is comparatively little research concentrating on the integration of the development of these functionalities into the overall product development processes. The paper starts with a background describing Systematic Product Development. The second section deals with the product development of the sample product. The third part clarifies the notions monitoring, control and diagnosis. The following parts summarize some insights and formulate first hypotheses concerning control and diagnosis in Systematic Product Development.

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

International Nuclear Information System (INIS)

Tsai, Tai Ming; Wang, Wei Hui

2009-01-01

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

Energy Technology Data Exchange (ETDEWEB)

Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

2009-01-15

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

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.

A SUPPORT VECTOR MACHINE APPROACH FOR DEVELOPING TELEMEDICINE SOLUTIONS: MEDICAL DIAGNOSIS

Directory of Open Access Journals (Sweden)

Mihaela GHEORGHE

2015-06-01

Full Text Available Support vector machine represents an important tool for artificial neural networks techniques including classification and prediction. It offers a solution for a wide range of different issues in which cases the traditional optimization algorithms and methods cannot be applied directly due to different constraints, including memory restrictions, hidden relationships between variables, very high volume of computations that needs to be handled. One of these issues relates to medical diagnosis, a subset of the medical field. In this paper, the SVM learning algorithm is tested on a diabetes dataset and the results obtained for training with different kernel functions are presented and analyzed in order to determine a good approach from a telemedicine perspective.

Expert systems application to plant diagnosis and sensor data validation

International Nuclear Information System (INIS)

Hashemi, S.; Hajek, B.K.; Miller, D.W.; Chandrasekaran, B.; Josephson, J.R.

1986-01-01

In a nuclear power plant, over 2000 alarms and displays are available to the operator. For any given set of alarms and displays, the operator must be able to diagnose and correct the problem (s) quickly and accurately. At the same time, the operator is expected to distinguish the plant system faults from instrumentation channel failures and drifts. Needs for plant operator aids have been considered since the accident at TMI. Many of these aids are of the form of the Safety Parameter Display Systems and offer improved methods of displaying otherwise available data to the operator in a more concise and summarized format. diagnosis, however, remains a desirable objective of an operator aid. At The Ohio State University, faculty and students in nuclear engineering and computer science have evaluated this problem. The results of these studies have shown that plant diagnosis and sensor data validation must be considered as one integral problem and cannot be isolated from one another. Otherwise, an incorrect diagnosis based on faulty instrument information might be provided to the operator. In this study, the Knowlege Based System (KBS) technology is being incorporated to accomplish a final goal of an intelligent operator aid system

Diagnosis abnormalities of limb movement in disorders of the nervous system

Science.gov (United States)

Tymchik, Gregory S.; Skytsiouk, Volodymyr I.; Klotchko, Tatiana R.; Bezsmertna, Halyna; Wójcik, Waldemar; Luganskaya, Saule; Orazbekov, Zhassulan; Iskakova, Aigul

2017-08-01

The paper deals with important issues of diagnosis early signs of diseases of the nervous system, including Parkinson's disease and other specific diseases. Small quantities of violation trajectory of spatial movement of the extremities of human disease at the primary level as the most appropriate features are studied. In modern medical practice is very actual the control the emergence of diseases of the nervous system, including Parkinson's disease. In work a model limbs with six rotational kinematic pairs for diagnosis of early signs of diseases of the nervous system is considered. subject.

An Approach to Management of Health Care and Medical Diagnosis Using of a Hybrid Disease Diagnosis System

Directory of Open Access Journals (Sweden)

Hodjat Hamidi

2017-02-01

Full Text Available Introduction: In order to simplify the information exchange within the medical diagnosis process, a collaborative software agent’s framework is presented. The purpose of the framework is to allow the automated information exchange between different medicine specialists. Methods: This study presented architecture of a hybrid disease diagnosis system. The architecture employed a learning algorithm and used soft computing to build a medical knowledge base. These machine intelligences are combined in a complementary approach to overcome the weakness of each other. To evaluate the hybrid learning algorithm and compare it with other methods, 699 samples were used in each experiment, where 60% was for training, 20% was for cross validation, and 20% for testing. Results: The results were obtained from the experiments on the breast cancer dataset. Different methods of soft computing system were merged to create diagnostic software functionality. As it is shown in the structure, the system has the ability to learn and collect knowledge that can be used in the detection of new images. Currently, the system is at the design stage. The system is to evaluate the performance of hybrid learning algorithm. The preliminary results showed a better performance of this system than other methods. However, the results can be tested with hybrid system on larger data sets to improve hybrid learning algorithm. Conclusion: The purpose of this paper was to simplify the diagnosis process of a patient by splitting the medical domain concepts (e.g., causes, effects, symptoms, tests in human body systems (e.g., respiratory, cardiovascular, though maintaining the holistic perspective through the links between common concepts.

An expert system approach for safety diagnosis

International Nuclear Information System (INIS)

Erdmann, R.C.; Sun, B.K.H.

1988-01-01

An expert system was developed with the intent to provide real-time information about an accident to an operator who is in the process of diagnosing and bringing that accident under control. Explicit use was made of probabilistic risk analysis techniques and plant accident response information in constructing this system. The expert system developed contains 70 logic rules and provides contextual messages during simulated accident sequences and logic sequence information on the entire sequence in graphical form for accident diagnosis. The present analysis focuses on integrated control system-related transients with Babcock and Wilcox-type reactors. While the system developed here is limited in extent and was built for a composite reactor, it demonstrates that an expert system may enhance the operator's capability in the control room

[Habitability and life support systems].

Science.gov (United States)

Nefedov, Iu G; Adamovich, B A

1988-01-01

This paper discusses various aspects of space vehicle habitability and life support systems. It describes variations in the chemical and microbial composition of an enclosed atmosphere during prolonged real and simulated flights. The paper gives a detailed description of life support systems and environmental investigations onboard the Mir station. It also outlines the development of space vehicle habitability and life support systems as related to future flights.

An intraoperative diagnosis of parotid gland tumors using Raman spectroscopy and support vector machine

International Nuclear Information System (INIS)

Yan, Bing; Wen, Zhining; Li, Yi; Li, Longjiang; Xue, Lili

2014-01-01

The preoperative and intraoperative diagnosis of parotid gland tumors is difficult, but is important for their surgical management. In order to explore an intraoperative diagnostic method, Raman spectroscopy is applied to detect the normal parotid gland and tumors, including pleomorphic adenoma, Warthin’s tumor and mucoepidermoid carcinoma. In the 600–1800 cm −1 region of the Raman shift, there are numerous spectral differences between the parotid gland and tumors. Compared with Raman spectra of the normal parotid gland, the Raman spectra of parotid tumors show an increase of the peaks assigned to nucleic acids and proteins, but a decrease of the peaks related to lipids. Spectral differences also exist between the spectra of parotid tumors. Based on these differences, a remarkable classification and diagnosis of the parotid gland and tumors are carried out by support vector machine (SVM), with high accuracy (96.7∼100%), sensitivity (93.3∼100%) and specificity (96.7∼100%). Raman spectroscopy combined with SVM has a great potential to aid the intraoperative diagnosis of parotid tumors and could provide an accurate and rapid diagnostic approach. (paper)

Development and realization of the open fault diagnosis system based on XPE

Science.gov (United States)

Deng, Hui; Wang, TaiYong; He, HuiLong; Xu, YongGang; Zeng, JuXiang

2005-12-01

To make the complex mechanical equipment work in good service, the technology for realizing an embedded open system is introduced systematically, including open hardware configuration, customized embedded operation system and open software structure. The ETX technology is adopted in this system, integrating the CPU main-board functions, and achieving the quick, real-time signal acquisition and intelligent data analysis with applying DSP and CPLD data acquisition card. Under the open configuration, the signal bus mode such as PCI, ISA and PC/104 can be selected and the styles of the signals can be chosen too. In addition, through customizing XPE system, adopting the EWF (Enhanced Write Filter), and realizing the open system authentically, the stability of the system is enhanced. Multi-thread and multi-task programming techniques are adopted in the software programming process. Interconnecting with the remote fault diagnosis center via the net interface, cooperative diagnosis is conducted and the intelligent degree of the fault diagnosis is improved.

75 FR 35457 - Draft of the 2010 Causal Analysis/Diagnosis Decision Information System (CADDIS)

Science.gov (United States)

2010-06-22

... Causal Analysis/Diagnosis Decision Information System (CADDIS) AGENCY: Environmental Protection Agency... site, ``2010 release of the Causal Analysis/Diagnosis Decision Information System (CADDIS).'' The..., organize, and share information useful for causal evaluations in aquatic systems. CADDIS is based on EPA's...

Remotely supported prehospital ultrasound: A feasibility study of real-time image transmission and expert guidance to aid diagnosis in remote and rural communities.

Science.gov (United States)

Eadie, Leila; Mulhern, John; Regan, Luke; Mort, Alasdair; Shannon, Helen; Macaden, Ashish; Wilson, Philip

2017-01-01

Introduction Our aim is to expedite prehospital assessment of remote and rural patients using remotely-supported ultrasound and satellite/cellular communications. In this paradigm, paramedics are remotely-supported ultrasound operators, guided by hospital-based specialists, to record images before receiving diagnostic advice. Technology can support users in areas with little access to medical imaging and suboptimal communications coverage by connecting to multiple cellular networks and/or satellites to stream live ultrasound and audio-video. Methods An ambulance-based demonstrator system captured standard trauma and novel transcranial ultrasound scans from 10 healthy volunteers at 16 locations across the Scottish Highlands. Volunteers underwent brief scanning training before receiving expert guidance via the communications link. Ultrasound images were streamed with an audio/video feed to reviewers for interpretation. Two sessions were transmitted via satellite and 21 used cellular networks. Reviewers rated image and communication quality, and their utility for diagnosis. Transmission latency and bandwidth were recorded, and effects of scanner and reviewer experience were assessed. Results Appropriate views were provided in 94% of the simulated trauma scans. The mean upload rate was 835/150 kbps and mean latency was 114/2072 ms for cellular and satellite networks, respectively. Scanning experience had a significant impact on time to achieve a diagnostic image, and review of offline scans required significantly less time than live-streamed scans. Discussion This prehospital ultrasound system could facilitate early diagnosis and streamlining of treatment pathways for remote emergency patients, being particularly applicable in rural areas worldwide with poor communications infrastructure and extensive transport times.

Residual life of technical systems; diagnosis, prediction and life extension

International Nuclear Information System (INIS)

Reinertsen, Rune

1996-01-01

The paper presents and discusses research related to residual life of non-repairable and repairable technical systems. Diagnosis of systems and extension of residual life of technical systems are also presented and discussed. This paper concludes that research published describing determination and extension of residual life as well as methods for diagnosis of non-repairable and repairable technical systems, is somewhat limited. Many papers have a rather pragmatic approach. The authors only describe special cases from their own plant and do not provide any explanation of a more academical nature. The other papers are mainly describing very specific applications of statistical models, leaving the more general case out of consideration. One of the main results of this paper is to point out these facts, and thereby identify the need for future research in this area

Monitoring and diagnosis for sensor fault detection using GMDH methodology

International Nuclear Information System (INIS)

Goncalves, Iraci Martinez Pereira

2006-01-01

The fault detection and diagnosis system is an Operator Support System dedicated to specific functions that alerts operators to sensors and actuators fault problems, and guide them in the diagnosis before the normal alarm limits are reached. Operator Support Systems appears to reduce panels complexity caused by the increase of the available information in nuclear power plants control room. In this work a Monitoring and Diagnosis System was developed based on the GMDH (Group Method of Data Handling) methodology. The methodology was applied to the IPEN research reactor IEA-R1. The system performs the monitoring, comparing GMDH model calculated values with measured values. The methodology developed was firstly applied in theoretical models: a heat exchanger model and an IPEN reactor theoretical model. The results obtained with theoretical models gave a base to methodology application to the actual reactor operation data. Three GMDH models were developed for actual operation data monitoring: the first one using just the thermal process variables, the second one was developed considering also some nuclear variables, and the third GMDH model considered all the reactor variables. The three models presented excellent results, showing the methodology utilization viability in monitoring the operation data. The comparison between the three developed models results also shows the methodology capacity to choose by itself the best set of input variables for the model optimization. For the system diagnosis implementation, faults were simulated in the actual temperature variable values by adding a step change. The fault values correspond to a typical temperature descalibration and the result of monitoring faulty data was then used to build a simple diagnosis system based on fuzzy logic. (author)

The Relationship of Learning and Performance Diagnosis at Different System Levels.

Science.gov (United States)

Lubega, Khalid

2003-01-01

Examines learning and performance diagnosis, separately and in relation to each other, as they function in organization systems; explains the relationship between learning and performance diagnosis at the individual, process, and organizational levels using a three-level performance model; and discusses types of learning, including nonlearning,…

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

Data-driven design of fault diagnosis and fault-tolerant control systems

CERN Document Server

Ding, Steven X

2014-01-01

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods, and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and...

14 CFR 417.307 - Support systems.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Support systems. 417.307 Section 417.307... TRANSPORTATION LICENSING LAUNCH SAFETY Flight Safety System § 417.307 Support systems. (a) General. (1) A flight safety system must include the systems required by this section to support the functions of the flight...

A Decision Support System for Preventing Falls in Elderly People

Directory of Open Access Journals (Sweden)

Estelle Courtial

2015-12-01

Full Text Available Preventing falls in older people is a real challenge for Public Health. This paper addresses this issue by designing a decision support system which provides a fall risk index. The proposed approach is based on three selected tests (the Timed up and go (TUG, the 30s sit-to-stand and the 4-stage balance tests, which are widely used in the medical sector for assessing mobility and balance of the elderly. During the tests, a video records the older person performing the test and thanks to an image processing algorithm, kinematics and biomechanics parameters are extracted. Based on fuzzy logic, a decision support system fuses all these data and estimates a fall risk index according to the senior's age and gender. It can also assist the Health Professional in making improved medical diagnosis relied on targeted measurements. Simulation results drawing on experimental data of 12 older persons performing the TUG test illustrate the feasibility and the effectiveness of the proposed approach. Objectively assessing the senior's motor functions and the fall risk is possible in less than 10 minutes, at low cost and in an easy and non invasive way.

Information interfaces for process plant diagnosis

International Nuclear Information System (INIS)

Lind, M.

1984-02-01

The paper describes a systematic approach to the design of information interfaces for operator support in diagnosing complex systems faults. The need of interpreting primary measured plant variables within the framework of different system representations organized into an abstraction hierarchy is identified from an analysis of the problem of diagnosing complex systems. A formalized approach to the modelling of production systems, called Multilevel Flow Modelling, is described. A MFM model specifies plant control requirements and the associated need for plant information and provide a consistent context for the interpretation of real time plant signals in diagnosis of malfunctions. The use of MFM models as a basis for functional design of the plant instrumentation system is outlined, and the use of knowledge Based (Expert) Systems for the design of man-machine interfaces is mentioned. Such systems would allow an active user participation in diagnosis and thus provide the basis for cooperative problem solving. 14 refs. (author)

A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

OpenAIRE

Xiangyu He; Shanghong He

2014-01-01

Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

Increased pulsatility index supports diagnosis of vascular parkinsonism versus idiopathic Parkinson's disease.

Science.gov (United States)

Caba, L M; Ferrairó, J I T; Torres, I M; Costa, J F V; Muñoz, R B; Martin, A L

2017-12-29

The diagnosis of vascular parkinsonism (VP) is based on a series of clinical criteria and neuroimaging findings. An increase in transcranial Doppler ultrasonography pulsatility index (PI) has been described as a frequent finding in patients with VP. We aimed to confirm this association and to determine the PI value with the highest sensitivity and specificity for diagnosis of VP. PI was determined in all patients admitted to Hospital Universitari i Politècnic La Fe due to parkinsonism between January 2012 and June 2016. We assessed the probability of having VP based on PI values in patients with a definite diagnosis of either VP or idiopathic Parkinson's disease (IPD). A ROC curve was created to determine the PI value with the highest sensitivity and specificity. We assessed a total of 146 patients with suspected parkinsonism; 54 (37%) were diagnosed with IPD and 15 (10%) with VP. Patients with VP were significantly older than those with IPD (mean age of 79 vs 68.5, P=.00144) and had a higher PI (median of 1.29 [IQR: 1.09-1.38] vs 0.96 [IQR: 0.89-1.16], P=.01328). In our sample, a PI of 1.09 conferred 84% sensitivity and 70% specificity. In our series, the PI was significantly higher in patients with VP than in those with IPD. We therefore support the use of transcranial Doppler ultrasonography for the initial assessment of elderly patients with akinetic-rigid syndrome. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

Spill operation system decision support system

International Nuclear Information System (INIS)

Clark, R.

1992-01-01

The MSRC Spill Operation System (SOS) is a tool for the support of decision-making at the time of a catastrophic oil spill. SOS provides MSRC decision-makers with access to information about the source of the spill, the spill environment, and the availability of spill response resources. This system is designed to meet the information needs of a Response Supervisor, an Environmental Advisor, Logistics/Maintenance Supervisor, Operations Supervisor, and the MSRC Regional General Manager. The SOS project Objectives are: (1) integrate currently available data, systems, and technologies; (2) develop an application that effectively supports mobilized operations and can be adapted to support normal operations; (3) ensure that the development of computer applications is driven by user needs and not by technology; and (4) coordinate with government and other industry organizations to avoid duplication of effort. Design Objectives for SOS are: (1) centralize management information storage while decentralizing decision making capabilities; (2) boost User confidence by providing a system that is easy to learn, easy to use, and is open-quotes Sailor Proofclose quotes; and (3) use visualization technology in providing spill related information. This approach includes the use of Geographic Information System (GIS) technology for maps and geographically associated resource; and support MSRC's concept of operation which includes - a swift notification of response personnel; fast mobilization of response resources; and accurate tracking of resources during a spill. MSRC is organized into five responsibility regions

Formalisation for decision support in anaesthesiology

NARCIS (Netherlands)

Renardel de Lavalette, G R; Groenboom, R.; Rotterdam, E; van Harmelen, F; ten Teije, A; de Geus, F.

1997-01-01

This paper reports on research for decision support for anaesthesiologists at the University Hospital in Groningen, the Netherlands. Based on CAROLA, an existing automated operation documentation system, we designed a support environment that will assist in real-time diagnosis. The core of the work

Knowledge based support for real time application of multiagent control and automation in electric power systems

DEFF Research Database (Denmark)

Saleem, Arshad; Nordstrom, Lars; Lind, Morten

2011-01-01

This paper presents a mechanism for developing knowledge based support for real time application of multiagent systems (MAS) in control, automation and diagnosis of electric power systems. In particular it presents a way for autonomous agents to utilize a qualitative means-ends based model...... for reasoning about control situations. The proposed mechanism has been used in different scenarios of electric power distribution system protection and control. Results show that agents can use local models of their environment and coordinate with other agents to analyze and understand a disturbance situation...

NASA Advanced Exploration Systems: Advancements in Life Support Systems

Science.gov (United States)

Shull, Sarah A.; Schneider, Walter F.

2016-01-01

The NASA Advanced Exploration Systems (AES) Life Support Systems (LSS) project strives to develop reliable, energy-efficient, and low-mass spacecraft systems to provide environmental control and life support systems (ECLSS) critical to enabling long duration human missions beyond low Earth orbit (LEO). Highly reliable, closed-loop life support systems are among the capabilities required for the longer duration human space exploration missions assessed by NASA’s Habitability Architecture Team.

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.

Social support and delays seeking care after HIV diagnosis, North Carolina, 2000–2006

OpenAIRE

McCoy, Sandra I.; Strauss, Ronald P.; MacDonald, Pia D. M.; Leone, Peter A.; Eron, Joseph J.; Miller, William C.

2009-01-01

Many adults in the United States enter primary care late in the course of HIV infection, countering the clinical benefits of timely HIV services and missing opportunities for risk reduction. Our objective was to determine if perceived social support was associated with delay entering care after an HIV diagnosis. Two hundred sixteen patients receiving primary care at a large, university-based HIV outpatient clinic in North Carolina were included in the study. Dimensions of functional social su...

Possibilistic networks for uncertainty knowledge processing in student diagnosis

Directory of Open Access Journals (Sweden)

Adina COCU

2006-12-01

Full Text Available In this paper, a possibilistic network implementation for uncertain knowledge modeling of the diagnostic process is proposed as a means to achieve student diagnosis in intelligent tutoring system. This approach is proposed in the object oriented programming domain for diagnosis of students learning errors and misconception. In this expertise domain dependencies between data exist that are encoded in the structure of network. Also, it is available qualitative information about these data which are represented and interpreted with qualitative approach of possibility theory. The aim of student diagnosis system is to ensure an adapted support for the student and to sustain the student in personalized learning process and errors explanation.

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.

The Intelligent System of Cardiovascular Disease Diagnosis Based on Extension Data Mining

Science.gov (United States)

Sun, Baiqing; Li, Yange; Zhang, Lin

This thesis gives the general definition of the concepts of extension knowledge, extension data mining and extension data mining theorem in high dimension space, and also builds the IDSS integrated system by the rough set, expert system and neural network, develops the relevant computer software. From the diagnosis tests, according to the common diseases of myocardial infarctions, angina pectoris and hypertension, and made the test result with physicians, the results shows that the sensitivity, specific and accuracy diagnosis by the IDSS are all higher than the physicians. It can improve the rate of the accuracy diagnosis of physician with the auxiliary help of this system, which have the obvious meaning in low the mortality, disability rate and high the survival rate, and has strong practical values and further social benefits.

49 CFR 193.2609 - Support systems.

Science.gov (United States)

2010-10-01

... 49 Transportation 3 2010-10-01 2010-10-01 false Support systems. 193.2609 Section 193.2609 Transportation Other Regulations Relating to Transportation (Continued) PIPELINE AND HAZARDOUS MATERIALS SAFETY...: FEDERAL SAFETY STANDARDS Maintenance § 193.2609 Support systems. Each support system or foundation of each...

Development of water chemistry diagnosis system for BWR primary loop

International Nuclear Information System (INIS)

Nagase, Makoto; Asakura, Yamato; Sakagami, Masaharu; Uchida, Shunsuke; Ohsumi, Katsumi.

1988-01-01

The prototype of a water chemistry diagnosis system for BWR primary loop has been developed. Its purposes are improvement of water chemistry control and reduction of the work burden on plant chemistry personnel. It has three main features as follows. (1) Intensifying the observation of water chemistry conditions by variable sampling intervals based on the on-line measured data. (2) Early detection of water chemistry data trends using a second order regression curve which is calculated from the measured data, and then searching the cause of anomaly if anything (3) Diagnosis of Fe concentration in feedwater using model simulations, in order to lower the radiation level in the primary system. (author)

Development of decommissioning engineering support system for fugen. Development of support system during actual dismantlement works

International Nuclear Information System (INIS)

Masanori Izumi; Yukihiro Iguchi; Yoshiki Kannehira

2005-01-01

The Advanced Thermal Reactor, Fugen Nuclear Power Station was permanently shut down in March 2003, and is now preparing for decommissioning. We have been developing Decommissioning Engineering Support System (DEXUS) aimed at planning optimal dismantlement process and carrying out dismantlement work safely and precisely. DEXUS consists of 'decommissioning planning support system' and 'dismantling support system'. The dismantling support system is developed aiming at using during actual dismantling work. It consists of three subsystems such as 'Worksite Visualization System', 'Dismantling Data Collection System' and 'Generated Waste Management System'. 'Worksite Visualization System' is a support system designed to provide the necessary information to workers during actual dismantlement works. And this system adopts AR (Augmented Reality) technology, overlapping calculation information into real world. 'Dismantling Data Collection System' is to collect necessary data for improving accuracy of decommissioning planning by evaluating work content and worker equipage, work time for dismantlement works. 'Generated Waste Management system' is a system recording necessary information by attaching the barcode to dismantled wastes or the containers. We can get the information of generated waste by recording generation place, generated time, treatment method and the contents. These subsystems enable to carry out reasonable and safe decommissioning of Fugen. In addition, we expect that those systems will be used for decommissioning of other nuclear facilities in the future. (authors)

A computer program to reduce the time of diagnosis in complex systems

International Nuclear Information System (INIS)

Arellano-Gomez, Juan; Romero-Rubio, Omar U.

2006-01-01

In Nuclear Power Plants (NPPs), the time that some systems are allowed to be down is frequently very limited. Thus, when one of these systems fails, diagnosis and repair must be quickly performed in order to get the system back to an operative state. Unfortunately, in complex systems, a considerable amount of the total repair time could be spent in the duty of diagnosis. For this reason, it seems very useful to provide maintenance personnel with a systematic approach to system failure diagnosis, capable to minimize the time required to effectively identify the causes of system malfunction. In this context, the expert systems technology has been widely applied in several disciplines to successfully develop diagnostic systems. Obviously, an important input to develop these expert systems is, of course, knowledge; this knowledge includes both formal knowledge and experience on what faults could occur, how these faults occur, which are the effects of these faults, what could be inferred from symptoms, etc. Due to their logical nature, those fault trees developed by expert analysts during risk studies could also be used as the source of knowledge of diagnostic expert systems (DES); however, these fault trees must be expanded to include symptoms because, typically, diagnosis is performed by inferring the causes of system malfunction from symptoms. This paper presents SANA (Symptom Analyzer), a new software package specially designed to develop diagnostic expert systems. The main feature of this software is that it exploits the knowledge stored in fault trees (in particular, expanded fault trees) to generate very efficient diagnostic strategies. These strategies guide diagnostic activities seeking to minimize the time required to identify those components which are responsible of the system failure. Besides, the generated strategies 'emulate' the way experienced technicians proceed in order to diagnose the causes of system failure (i.e. by recognizing categories of

Support system of a structure on a support base

International Nuclear Information System (INIS)

Arene, G.; Renaux, C.; Minguet, J.L.; Chantot, H.

1984-01-01

Two series of strips are fixed to the structure to be supported and to the base to define each one a closed convex envelope; the strips are flexible in the radial direction with regard to the envelope. The two series of strips are connected by a treillis of rigid bars set to form juxtaposed V or X. A good transversal rigidity and a certain radial flexibility are obtained. The invention can be applied to a fast neutron nuclear reactor, the reactor comprising a vertical axis vessel filled with liquid metal; the vessel rests on a support foundation by means of the support system proposed by the invention. The support system allows the supported structure to resist the effects of an eventual earthquake and brutal temperature variations [fr

Computerized operator support system with new man-machine interface for BWR power plants

International Nuclear Information System (INIS)

Monta, K.; Naito, N.; Sugawara, M.; Sato, N.; Mori, N.; Tai, I.; Fukumoto, A.; Tsuchida, M.

1984-01-01

Improvement of the man-machine interface of nuclear power plants is an important contribution to the further enhancement of operational safety. In addition, recent advances in computer technology seem to offer the greatest opportunity to date for achieving improvement in the man-machine interface. The development of a computerized operator support system for BWRs has been undertaken since 1980 with the support of the Japanese Government. The conceptual design of this system is based on the role of the operators. The main functions are standby system management, disturbance analysis and post-trip operational guidance. The objective of the standby system management is to monitor the standby status of the engineered safety feature during normal operation to assure its proper functioning at the onset of emergency situations. The disturbance analysis system detects disturbances in the plant in their early stages and informs the plant operators about, for example, the cause of the disturbances, the plant status and possible propagations. Consequently, operators can take corrective actions to prevent unnecessary plant shutdown. The objective of the post trip operational guide is to support operators in diagnosis and corrective action after a plant trip. Its functions are to monitor the performance of the engineered safety feature, to identify the plant status and to guide the appropriate corrective action to achieve safe plant shutdown. The information from the computerized operator support system is supplied to operators through a colour CRT operator console. The authors have evaluated the performance of various new man-machine interfacing tools and proposed a new operator console design. A prototype system has been developed and verification/validation is proceeding with a BWR plant simulator. (author)

Method of modelization assistance with bond graphs and application to qualitative diagnosis of physical systems

International Nuclear Information System (INIS)

Lucas, B.

1994-05-01

After having recalled the usual diagnosis techniques (failure index, decision tree) and those based on an artificial intelligence approach, the author reports a research aimed at exploring the knowledge and model generation technique. He focuses on the design of an aid to model generation tool and aid-to-diagnosis tool. The bond graph technique is shown to be adapted to the aid to model generation, and is then adapted to the aid to diagnosis. The developed tool is applied to three projects: DIADEME (a diagnosis system based on physical model), the improvement of the SEXTANT diagnosis system (an expert system for transient analysis), and the investigation on an Ariane 5 launcher component. Notably, the author uses the Reiter and Greiner algorithm

Initial diagnosis and treatment in first-episode psychosis: can an operationalized diagnostic classification system enhance treating clinicians' diagnosis and the treatment chosen?

LENUS (Irish Health Repository)

Coentre, Ricardo

2011-05-01

Diagnosis during the initial stages of first-episode psychosis is particularly challenging but crucial in deciding on treatment. This is compounded by important differences in the two major classification systems, International Classification of Diseases, 10th revision (ICD-10) and Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV). We aimed to compare the concordance between an operationalized diagnosis using Operational Criteria Checklist (OPCRIT) and treating clinician-generated diagnosis in first episode psychosis diagnosis and its correlation with treatment prescribed.

Testing and diagnosis of the cause of increased vibration of the fan plant's support structure

Directory of Open Access Journals (Sweden)

Varju Đerđ

2015-01-01

Full Text Available This paper presents a procedure of determining the causes of increased vibration of a fan plant and its support structure in the PUC 'Subotička toplana'. Excessive vibrations were observed following the installation of the frequency converter, thus a methodological approach of testing-analysis-diagnosis has been applied. Based on the definition of the causes of this problem, the paper also suggests possible repair procedures.

Active Fault Diagnosis in Sampled-data Systems

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2015-01-01

The focus in this paper is on active fault diagnosis (AFD) in closed-loop sampleddata systems. Applying the same AFD architecture as for continuous-time systems does not directly result in the same set of closed-loop matrix transfer functions. For continuous-time systems, the LFT (linear fractional...... transformation) structure in the connection between the parametric faults and the matrix transfer function (also known as the fault signature matrix) applied for AFD is not directly preserved for sampled-data system. As a consequence of this, the AFD methods cannot directly be applied for sampled-data systems....... Two methods are considered in this paper to handle the fault signature matrix for sampled-data systems such that standard AFD methods can be applied. The first method is based on a discretization of the system such that the LFT structure is preserved resulting in the same LFT structure in the fault...

Broad Categories for the Diagnosis of Eating Disorders (BCD-ED): An Alternative System for Classification

Science.gov (United States)

Walsh, B. Timothy; Sysko, Robyn

2009-01-01

Eating Disorder Not Otherwise Specified (EDNOS), a residual category in DSM-IV, is the most commonly used eating disorder diagnosis in clinical settings. However, the features of individuals with EDNOS are heterogeneous and difficult to characterize. A diagnostic scheme, termed Broad Categories for the Diagnosis of Eating Disorders (BCD-ED), is proposed to diminish use of the EDNOS category markedly while preserving the existing eating disorder categories. The BCD-ED scheme consists of three broad categories, in a hierarchical relationship, consisting of: Anorexia Nervosa and Behaviorally Similar disorders, Bulimia Nervosa and Behaviorally Similar Disorders, Binge Eating Disorder and Behaviorally Similar Disorders, and a residual category of EDNOS. The advantages and disadvantages of adopting this scheme for DSM-V are considered, and issues relevant to BCD-ED are discussed. Specifically, we review the proportion of individuals with DSM-IV EDNOS that would be re-classified under the BCD-ED system, support for the hierarchy of the three categories, and the potential risk of “overdiagnosis.” PMID:19650083

Study on fault diagnosis and load feedback control system of combine harvester

Science.gov (United States)

Li, Ying; Wang, Kun

2017-01-01

In order to timely gain working status parameters of operating parts in combine harvester and improve its operating efficiency, fault diagnosis and load feedback control system is designed. In the system, rotation speed sensors were used to gather these signals of forward speed and rotation speeds of intermediate shaft, conveying trough, tangential and longitudinal flow threshing rotors, grain conveying auger. Using C8051 single chip microcomputer (SCM) as processor for main control unit, faults diagnosis and forward speed control were carried through by rotation speed ratio analysis of each channel rotation speed and intermediate shaft rotation speed by use of multi-sensor fused fuzzy control algorithm, and these processing results would be sent to touch screen and display work status of combine harvester. Field trials manifest that fault monitoring and load feedback control system has good man-machine interaction and the fault diagnosis method based on rotation speed ratios has low false alarm rate, and the system can realize automation control of forward speed for combine harvester.

Suboptimal processor for anomaly detection for system surveillance and diagnosis

Energy Technology Data Exchange (ETDEWEB)

Ciftcioglu, Oe.; Hoogenboom, J.E.; Dam, H. van

1989-06-01

Anomaly detection for nuclear reactor surveillance and diagnosis is described. The residual noise obtained as a result of autoregressive (AR) modelling is essential to obtain high sensitivity for anomaly detection. By means of the method of hypothesis testing a suboptimal anomaly detection processor is devised for system surveillance and diagnosis. Experiments are carried out to investigate the performance of the processor, which is in particular of interest for on-line and real-time applications.

Supporting Multiple Cognitive Processing Styles Using Tailored Support Systems

International Nuclear Information System (INIS)

Tuan Q. Tran; Karen M. Feigh; Amy R. Pritchett

2007-01-01

According to theories of cognitive processing style or cognitive control mode, human performance is more effective when an individual's cognitive state (e.g., intuition/scramble vs. deliberate/strategic) matches his/her ecological constraints or context (e.g., utilize intuition to strive for a 'good-enough' response instead of deliberating for the 'best' response under high time pressure). Ill-mapping between cognitive state and ecological constraints are believed to lead to degraded task performance. Consequently, incorporating support systems which are designed to specifically address multiple cognitive and functional states e.g., high workload, stress, boredom, and initiate appropriate mitigation strategies (e.g., reduce information load) is essential to reduce plant risk. Utilizing the concept of Cognitive Control Models, this paper will discuss the importance of tailoring support systems to match an operator's cognitive state, and will further discuss the importance of these ecological constraints in selecting and implementing mitigation strategies for safe and effective system performance. An example from the nuclear power plant industry illustrating how a support system might be tailored to support different cognitive states is included

FileNet's BPM life-cycle support

NARCIS (Netherlands)

Netjes, M.; Reijers, H.A.; Aalst, van der W.M.P.

2006-01-01

Business Process Management (BPM) systems provide a broad range of facilities to enact and manage operational business processes. Ideally, these systems should provide support for the complete BPM life-cycle: (re)design, configuration, execution, control, and diagnosis of processes. In the research

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.

Diagnosis System for Diabetic Retinopathy and Glaucoma Screening to Prevent Vision Loss

Directory of Open Access Journals (Sweden)

Siva Sundhara Raja DHANUSHKODI

2014-03-01

Full Text Available Aim: Diabetic retinopathy (DR and glaucoma are two most common retinal disorders that are major causes of blindness in diabetic patients. DR caused in retinal images due to the damage in retinal blood vessels, which leads to the formation of hemorrhages spread over the entire region of retina. Glaucoma is caused due to hypertension in diabetic patients. Both DR and glaucoma affects the vision loss in diabetic patients. Hence, a computer aided development of diagnosis system for Diabetic retinopathy and Glaucoma screening is proposed in this paper to prevent vision loss. Method: The diagnosis system of DR consists of two stages namely detection and segmentation of fovea and hemorrhages. The diagnosis system of glaucoma screening consists of three stages namely blood vessel segmentation, Extraction of optic disc (OD and optic cup (OC region and determination of rim area between OD and OC. Results: The specificity and accuracy for hemorrhages detection is found to be 98.47% and 98.09% respectively. The accuracy for OD detection is found to be 99.3%. This outperforms state-of-the-art methods. Conclusion: In this paper, the diagnosis system is developed to classify the DR and glaucoma screening in to mild, moderate and severe respectively.

Operator decision support system for integrated wastewater management including wastewater treatment plants and receiving water bodies.

Science.gov (United States)

Kim, Minsoo; Kim, Yejin; Kim, Hyosoo; Piao, Wenhua; Kim, Changwon

2016-06-01

An operator decision support system (ODSS) is proposed to support operators of wastewater treatment plants (WWTPs) in making appropriate decisions. This system accounts for water quality (WQ) variations in WWTP influent and effluent and in the receiving water body (RWB). The proposed system is comprised of two diagnosis modules, three prediction modules, and a scenario-based supporting module (SSM). In the diagnosis modules, the WQs of the influent and effluent WWTP and of the RWB are assessed via multivariate analysis. Three prediction modules based on the k-nearest neighbors (k-NN) method, activated sludge model no. 2d (ASM2d) model, and QUAL2E model are used to forecast WQs for 3 days in advance. To compare various operating alternatives, SSM is applied to test various predetermined operating conditions in terms of overall oxygen transfer coefficient (Kla), waste sludge flow rate (Qw), return sludge flow rate (Qr), and internal recycle flow rate (Qir). In the case of unacceptable total phosphorus (TP), SSM provides appropriate information for the chemical treatment. The constructed ODSS was tested using data collected from Geumho River, which was the RWB, and S WWTP in Daegu City, South Korea. The results demonstrate the capability of the proposed ODSS to provide WWTP operators with more objective qualitative and quantitative assessments of WWTP and RWB WQs. Moreover, the current study shows that ODSS, using data collected from the study area, can be used to identify operational alternatives through SSM at an integrated urban wastewater management level.

[Public health impact of a remote diagnosis system implemented in regional and district hospitals in Paraguay].

Science.gov (United States)

Galván, Pedro; Velázquez, Miguel; Benítez, Gualberto; Ortellado, José; Rivas, Ronald; Barrios, Antonio; Hilario, Enrique

2017-06-08

Determine the viability of a remote diagnosis system implemented to provide health care to remote and scattered populations in Paraguay. The study was conducted in all regional and general hospitals in Paraguay, and in the main district hospitals in the country's 18 health regions. Clinical data, tomographic images, sonography, and electrocardiograms (ECGs) of patients who needed a diagnosis by a specialized physician were entered into the system. This information was sent to specialists in diagnostic imaging and in cardiology for remote diagnosis and the report was then forwarded to the hospitals connected to the system. The cost-benefit and impact of the remote diagnosis tool was analyzed from the perspective of the National Health System. Between January 2014 and May 2015, a total of 34 096 remote diagnoses were made in 25 hospitals in the Ministry of Health's telemedicine system. The average unit cost of remote diagnosis was US$2.6 per ECG, tomography, and sonography, while the unit cost of "face-to-face" diagnosis was US$11.8 per ECG, US$68.6 per tomography, and US$21.5 per sonography. As a result of remote diagnosis, unit costs were 4.5 times lower for ECGs; 26.4 times lower for tomography, and 8.3 times lower for sonography. In monetary terms, implementation of the remote diagnosis system during the 16 months of the study led to average savings of US$2 420 037. Paraguay has a remote diagnosis system for electrocardiography, tomography, and sonography, using low-cost information and communications technologies (ICTs) based on free software that is scalable to other types of remote diagnostic studies of interest for public health. Implementation of remote diagnosis helped to strengthen the integrated network of health services and programs, enabling professionals to optimize their time and productivity, while improving quality, increasing access and equity, and reducing costs.

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.

Operator support systems activities at EPRI

International Nuclear Information System (INIS)

Naser, J.A.

1993-01-01

The integration of operator support systems supports the nuclear power plant goals of improved availability and reliability, enhanced safety, reduced operations and maintenance costs, and improved productivity. Two major aspects which supports this integration are discussed in this paper. The first is the plant communications and computing architecture which provides the infrastructure that allows the integration to exist in a easy to implement manner. Open systems concepts are utilized to guarantee interoperability of systems and interchangeability of equipment. The second is the EPRI Plant-Window System which supplies the interface between the human and the plant systems. It implements common human-machine interfaces amongst systems and supports the implementation of diagnostic and decision aids. Work in both of these areas is being done as part of the EPRI Instrumentation and Control Upgrade Program. A number of operator support systems have been developed and are in various stages of implementation, testing and utilization. Two of these, the RWCU and the EOPTS, are described here. 5 refs, 14 figs

Computer-supported quality control in X-ray diagnosis

International Nuclear Information System (INIS)

Maier, W.; Klotz, E.

1989-01-01

Quality control of X-ray facilities in radiological departments of large hospitals is possible only if the instrumentation used for measurements is interfaced to a computer. The central computer helps to organize the measurements as well as analyse and record the results. It can also be connected to a densitometer and camera for evaluating radiographs of test devices. Other quality control tests are supported by a mobile station with equipment for non-invasive dosimetry measurements. Experience with a computer-supported system in quality control of film and film processing is described and the evaluation methods of ANSI and the German industrial standard DIN are compared. The disadvantage of these methods is the exclusion of film quality parameters, which can make processing control almost worthless. (author)

Development of a New Safety Culture Assessment Method for Nuclear Power Plants (NPPs) (Decision support system for an OPR-1000 type power plant)

Energy Technology Data Exchange (ETDEWEB)

Kim, Yo Chan; Jung, Won Dea [Korea Atomic Energy Institute, Daejeon (Korea, Republic of)

2014-08-15

To support operators in the diagnosis of abnormal operating procedures (AOPs), we developed a decision support system for an OPR-1000 type power plant. This system aids operators who identify abnormal situations from annunciated alarms using three functions: an AOP flowchart, AOP search, and alarm simulation. This paper introduces the developed system, compares the characteristics of the functions in the system, and discusses the strength of this approach compared with other previous research. It is expected that the advanced functions may elevate the performance and reliability of operators who manage abnormal situations.

Development of a New Safety Culture Assessment Method for Nuclear Power Plants (NPPs) (Decision support system for an OPR-1000 type power plant)

International Nuclear Information System (INIS)

Kim, Yo Chan; Jung, Won Dea

2014-01-01

To support operators in the diagnosis of abnormal operating procedures (AOPs), we developed a decision support system for an OPR-1000 type power plant. This system aids operators who identify abnormal situations from annunciated alarms using three functions: an AOP flowchart, AOP search, and alarm simulation. This paper introduces the developed system, compares the characteristics of the functions in the system, and discusses the strength of this approach compared with other previous research. It is expected that the advanced functions may elevate the performance and reliability of operators who manage abnormal situations

Design Rules for Life Support Systems

Science.gov (United States)

Jones, Harry

2002-01-01

This paper considers some of the common assumptions and engineering rules of thumb used in life support system design. One general design rule is that the longer the mission, the more the life support system should use recycling and regenerable technologies. A more specific rule is that, if the system grows more than half the food, the food plants will supply all the oxygen needed for the crew life support. There are many such design rules that help in planning the analysis of life support systems and in checking results. These rules are typically if-then statements describing the results of steady-state, "back of the envelope," mass flow calculations. They are useful in identifying plausible candidate life support system designs and in rough allocations between resupply and resource recovery. Life support system designers should always review the design rules and make quick steady state calculations before doing detailed design and dynamic simulation. This paper develops the basis for the different assumptions and design rules and discusses how they should be used. We start top-down, with the highest level requirement to sustain human beings in a closed environment off Earth. We consider the crew needs for air, water, and food. We then discuss atmosphere leakage and recycling losses. The needs to support the crew and to make up losses define the fundamental life support system requirements. We consider the trade-offs between resupplying and recycling oxygen, water, and food. The specific choices between resupply and recycling are determined by mission duration, presence of in-situ resources, etc., and are defining parameters of life support system design.

Introduction to Life Support Systems

Science.gov (United States)

Perry, Jay

2017-01-01

This course provides an introduction to the design and development of life support systems to sustain humankind in the harsh environment of space. The life support technologies necessary to provide a respirable atmosphere and clean drinking water are emphasized in the course. A historical perspective, beginning with open loop systems employed aboard the earliest crewed spacecraft through the state-of-the-art life support technology utilized aboard the International Space Station today, will provide a framework for students to consider applications to possible future exploration missions and destinations which may vary greatly in duration and scope. Development of future technologies as well as guiding requirements for designing life support systems for crewed exploration missions beyond low-Earth orbit are also considered in the course.

Radiological diagnosis systems - problems and solutions

International Nuclear Information System (INIS)

Koeppe, P.

1983-01-01

An essential part of the work in a radiological diagnosis department is to produce the (written) physicians' reports. The majority is generally without any findings or ''normal pathologic'', only a minor part needs a special treatment. With respect to the quantity of this work, automation of the routine report writing was early attempted ley means of technical aids. Text processing systems and computers were used. The transition between these techniques is gradual. The article is limited to the use of computers in automation of report writing. (orig.) [de

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)

An advanced design of non-radioactive image capturing and management system for applications in non-invasive skin disorder diagnosis

Science.gov (United States)

Liu, Carol Y. B.; Luk, David C. K.; Zhou, Kany S. Y.; So, Bryan M. K.; Louie, Derek C. H.

2015-03-01

Due to the increasing incidences of malignant melanoma, there is a rising demand for assistive technologies for its early diagnosis and improving the survival rate. The commonly used visual screening method is with limited accuracy as the early phase of melanoma shares many clinical features with an atypical nevus, while conventional dermoscopes are not user-friendly in terms of setup time and operations. Therefore, the development of an intelligent and handy system to assist the accurate screening and long-term monitoring of melanocytic skin lesions is crucial for early diagnosis and prevention of melanoma. In this paper, an advanced design of non-invasive and non-radioactive dermoscopy system was reported. Computer-aided simulations were conducted for optimizing the optical design and uniform illumination distribution. Functional prototype and the software system were further developed, which could enable image capturing at 10x amplified and general modes, convenient data transmission, analysis of dermoscopic features (e.g., asymmetry, border irregularity, color, diameter and dermoscopic structure) for assisting the early detection of melanoma, extract patient information (e.g. code, lesion location) and integrate with dermoscopic images, thus further support long term monitoring of diagnostic analysis results. A clinical trial study was further conducted on 185 Chinese children (0-18 years old). The results showed that for all subjects, skin conditions diagnosed based on the developed system accurately confirmed the diagnoses by conventional clinical procedures. Besides, clinical analysis on dermoscopic features and a potential standard approach by the developed system to support identifying specific melanocytic patterns for dermoscopic examination in Chinese children were also reported.

Application of mobile computers in a measuring system supporting examination of posture diseases

Science.gov (United States)

Piekarski, Jacek; Klimiec, Ewa; Zaraska, Wiesław

2013-07-01

Measuring system designed and manufactured by the authors and based on mobile computers (smartphones and tablets) working as data recorders has been invented to support diagnosis of orthopedic, especially feet, diseases. The basic idea is to examine a patient in his natural environment, during the usual activities (such as walking or running). The paper describes the proposed system with sensors manufactured from piezoelectric film (PVDF film) and placed in the shoe insole. The mechanical reliability of PVDF film is excellent, though elimination of the pyroelectric effect is required. A possible solution of the problem and the test results are presented in the paper. Data recording is based on wireless transmission to a mobile device used as a data logger.

Use of bactec 460 TB system in the diagnosis of tuberculosis

Directory of Open Access Journals (Sweden)

Rodrigues C

2007-01-01

Full Text Available Purpose : To evaluate, the efficacy of BACTEC 460 TB system for the diagnosis of tuberculosis in a tertiary care hospital in Mumbai, India. Methods : We compared 12,726 clinical specimens using BACTEC 460 TB system and conventional method for detection of Mycobacterium tuberculosis over a period of six years. Result: The overall recovery rate was 39% by BACTEC technique and 29% using Lowenstein-Jensen (LJ medium. An average detection time for B actec0 460 TB system was found to be 13.3 days and 15.3 days as against 31.2 days and 35.3 days by LJ method for respiratory and nonrespiratory specimens respectively. The average reporting time for drug susceptibility results ranged from 6-10 days for the BACTEC 460 TB system. Conclusions: The BACTEC system is a good system for level II laboratories, especially in the diagnosis of extrapulmonary and smear negative tuberculosis.

Fault diagnosis

Science.gov (United States)

Abbott, Kathy

1990-01-01

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

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.

Mathematical Modeling Of Life-Support Systems

Science.gov (United States)

Seshan, Panchalam K.; Ganapathi, Balasubramanian; Jan, Darrell L.; Ferrall, Joseph F.; Rohatgi, Naresh K.

1994-01-01

Generic hierarchical model of life-support system developed to facilitate comparisons of options in design of system. Model represents combinations of interdependent subsystems supporting microbes, plants, fish, and land animals (including humans). Generic model enables rapid configuration of variety of specific life support component models for tradeoff studies culminating in single system design. Enables rapid evaluation of effects of substituting alternate technologies and even entire groups of technologies and subsystems. Used to synthesize and analyze life-support systems ranging from relatively simple, nonregenerative units like aquariums to complex closed-loop systems aboard submarines or spacecraft. Model, called Generic Modular Flow Schematic (GMFS), coded in such chemical-process-simulation languages as Aspen Plus and expressed as three-dimensional spreadsheet.

Development of condition monitoring and diagnosis system for standby diesel generator

Energy Technology Data Exchange (ETDEWEB)

Choi, Kwang Hee; Park, Jong Hyuck; Park, Jong Eun [Korea Electric Power Research Institute, Daejeon (Korea, Republic of)

2009-05-15

The emergency diesel generator (EDG) of the nuclear power plant is designed to supply the power to the nuclear on Station Black Out (SBO) condition. The operation reliability of onsite emergency diesel generator should be ensured by a condition monitoring system designed to monitor and analysis the condition of diesel generator. For this purpose, we have developed the online condition monitoring and diagnosis system for the wolsong unit 3 and 4 standby diesel generator including diesel engine performance. In this paper, technologies of condition monitoring and diagnosis system (SDG MDS) for the wolsong standby diesel generator are described. By using the condition monitoring module of the SDG MDS, performance monitoring function for major operating parameters of EDG reliability program required by Reg. guide 1.155 can be operated as on line monitoring system.

Development of condition monitoring and diagnosis system for standby diesel generator

International Nuclear Information System (INIS)

Choi, Kwang Hee; Park, Jong Hyuck; Park, Jong Eun

2009-01-01

The emergency diesel generator (EDG) of the nuclear power plant is designed to supply the power to the nuclear on Station Black Out (SBO) condition. The operation reliability of onsite emergency diesel generator should be ensured by a condition monitoring system designed to monitor and analysis the condition of diesel generator. For this purpose, we have developed the online condition monitoring and diagnosis system for the wolsong unit 3 and 4 standby diesel generator including diesel engine performance. In this paper, technologies of condition monitoring and diagnosis system (SDG MDS) for the wolsong standby diesel generator are described. By using the condition monitoring module of the SDG MDS, performance monitoring function for major operating parameters of EDG reliability program required by Reg. guide 1.155 can be operated as on line monitoring system

OPAD: An expert system for research reactor operations and fault diagnosis using probabilistic safety assessment tools

International Nuclear Information System (INIS)

Verma, A.K.; Varde, P.V.; Sankar, S.; Prakash, P.

1996-01-01

A prototype Knowledge Based (KB) operator Adviser (OPAD) system has been developed for 100 MW(th) Heavy Water moderated, cooled and Natural Uranium fueled research reactor. The development objective of this system is to improve reliability of operator action and hence the reactor safety at the time of crises as well as normal operation. The jobs performed by this system include alarm analysis, transient identification, reactor safety status monitoring, qualitative fault diagnosis and procedure generation in reactor operation. In order to address safety objectives at various stages of the Operator Adviser (OPAD) system development the Knowledge has been structured using PSA tools/information in an shell environment. To demonstrate the feasibility of using a combination of KB approach with PSA for operator adviser system, salient features of some of the important modules (viz. FUELEX, LOOPEX and LOCAEX) have been discussed. It has been found that this system can serve as an efficient operator support system

Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines

Directory of Open Access Journals (Sweden)

Joanna Jaworek-Korjakowska

2016-01-01

Full Text Available Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification. Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs. Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination. Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions.

Professional Growth & Support System Self-Assessment

Science.gov (United States)

Education Resource Strategies, 2013

2013-01-01

The "Professional Growth & Support System Self-Assessment" is designed to help school systems evaluate their current Professional Growth & Support strategy. The self-assessment is organized around the "Eight Principles of Strategic Professional Growth & Support." Each section allows school leaders to identify the…

REACTOR: an expert system for diagnosis and treatment of nuclear reactor accidents

International Nuclear Information System (INIS)

Nelson, W.R.

1982-01-01

REACTOR is an expert system under development at EG and G Idaho, Inc., that will assist operators in the diagnosis and treatment of nuclear reactor accidents. This paper covers the background of the nuclear industry and why expert system technology may prove valuable in the reactor control room. Some of the basic features of the REACTOR system are discussed, and future plans for validation and evaluation of REACTOR are presented. The concept of using both event-oriented and function-oriented strategies for accident diagnosis is discussed. The response tree concept for representing expert knowledge is also introduced

Role of fluorographic examinations in diagnosis of respiratory system diseases

International Nuclear Information System (INIS)

Vil'derman, A.M.; Tsurkan, E.P.; Moskovchuk, A.F.

1984-01-01

Materials are considered on the role of fluorography in diagnosis of posttuberculous changes and chromic respiratory system diseases during total epidemiologic examination of 7791 adults from urban and rural population. A scheme is developed that characterize diagnosed pathology of respiratory organs with references to medical establishments rendering medical supervision and forms of supervision. It is shown that fluorograhic examination of the population provide an early diagnosis of both tuberculosis, neoplastic diseases and nonspecific pulmonary diseases that have no visible clinical symptomatology

Fault Diagnosis of Nonlinear Systems Using Structured Augmented State Models

Institute of Scientific and Technical Information of China (English)

Jochen Aβfalg; Frank Allg(o)wer

2007-01-01

This paper presents an internal model approach for modeling and diagnostic functionality design for nonlinear systems operating subject to single- and multiple-faults. We therefore provide the framework of structured augmented state models. Fault characteristics are considered to be generated by dynamical exosystems that are switched via equality constraints to overcome the augmented state observability limiting the number of diagnosable faults. Based on the proposed model, the fault diagnosis problem is specified as an optimal hybrid augmented state estimation problem. Sub-optimal solutions are motivated and exemplified for the fault diagnosis of the well-known three-tank benchmark. As the considered class of fault diagnosis problems is large, the suggested approach is not only of theoretical interest but also of high practical relevance.

EPICS: operating system independent device/driver support

International Nuclear Information System (INIS)

Kraimer, M.R.

2003-01-01

Originally EPICS input/output controllers (IOCs) were only supported on VME-based systems running the vxWorks operating system. Now IOCs are supported on many systems: vxWorks, RTEMS, Solaris, HPUX, Linux, WIN32, and Darwin. A challenge is to provide operating-system-independent device and driver support. This paper presents some techniques for providing such support. EPICS (Experimental Physics and Industrial Control System) is a set of software tools, libraries, and applications developed collaboratively and used worldwide to create distributed, real-time control systems for scientific instruments such as particle accelerators, telescopes, and other large scientific experiments. An important component of all EPICS-based control systems is a collection of input/output controllers (IOCs). An IOC has three primary components: (1) a real-time database; (2) channel access, which provides network access to the database; and (3) device/driver support for interfacing to equipment. This paper describes some projects related to providing device/driver support on non-vxWorks systems. In order to support IOCs on platforms other than vxWorks, operating-system-independent (OSI) application program interfaces (APIs) were defined for threads, semaphores, timers, etc. Providing support for a new platform consists of providing an operating-system-dependent implementation of the OSI APIs.

A study on quantification of the information flow and effectiveness of information aids for diagnosis tasks in nuclear power plants

International Nuclear Information System (INIS)

Kim, Jong Hyun

2004-02-01

Diagnosis is one of the most complex and mental resource-demanding tasks in nuclear power plants (NPPs), especially, to main control room (MCR) operators. Diagnosis is a crucial part of disturbance control in NPPs, since it is a prerequisite task for initiating operating procedures. In order to design a control room feature for NPPs, three elements need to be considered: 1) the operational tasks that must be performed, 2) a model of human performance for these tasks, and 3) a model of how control room features are intended to support performance. The operational tasks define the classes of performance that must be considered. A model of human performance makes more explicit the requirements for accurate and efficient performance and reveals potential sources of error. Finally, the model of support allows the generation of specific hypotheses about how performance is facilitated in the control room. The model of support needs to be developed based on the human performance model. This paper proposes three approaches for the system design of operator support systems to aid MCR operators' diagnosis tasks in NPPs, considering the above three elements. This paper presents 1) a quantitative approach to modeling the information flow of diagnosis tasks, 2) strategy-based evaluation of information aids for diagnosis tasks, and 3) quantitative evaluation of NPP decision support systems. As an analysis of diagnosis tasks, this paper presents a method to quantify the cognitive information flow of diagnosis tasks, integrating a stage model (a qualitative approach) with information theory (a quantitative approach). The method includes: 1) constructing the information flow model, which consists of four stages based on operating procedures of NPPs: and 2) quantifying the information flow using Conant's model, a kind of information theory. Then, three experiments were conducted to evaluate the effectiveness of the proposed approach to predicting human performances, especially in

An expert fault diagnosis system for vehicle air conditioning product development

NARCIS (Netherlands)

Tan, C.F.; Tee, B.T.; Khalil, S.N.; Chen, W.; Rauterberg, G.W.M.

2015-01-01

The paper describes the development of the vehicle air-conditioning fault diagnosis system in automotive industries with expert system shell. The main aim of the research is to diagnose the problem of new vehicle air-conditioning system development process and select the most suitable solution to

Virtual reality system for diagnosis and therapeutic planning of cerebral aneurysms.

Science.gov (United States)

Mo, Da-peng; Bao, Sheng-de; Li, Liang; Yi, Zhi-qiang; Zhang, Jia-yong; Zhang, Yang

2010-08-01

The virtual reality (VR) system can provide the neurosurgeon to intuitively interact with and manipulate the three dimensional (3-D) image similarly to manipulate a real object. It was seldom reported that the system was used in diagnosis and treatment of cerebral aneurysms. This study aimed to investigate the application of VR system in diagnosis and therapeutic planning of cerebral aneurysms. A total of 24 cases of cerebral aneurysms were enrolled in this study from 2006 to 2008, which diagnosed by 3-D digital subtraction angiography (3D-DSA) or VR-based computed tomography angiographies (CTA). The VR system and 3D-DSA system were used to observe and measure aneurysms and the adjacent vessels. The data of observation and measurements were compared between VR image and 3D-DSA image. All the patients underwent surgical plan and simulated neurosurgical procedures in the VR system. There were 28 aneurysms detected in VR system and 3D-DSA system. The VR system generated clear and vivid 3-D virtual images which clearly displayed the location and size of the aneurysms and their precise anatomical spatial relations to the parent arteries and skull. The location, size and shape of the aneurysms and their anatomical relationship with the adjacent vessels were similar between 3-D virtual image and 3D-DSA, but the spatial relationship between aneurysms and skull only been displayed by VR system. This VR system also could simulate simple surgical procedures and surgical environments. The VR system can provide a highly effective way to provide precise imaging details as same as 3D-DSA system and assist the diagnosis of cerebral aneurysms with virtual 3-D data based on CTA. It significantly enhances the chosen therapeutic strategy of cerebral aneurysms.

Context-sensitive autoassociative memories as expert systems in medical diagnosis

Directory of Open Access Journals (Sweden)

Olivera Fernando

2006-11-01

Full Text Available Abstract Background The complexity of our contemporary medical practice has impelled the development of different decision-support aids based on artificial intelligence and neural networks. Distributed associative memories are neural network models that fit perfectly well to the vision of cognition emerging from current neurosciences. Methods We present the context-dependent autoassociative memory model. The sets of diseases and symptoms are mapped onto a pair of basis of orthogonal vectors. A matrix memory stores the associations between the signs and symptoms, and their corresponding diseases. A minimal numerical example is presented to show how to instruct the memory and how the system works. In order to provide a quick appreciation of the validity of the model and its potential clinical relevance we implemented an application with real data. A memory was trained with published data of neonates with suspected late-onset sepsis in a neonatal intensive care unit (NICU. A set of personal clinical observations was used as a test set to evaluate the capacity of the model to discriminate between septic and non-septic neonates on the basis of clinical and laboratory findings. Results We show here that matrix memory models with associations modulated by context can perform automatic medical diagnosis. The sequential availability of new information over time makes the system progress in a narrowing process that reduces the range of diagnostic possibilities. At each step the system provides a probabilistic map of the different possible diagnoses to that moment. The system can incorporate the clinical experience, building in that way a representative database of historical data that captures geo-demographical differences between patient populations. The trained model succeeds in diagnosing late-onset sepsis within the test set of infants in the NICU: sensitivity 100%; specificity 80%; percentage of true positives 91%; percentage of true negatives 100

Context-sensitive autoassociative memories as expert systems in medical diagnosis

Science.gov (United States)

Pomi, Andrés; Olivera, Fernando

2006-01-01

Background The complexity of our contemporary medical practice has impelled the development of different decision-support aids based on artificial intelligence and neural networks. Distributed associative memories are neural network models that fit perfectly well to the vision of cognition emerging from current neurosciences. Methods We present the context-dependent autoassociative memory model. The sets of diseases and symptoms are mapped onto a pair of basis of orthogonal vectors. A matrix memory stores the associations between the signs and symptoms, and their corresponding diseases. A minimal numerical example is presented to show how to instruct the memory and how the system works. In order to provide a quick appreciation of the validity of the model and its potential clinical relevance we implemented an application with real data. A memory was trained with published data of neonates with suspected late-onset sepsis in a neonatal intensive care unit (NICU). A set of personal clinical observations was used as a test set to evaluate the capacity of the model to discriminate between septic and non-septic neonates on the basis of clinical and laboratory findings. Results We show here that matrix memory models with associations modulated by context can perform automatic medical diagnosis. The sequential availability of new information over time makes the system progress in a narrowing process that reduces the range of diagnostic possibilities. At each step the system provides a probabilistic map of the different possible diagnoses to that moment. The system can incorporate the clinical experience, building in that way a representative database of historical data that captures geo-demographical differences between patient populations. The trained model succeeds in diagnosing late-onset sepsis within the test set of infants in the NICU: sensitivity 100%; specificity 80%; percentage of true positives 91%; percentage of true negatives 100%; accuracy (true positives

System support software for TSTA

International Nuclear Information System (INIS)

Claborn, G.W.; Mann, L.W.; Nielson, C.W.

1987-01-01

The software at the Tritium Systems Test Assembly (TSTA) is logically broken into two parts, the system support software and the subsystem software. The purpose of the system support software is to isolate the subsystem software from the physical hardware. In this sense the system support software forms the kernel of the software at TSTA. The kernel software performs several functions. It gathers data from CAMAC modules and makes that data available for subsystem processes. It services requests to send commands to CAMAC modules. It provides a system of logging functions and provides for a system-wide global program state that allows highly structured interaction between subsystem processes. The kernel's most visible function is to provide the Man-Machine Interface (MMI). The MMI allows the operators a window into the physical hardware and subsystem process state. Finally the kernel provides a data archiving and compression function that allows archival data to be accessed and plotted. Such kernel software as developed and implemented at TSTA is described

A qualitative diagnosis method for a continuous process monitor system

International Nuclear Information System (INIS)

Lucas, B.; Evrard, J.M.; Lorre, J.P.

1993-01-01

SEXTANT, an expert system for the analysis of transients, was built initially to study physical transients in nuclear reactors. It combines several knowledge bases concerning measurements, models and qualitative behavior of the plant with a generate-and-test mechanism and a set of numerical models of the physical process. The integration of an improved diagnosis method using a mixed model in SEXTANT in order to take into account the existence and the reliability of only a few number of sensors, the knowledge on failure and the possibility of non anticipated failures, is presented. This diagnosis method is based on two complementary qualitative models of the process and a methodology to build these models from a system description. 8 figs., 17 refs

Design of a real-time fault diagnosis expert system for the EAST cryoplant

International Nuclear Information System (INIS)

Zhou Zhiwei; Zhuang Ming; Lu Xiaofei; Hu Liangbing; Xia Genhai

2012-01-01

Highlights: ► An expert system of real-time fault diagnosis for EAST cryoplant is designed. ► Knowledge base is built via fault tree analysis based on our fault experience. ► It can make up the deficiency of safety monitoring in cryogenic DCS. ► It can help operators to find the fault causes and give operation suggestion. ► It plays a role of operators training in certain degree. - Abstract: The EAST cryoplant consists of a 2 kW/4 K helium refrigerator and a helium distribution system. It is a complex process system which involves many process variables and cryogenic equipments. Each potential fault or abnormal event may influence stability and safety of the cryogenic system, thereby disturbing the fusion experiment. The cryogenic control system can monitor the process data and detect process alarms, but it is difficult to effectively diagnose the fault causes and provide operation suggestions to operators when anomalies occur. Therefore, a real-time fault diagnosis expert system is essential for a safe and steady operation of EAST cryogenic system. After a brief description of the EAST cryoplant and its control system, the structure design of the cryogenic fault diagnosis expert system is proposed. Based on the empirical knowledge, the fault diagnosis model is built adopting fault tree analysis method which considers the uncertainty. The knowledge base and the inference machine are presented in detail. A cross-platform integrated development environment Qt Creator and MySQL database have been used to develop the system. The proposed expert system has a fine graphic user interface for monitoring and operation. Preliminary test was conducted and the results found to be satisfactory.

The CDD system in computed tomographic diagnosis of diverticular disease

International Nuclear Information System (INIS)

Pustelnik, Daniel; Elsholtz, Fabian Henry Juergen; Hamm, Bernd; Niehues, Stefan Markus; Bojarski, Christian

2017-01-01

Purpose cation in computed tomographic diagnosis and briefly recapitulates its targeted advantages over preliminary systems. Primarily, application of the CDD in computed tomography diagnostics is described. Differences with respect to the categories of the older systems are pointed out on the level of each CDD type using imaging examples. The presented images are derived from our institute according to the S2k criteria. Literature was researched on PubMed. Results The CDD constitutes an improvement compared to older systems for categorizing the stages of diverticular disease. It provides more discriminatory power on the descriptive-morphological level and defines as well as differentiates more courses of the disease. Furthermore, the categories translate more directly into state-of-the-art decision-making concerning hospitalization and therapy. The CDD should be applied routinely in the computed tomographic diagnosis of diverticular disease. Typical imaging patterns are presented.

Fault Diagnosis and Fault-tolerant Control of Modular Multi-level Converter High-voltage DC System

DEFF Research Database (Denmark)

Liu, Hui; Ma, Ke; Wang, Chao

2016-01-01

of failures and lower the reliability of the MMC-HVDC system. Therefore, research on the fault diagnosis and fault-tolerant control of MMC-HVDC system is of great significance in order to enhance the reliability of the system. This paper provides a comprehensive review of fault diagnosis and fault handling...

Information diversification for intelligent diagnosis of nuclear plants

International Nuclear Information System (INIS)

Furukawa, Hiroshi; Kuchimura, Keiji; Kitamura, Masaharu; Washio, Takashi.

1995-01-01

A general framework for future development of intelligent operator support systems in nuclear plants is proposed in this paper. The central idea in the framework is the decision-making through consensus among multiple agents, each conducting diagnosis on the basis of mutually different, i.e. diverse, principle by focusing dissimilar symptoms obtained from the plant. The applicability and credibility of the operator support system are expected to be significantly improved by implementing the proposed scheme. The effectiveness of diversification in symptom description independently of the effect of reasoning methods was mainly evaluated in this paper. A prototype system was developed for the subtask of fault diagnosis by multiple neural networks emulating the diagnostic agents. The advantage of the proposed framework, together with the related technique of symptom diversification and consensus, was clearly demonstrated through numerical evaluations simulating anomalies in a pressurized water reactor. The obtained results validate, at least to same extent, the present claim of combining multiple and diverse perspectives for reliable decision-making in high-hazard artifacts. (author)

Measurement and diagnosis system for 1.2 MV repetitive pulsed power source

International Nuclear Information System (INIS)

Li Yawei; Deng Jianjun; Xie Min; Feng Zongming; Liu Yuntao; Ma Chenggang

2010-01-01

In order to analyze the discharge performance and improve the design of the power system, a set of measurement and diagnosis system for the 1.2 MV repetitive pulsed power source, which supplies the drive power for a high power microwave source, has been designed by studying the high-voltage, high-current testing technology, data acquisition, signal processing, fault diagnosis, virtual instruments and electromagnetic compatibility technology, etc. A resistive-capacitive divider and a Rogowski coil are adopted in measurement; ADLINK corporation's PXI chips are used in data acquisition; data transmission system, condition monitoring and data analysis are developed by LabVIEW. This system can realize on-line monitoring and data analysis for the repetitive pulsed power source. (authors)

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.

An Environment for Guideline-based Decision Support Systems for Outpatients Monitoring.

Science.gov (United States)

Zini, Elisa M; Lanzola, Giordano; Bossi, Paolo; Quaglini, Silvana

2017-08-11

We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic. We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2. The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient's conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients. Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients' needs, in our work the Decision

A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment.

Science.gov (United States)

Choi, Young Jun; Baek, Jung Hwan; Park, Hye Sun; Shim, Woo Hyun; Kim, Tae Yong; Shong, Young Kee; Lee, Jeong Hyun

2017-04-01

An initial clinical assessment is described of a new, commercially available, computer-aided diagnosis (CAD) system using artificial intelligence (AI) for thyroid ultrasound, and its performance is evaluated in the diagnosis of malignant thyroid nodules and categorization of nodule characteristics. Patients with thyroid nodules with decisive diagnosis, whether benign or malignant, were consecutively enrolled from November 2015 to February 2016. An experienced radiologist reviewed the ultrasound image characteristics of the thyroid nodules, while another radiologist assessed the same thyroid nodules using the CAD system, providing ultrasound characteristics and a diagnosis of whether nodules were benign or malignant. The diagnostic performance and agreement of US characteristics between the experienced radiologist and the CAD system were compared. In total, 102 thyroid nodules from 89 patients were included; 59 (57.8%) were benign and 43 (42.2%) were malignant. The CAD system showed a similar sensitivity as the experienced radiologist (90.7% vs. 88.4%, p > 0.99), but a lower specificity and a lower area under the receiver operating characteristic (AUROC) curve (specificity: 74.6% vs. 94.9%, p = 0.002; AUROC: 0.83 vs. 0.92, p = 0.021). Classifications of the ultrasound characteristics (composition, orientation, echogenicity, and spongiform) between radiologist and CAD system were in substantial agreement (κ = 0.659, 0.740, 0.733, and 0.658, respectively), while the margin showed a fair agreement (κ = 0.239). The sensitivity of the CAD system using AI for malignant thyroid nodules was as good as that of the experienced radiologist, while specificity and accuracy were lower than those of the experienced radiologist. The CAD system showed an acceptable agreement with the experienced radiologist for characterization of thyroid nodules.

Computerized accident management support system: development for severe accident management

International Nuclear Information System (INIS)

Garcia, V.; Saiz, J.; Gomez, C.

1998-01-01

The activities involved in the international Halden Reactor Project (HRP), sponsored by the OECD, include the development of a Computerized Accident Management Support System (CAMS). The system was initially designed for its operation under normal conditions, operational transients and non severe accidents. Its purpose is to detect the plant status, analyzing the future evolution of the sequence (initially using the APROS simulation code) and the possible recovery and mitigation actions in case of an accident occurs. In order to widen the scope of CAMS to severe accident management issues, the integration of the MAAP code in the system has been proposed, as the contribution of the Spanish Electrical Sector to the project (with the coordination of DTN). To include this new capacity in CAMS is necessary to modify the system structure, including two new modules (Diagnosis and Adjustment). These modules are being developed currently for Pressurized Water Reactors and Boiling Water REactors, by the engineering of UNION FENOSA and IBERDROLA companies (respectively). This motion presents the characteristics of the new structure of the CAMS, as well as the general characteristics of the modules, developed by these companies in the framework of the Halden Reactor Project. (Author)

A Game-Theoretic approach to Fault Diagnosis of Hybrid Systems

Directory of Open Access Journals (Sweden)

Davide Bresolin

2011-06-01

Full Text Available Physical systems can fail. For this reason the problem of identifying and reacting to faults has received a large attention in the control and computer science communities. In this paper we study the fault diagnosis problem for hybrid systems from a game-theoretical point of view. A hybrid system is a system mixing continuous and discrete behaviours that cannot be faithfully modeled neither by using a formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, and we define a Fault Diagnosis Game on them, using two players: the environment and the diagnoser. The environment controls the evolution of the system and chooses whether and when a fault occurs. The diagnoser observes the external behaviour of the system and announces whether a fault has occurred or not. Existence of a winning strategy for the diagnoser implies that faults can be detected correctly, while computing such a winning strategy corresponds to implement a diagnoser for the system. We will show how to determine the existence of a winning strategy, and how to compute it, for some decidable classes of hybrid automata like o-minimal hybrid automata.

Basic physiological systems indicator's informative assessment for children and adolescents obesity diagnosis tasks

Science.gov (United States)

Marukhina, O. V.; Berestneva, O. G.; Emelyanova, Yu A.; Romanchukov, S. V.; Petrova, L.; Lombardo, C.; Kozlova, N. V.

2018-05-01

The healthcare computerization creates opportunities to the clinical decision support system development. In the course of diagnosis, doctor manipulates a considerable amount of data and makes a decision in the context of uncertainty basing upon the first-hand experience and knowledge. The situation is exacerbated by the fact that the knowledge scope in medicine is incrementally growing, but the decision-making time does not increase. The amount of medical malpractice is growing and it leads to various negative effects, even the mortality rate increase. IT-solution's development for clinical purposes is one of the most promising and efficient ways to prevent these effects. That is why the efforts of many IT specialists are directed to the doctor's heuristics simulating software or expert-based medical decision-making algorithms development. Thus, the objective of this study is to develop techniques and approaches for the body physiological system's informative value assessment index for the obesity degree evaluation based on the diagnostic findings.

Expert System for Diagnosis of Hepatitis B Ibrahim Mailafiya, Fatima ...

African Journals Online (AJOL)

the rice plant appearing during their life span. [1]. ... use of intelligent systems such as fuzzy logic, artificial neural network and genetic algorithm have been developed [5]. ... The liver being the ..... doctors but to assist them in the quality ... P.Santosh Kumar Patra, An Expert System for Diagnosis of Human diseases, 2010.

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)

Large scale mining and evidence combination to support medical diagnosis

NARCIS (Netherlands)

Berrada, Ghita

2015-01-01

"Errare humanum est". Clinicians, however smart, caring and meticulous they may be, are only human, all too human. So they are bound to occasionally make mistakes, diagnosis errors (i.e delayed/missed or wrong diagnosis) in particular. With a prevalence of misdiagnosis of 15% in most areas of

Next-Generation Sequencing in Neuropathologic Diagnosis of Infections of the Nervous System (Open Access)

Science.gov (United States)

2016-06-13

nervous system ABSTRACT Objective: To determine the feasibility of next-generation sequencing (NGS) microbiome ap- proaches in the diagnosis of infectious...V, van Doorn HR, Nghia HD, et al. Identification of a new cyclovirus in cerebrospinal fluid of patients with acute central nervous system infections...Kumar, et al. system Next-generation sequencing in neuropathologic diagnosis of infections of the nervous This information is current as of June 13

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

Fiscal 1998 survey report. Medical equipment (Development of fine sampling/analysis system for blood / Development of high-precision 3-D image diagnosis system / Development of low-invasion operation support system / Total development of artificial internal organ technologies); 1998 nendo chosa hokokusho. Iryo kiki (ketsuekinado biryo saishu, biryo bunseki system kaihatsu/koseido sanjigen eizo shindan system kaihatsu/teishinshu shujutsu shien system kaihatsu/jinko zoki gijutsu sogo kaihatsu)

Energy Technology Data Exchange (ETDEWEB)

NONE

1999-03-01

For efficient medical care supply systems, the R and D of advanced medical care technology and equipment largely contribute to productivity improvement for medical care services. Among them, a progress of medical care technology is becoming important for preparation of efficient and fair supply systems. MITI thus established 'Medical care and welfare equipment development project' in 1994, and has promoted the strategic long-term R and D project of medical care and welfare equipment as joint R and D project of NEDO and private enterprises. In fiscal 1998, on the development of the fine sampling/analysis system for blood, the high- precision 3-D image diagnosis system, the low-invasion operation support system, and the artificial internal organ technologies since fiscal 1996, this project clarified essential technical issues based on the future view, selected some themes to be newly developed in the future, and surveyed and evaluated the details of their R and D concretely. (NEDO)

Nailfold Capillaroscopy - Its Role in Diagnosis and Differential Diagnosis of Microvascular Damage in Systemic Sclerosis.

Science.gov (United States)

Lambova, Sevdalina; Hermann, W; Muller-Ladner, Ulf

2013-01-01

In the nailfold area, specific diagnostic microvascular abnormalities are easily recognized via capillaroscopic examination in systemic sclerosis (SSc). They are termed "scleroderma" type capillaroscopic pattern, which includes presence of dilated, giant capillaries, haemorrhages, avascular areas, and neoangiogenic capillaries and are observed in the majority of SSc patients (in more than 90%). LeRoy and Medsger (2001) proposed criteria for early diagnosis of SSc with inclusion of the abnormal capillaroscopic changes and suggested to prediagnose SSc prior to the development of other manifestations of the disease. It is a new era in the diagnosis of SSc. At present, an international multicenter project is performed. It aims validation of criteria for very early diagnosis of SSc (project VEDOSS (Very Early Diagnosis of Systemic Sclerosis) and is organized by European League Against Rheumatism (EULAR) Scleroderma Trials and Reasearch. Very recently the first results of the VEDOSS project were processed and new EULAR/ACR (American College of Rheumatology) classification criteria have been validated and published (2013), in which the characteristic capillaroscopic changes have been included. Our observations confirm the high frequency of the specific capillaroscopic changes of the fingers in SSc, which have been found in 97.2% of the cases from the studied patient population. We have performed for the first time capillaroscopic examinations of the toes in SSc. Interestingly,"scleroderma type" capillaroscopic pattern was also found at the toes in a high proportion of patients - 66.7%, but it is significantly less frequent as compared with fingers (97.2%, p<0.05). In our opinion, the examination of the toes of SSc patients should be considered as it suggests an additional opportunity for evaluation of the microvascular changes in these patients although the observed changes are in a lower proportion of cases. Thus, capillaroscopic examination is a cornerstone for the very

Using Supervised Learning Techniques for Diagnosis of Dynamic Systems

Science.gov (United States)

2002-05-04

diagnosis task is to determine the system elements that could cause decision trees [14], where classification is the result of a series of the erroneous...Rodriguez, Carlos J. Alonso y Q. Isaac Moro. Clasificaci6n de patrones temporales en sistemas dinimicos mediante Boosting y Alineamiento dinamico

Education and training support system

International Nuclear Information System (INIS)

Kubota, Rhuji; Iyadomi, Motomi.

1996-01-01

In order to train the specialist such as operator or maintenance stuff of large scale plant such as nuclear power plant or thermal power plant, a high grade teaching and training support system is required as well as in training pilot of aeroplane. The specialist in such large scale plant is also a researcher in the field of machinery, electricity and physics at first, and is grown up a expert operator or maintenance stuff through learning of CAI system or OTJ used training material for teaching tool in addition of training used operating or maintenance training device imitating actual plant after acquiring determined knowledges by receiving fundamental education on nuclear and thermal power plants. In this paper, the teaching and training support systems of the nuclear and thermal power plants for a system supporting such teaching and training, respectively, were introduced. (G.K.)

A Computuerized Operator Support System Prototype

Energy Technology Data Exchange (ETDEWEB)

Thomas, Ken [Idaho National Lab. (INL), Idaho Falls, ID (United States); Boring, Ronald [Idaho National Lab. (INL), Idaho Falls, ID (United States); Lew, Roger [Idaho National Lab. (INL), Idaho Falls, ID (United States); Ulrich, Tom [Idaho National Lab. (INL), Idaho Falls, ID (United States); Villim, Richard [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2013-11-01

A report was published by the Idaho National Laboratory in September of 2012, entitled Design to Achieve Fault Tolerance and Resilience, which described the benefits of automating operator actions for transients. The report identified situations in which providing additional automation in lieu of operator actions would be advantageous. It recognized that managing certain plant upsets is sometimes limited by the operator’s ability to quickly diagnose the fault and to take the needed actions in the time available. Undoubtedly, technology is underutilized in the nuclear power industry for operator assistance during plant faults and operating transients. In contrast, other industry sectors have amply demonstrated that various forms of operator advisory systems can enhance operator performance while maintaining the role and responsibility of the operator as the independent and ultimate decision-maker. A computerized operator support system (COSS) is proposed for use in nuclear power plants to assist control room operators in addressing time-critical plant upsets. A COSS is a collection of technologies to assist operators in monitoring overall plant performance and making timely, informed decisions on appropriate control actions for the projected plant condition. The COSS does not supplant the role of the operator, but rather provides rapid assessments, computations, and recommendations to reduce workload and augment operator judgment and decision-making during fast-moving, complex events. This project proposes a general model for a control room COSS that addresses a sequence of general tasks required to manage any plant upset: detection, validation, diagnosis, recommendation, monitoring, and recovery. The model serves as a framework for assembling a set of technologies that can be interrelated to assist with each of these tasks. A prototype COSS has been developed in order to demonstrate the concept and provide a test bed for further research. The prototype is based

A Computuerized Operator Support System Prototype

Energy Technology Data Exchange (ETDEWEB)

Thomas, Ken [Idaho National Lab. (INL), Idaho Falls, ID (United States); Boring, Ronald [Idaho National Lab. (INL), Idaho Falls, ID (United States); Lew, Roger [Idaho National Lab. (INL), Idaho Falls, ID (United States); Ulrich, Tom [Idaho National Lab. (INL), Idaho Falls, ID (United States); Villim, Richard [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2013-08-01

A report was published by the Idaho National Laboratory in September of 2012, entitled Design to Achieve Fault Tolerance and Resilience, which described the benefits of automating operator actions for transients. The report identified situations in which providing additional automation in lieu of operator actions would be advantageous. It recognized that managing certain plant upsets is sometimes limited by the operator’s ability to quickly diagnose the fault and to take the needed actions in the time available. Undoubtedly, technology is underutilized in the nuclear power industry for operator assistance during plant faults and operating transients. In contrast, other industry sectors have amply demonstrated that various forms of operator advisory systems can enhance operator performance while maintaining the role and responsibility of the operator as the independent and ultimate decision-maker. A computerized operator support system (COSS) is proposed for use in nuclear power plants to assist control room operators in addressing time-critical plant upsets. A COSS is a collection of technologies to assist operators in monitoring overall plant performance and making timely, informed decisions on appropriate control actions for the projected plant condition. The COSS does not supplant the role of the operator, but rather provides rapid assessments, computations, and recommendations to reduce workload and augment operator judgment and decision-making during fast-moving, complex events. This project proposes a general model for a control room COSS that addresses a sequence of general tasks required to manage any plant upset: detection, validation, diagnosis, recommendation, monitoring, and recovery. The model serves as a framework for assembling a set of technologies that can be interrelated to assist with each of these tasks. A prototype COSS has been developed in order to demonstrate the concept and provide a test bed for further research. The prototype is based

A Distributed and Collaborative Intelligent System for Medical Diagnosis

Directory of Open Access Journals (Sweden)

Wided LEJOUAD-CHAARI

2013-08-01

Full Text Available In this paper, we present a distributed collaborative system assisting physicians in diagnosis when processing medical images. This is a Web-based solution since the different participants and resources are on various sites. It is collaborative because these participants (physicians, radiologists, knowledgebasesdesigners, program developers for medical image processing, etc. can work collaboratively to enhance the quality of programs and then the quality of the diagnosis results. It is intelligent since it is a knowledge-based system including, but not only, a knowledge base, an inference engine said supervision engine and ontologies. The current work deals with the osteoporosis detection in bone radiographies. We rely on program supervision techniques that aim to automatically plan and control complex software usage. Our main contribution is to allow physicians, who are not experts in computing, to benefit from technological advances made by experts in image processing, and then to efficiently use various osteoporosis detection programs in a distributed environment.

Faults and Diagnosis Systems in Power Converters

DEFF Research Database (Denmark)

Lee, Kyo-Beum; Choi, Uimin

2014-01-01

A power converter is needed in almost all kinds of renewable energy systems and drive systems. It is used both for controlling the renewable source and for interfacing with the load, which can be grid-connected or working in standalone mode. Further, it drives the motors efficiently. Increasing...... efforts have been put into making these systems better in terms of reliability in order to achieve high power source availability, reduce the cost of energy and also increase the reliability of overall systems. Among the components used in power converters, a power device and a capacitor fault occurs most...... frequently. Therefore, it is important to monitor the power device and capacitor fault to increase the reliability of power electronics. In this chapter, the diagnosis methods for power device fault will be discussed by dividing into open- and short-circuit faults. Then, the condition monitoring methods...

An intelligent interlock design support system

International Nuclear Information System (INIS)

Hayashi, Toshifumi; Kamiyama, Masahiko

1990-01-01

This paper presents an intelligent interlock design support system, called Handy. BWR plant interlocks have been designed on a conventional CAD system operating on a mini-computer based time sharing system. However, its ability to support interlock designers is limited, mainly due to the system not being capable of manipulating the interlock logic. Handy improves the design efficiency with consistent manipulation of the logic and drawings, interlock simulation, versatile database management, object oriented user interface, high resolution high speed graphics, and automatic interlock outlining with a design support expert system. Handy is now being tested by designers, and is expected to greatly contribute to their efficiency. (author)

Design of a real-time fault diagnosis expert system for the EAST cryoplant

Energy Technology Data Exchange (ETDEWEB)

Zhou Zhiwei, E-mail: zzw@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Zhuang Ming, E-mail: zhm@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Lu Xiaofei, E-mail: luxf1212@mail.ustc.edu.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Hu Liangbing, E-mail: huliangbing@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Xia Genhai, E-mail: xgh@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China)

2012-12-15

Highlights: Black-Right-Pointing-Pointer An expert system of real-time fault diagnosis for EAST cryoplant is designed. Black-Right-Pointing-Pointer Knowledge base is built via fault tree analysis based on our fault experience. Black-Right-Pointing-Pointer It can make up the deficiency of safety monitoring in cryogenic DCS. Black-Right-Pointing-Pointer It can help operators to find the fault causes and give operation suggestion. Black-Right-Pointing-Pointer It plays a role of operators training in certain degree. - Abstract: The EAST cryoplant consists of a 2 kW/4 K helium refrigerator and a helium distribution system. It is a complex process system which involves many process variables and cryogenic equipments. Each potential fault or abnormal event may influence stability and safety of the cryogenic system, thereby disturbing the fusion experiment. The cryogenic control system can monitor the process data and detect process alarms, but it is difficult to effectively diagnose the fault causes and provide operation suggestions to operators when anomalies occur. Therefore, a real-time fault diagnosis expert system is essential for a safe and steady operation of EAST cryogenic system. After a brief description of the EAST cryoplant and its control system, the structure design of the cryogenic fault diagnosis expert system is proposed. Based on the empirical knowledge, the fault diagnosis model is built adopting fault tree analysis method which considers the uncertainty. The knowledge base and the inference machine are presented in detail. A cross-platform integrated development environment Qt Creator and MySQL database have been used to develop the system. The proposed expert system has a fine graphic user interface for monitoring and operation. Preliminary test was conducted and the results found to be satisfactory.

Dynamism in Electronic Performance Support Systems.

Science.gov (United States)

Laffey, James

1995-01-01

Describes a model for dynamic electronic performance support systems based on NNAble, a system developed by the training group at Apple Computer. Principles for designing dynamic performance support are discussed, including a systems approach, performer-centered design, awareness of situated cognition, organizational memory, and technology use.…

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.

Computer-Supported Information Systems.

Science.gov (United States)

Mayhew, William H.

1983-01-01

The planning and implementation of a computerized management information system at a fictional small college is described. Nine key points are made regarding department involvement, centralization, gradual program implementation, lowering costs, system documentation, and upper-level administrative support. (MSE)

Further substantial improvement of interventional diagnosis and treatment via portal vein system

International Nuclear Information System (INIS)

Yang Weizhu; Chen Yongde

2006-01-01

Along with the development of interventional appliances and proficiency of operational skills, the interventional diagnosis and treatment via hepatic portal vein system have achieved great progress and improvement. However, in order to further exploit the advantages of interventional diagnosis and treatment, the review of the anatomical structures, normal aberrance of portal venous system were needed. Getting familiar with pathologic condition to discover the new interventional appliances and embolic agents, and then in term of conduct the research on a very tough substantial base in a down-to-earth manner were important. (authors)

System for technical innovation support

International Nuclear Information System (INIS)

2011-08-01

This book lists field of support system, which includes tax, development work, basic research project, industrial technology, information and communications field, energy field, part and materials field, local industry, the small and medium business such as technical development field, and industry-university collaboration like summary of investment and financing support and guarantee, support of manpower such as brain pool and contact Korea, support of technique like development technology and strategy for patent, support on certification such as company and technical goods, purchase support.

Handling Diagnosis of Schizophrenia by a Hybrid Method

Directory of Open Access Journals (Sweden)

Luciano Comin Nunes

2015-01-01

Full Text Available Psychotics disorders, most commonly known as schizophrenia, have incapacitated professionals in different sectors of activities. Those disorders have caused damage in a microlevel to the individual and his/her family and in a macrolevel to the economic and production system of the country. The lack of early and sometimes very late diagnosis has provided reactive measures, when the professional is already showing psychological signs of incapacity to work. This study aims to help the early diagnosis of psychotics’ disorders with a hybrid proposal of an expert system that is integrated to structured methodologies in decision support (multicriteria decision analysis: MCDA and knowledge structured representations into production rules and probabilities (artificial intelligence: AI.

Life Support Systems: Environmental Monitoring

Data.gov (United States)

National Aeronautics and Space Administration — The Advanced Exploration Systems (AES) Life Support Systems project Environmental Monitoring (EM) systems task objectives are to develop and demonstrate onboard...

A Browser-Server-Based Tele-audiology System That Supports Multiple Hearing Test Modalities.

Science.gov (United States)

Yao, Jianchu Jason; Yao, Daoyuan; Givens, Gregg

2015-09-01

Millions of global citizens suffering from hearing disorders have limited or no access to much needed hearing healthcare. Although tele-audiology presents a solution to alleviate this problem, existing remote hearing diagnosis systems support only pure-tone tests, leaving speech and other test procedures unsolved, due to the lack of software and hardware to enable communication required between audiologists and their remote patients. This article presents a comprehensive remote hearing test system that integrates the two most needed hearing test procedures: a pure-tone audiogram and a speech test. This enhanced system is composed of a Web application server, an embedded smart Internet-Bluetooth(®) (Bluetooth SIG, Kirkland, WA) gateway (or console device), and a Bluetooth-enabled audiometer. Several graphical user interfaces and a relational database are hosted on the application server. The console device has been designed to support the tests and auxiliary communication between the local site and the remote site. The study was conducted at an audiology laboratory. Pure-tone audiogram and speech test results from volunteers tested with this tele-audiology system are comparable with results from the traditional face-to-face approach. This browser-server-based comprehensive tele-audiology offers a flexible platform to expand hearing services to traditionally underserved groups.

Novel electro-hydraulic position control system for primary mirror supporting system

Directory of Open Access Journals (Sweden)

Xiongbin Peng

2016-05-01

Full Text Available In the field of modern large-scale telescope, primary mirror supporting system technology faces the difficulties of theoretically uniform output force request and bias compensation. Therefore, a novel position control system combining hydraulic system with servo motor system is introduced. The novel system ensures uniform output force on supporting points without complicating the mechanical structure. The structures of both primary mirror supporting system and novel position system are described. Then, the mathematical model of novel position control system is derived for controller selection. A proportional–derivative controller is adopted for simulations and experiments of step response and triangle path tracking. The results show that proportional–derivative controller guarantees the system with micrometer-level positioning ability. A modified proportional–derivative controller is utilized to promote system behavior with faster response overshoot. The novel position control system is then applied on primary mirror supporting system. Coupling effect is observed among actuator partitions, and relocation of virtual pivot supporting point is chosen as the decoupling measurement. The position keeping ability of the primary mirror supporting system is verified by rotating the mirror cell at a considerably high rate. The experiment results show that the decoupled system performs better with smaller bias and shorter recovery time.

Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm

Directory of Open Access Journals (Sweden)

Yuichi Sakumura

2017-02-01

Full Text Available Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs at very low concentrations (ppb level. We analyzed the breath exhaled by lung cancer patients and healthy subjects (controls using gas chromatography/mass spectrometry (GC/MS, and performed a subsequent statistical analysis to diagnose lung cancer based on the combination of multiple lung cancer-related VOCs. We detected 68 VOCs as marker species using GC/MS analysis. We reduced the number of VOCs and used support vector machine (SVM algorithm to classify the samples. We observed that a combination of five VOCs (CHN, methanol, CH3CN, isoprene, 1-propanol is sufficient for 89.0% screening accuracy, and hence, it can be used for the design and development of a desktop GC-sensor analysis system for lung cancer.

Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm.

Science.gov (United States)

Sakumura, Yuichi; Koyama, Yutaro; Tokutake, Hiroaki; Hida, Toyoaki; Sato, Kazuo; Itoh, Toshio; Akamatsu, Takafumi; Shin, Woosuck

2017-02-04

Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations (ppb level). We analyzed the breath exhaled by lung cancer patients and healthy subjects (controls) using gas chromatography/mass spectrometry (GC/MS), and performed a subsequent statistical analysis to diagnose lung cancer based on the combination of multiple lung cancer-related VOCs. We detected 68 VOCs as marker species using GC/MS analysis. We reduced the number of VOCs and used support vector machine (SVM) algorithm to classify the samples. We observed that a combination of five VOCs (CHN, methanol, CH₃CN, isoprene, 1-propanol) is sufficient for 89.0% screening accuracy, and hence, it can be used for the design and development of a desktop GC-sensor analysis system for lung cancer.

ELECTRICAL SUPPORT SYSTEM DESCRIPTION DOCUMENT

International Nuclear Information System (INIS)

Roy, S.

2004-01-01

The purpose of this revision of the System Design Description (SDD) is to establish requirements that drive the design of the electrical support system and their bases to allow the design effort to proceed to License Application. This SDD is a living document that will be revised at strategic points as the design matures over time. This SDD identifies the requirements and describes the system design as they exist at this time, with emphasis on those attributes of the design provided to meet the requirements. This SDD has been developed to be an engineering tool for design control. Accordingly, the primary audience/users are design engineers. This type of SDD both ''leads'' and ''trails'' the design process. It leads the design process with regard to the flow down of upper tier requirements onto the system. Knowledge of these requirements is essential in performing the design process. The SDD trails the design with regard to the description of the system. The description provided in the SDD is a reflection of the results of the design process to date. Functional and operational requirements applicable to electrical support systems are obtained from the ''Project Functional and Operational Requirements'' (F andOR) (Siddoway 2003). Other requirements to support the design process have been taken from higher-level requirements documents such as the ''Project Design Criteria Document'' (PDC) (Doraswamy 2004), and fire hazards analyses. The above-mentioned low-level documents address ''Project Requirements Document'' (PRD) (Canon and Leitner 2003) requirements. This SDD contains several appendices that include supporting information. Appendix B lists key system charts, diagrams, drawings, and lists, and Appendix C includes a list of system procedures

Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems

Energy Technology Data Exchange (ETDEWEB)

Tassou, S.A.; Grace, I.N. [Brunel University, Uxbridge (United Kingdom). Department of Mechanical Engineering

2005-08-01

The environmental impact of refrigeration systems can be reduced by operation at higher efficiency and reduction of refrigerant leakage. Refrigerant loss contributes both directly and indirectly to global warming through inefficient system operation, increased power consumption and greenhouse gas emissions and higher maintenance costs. Existing sensor-based leak detection methods are limited by the inability to detect gradual leakage and the need for careful sensor location. There is a requirement for a real-time performance monitoring approach to leak detection and fault diagnosis which overcomes these disadvantages. This paper reports on the development of a fault diagnosis and refrigerant leak detection system based on artificial intelligence and real-time performance monitoring. The system has been used successfully to distinguish between faulty and fault free operation, steady-state and transient operation, leakage and over charge conditions. Work currently underway is aimed at testing additional fault conditions and establishing further rules to distinguish between these patterns. (author)

Corrective maintenance support system for nuclear power plants

International Nuclear Information System (INIS)

Kakiuchi, Tetsuo

1996-01-01

With increase of share of nuclear power generation in electric power supply in Japan, requirement for further safe operation and improvement of economics for the nuclear power plants is promoting. The pressed water type (PWR) nuclear power plant in operation in Japan reaches to 22 sets, application rate of the instruments is 74% as mean value for 7 years since 1989 and in high level, which is due to a result of preventive maintenance in ordinary and periodical inspections. The present state of maintenance at the nuclear power plant is mainly preventive maintenance, which is mainly conducted in a shape of time planning maintenance but partially in a shape of state monitoring maintenance for partial rotating appliances. Concretely speaking, the periodical inspection was planned on a base of daily inspection and a long term program on maintenance, and executed on a base of feedback function to think of the long term program again by evaluating the periodical inspection results. Here were introduced on the monitoring diagnosis and periodical inspection regionalization equipment, fatigue monitoring system, automatic supersonic wave damage inspection equipment for reactor, steam evaporator heat conductive tube inspection equipment, automatic testing equipment for measuring controller, air working valve property testing equipment, as maintaining support system in the PW generation plant. (G.K.)

Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine

Energy Technology Data Exchange (ETDEWEB)

Xing, Zhanqiang; Qu, Jianfeng; Chai, Yi; Tang, Qiu; Zhou, Yuming [Chongqing University, Chongqing (China)

2017-02-15

The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.

Initiative For Thyroid Cancer Diagnosis: Decision Support System For Anaplast Thyroid Cancer

Directory of Open Access Journals (Sweden)

Jamil Ahmed Chandio

2017-12-01

Full Text Available Due to the high level exposure of biomedical image analysis, Medical image mining has become one of the well-established research area(s of machine learning. AI (Artificial Intelligence techniques have been vastly used to solve the complex classification problems of thyroid cancer. Since the persistence of copycat chromatin properties and unavailability of nuclei measurement techniques, it is really problem for doctors to determine the initial phases of nuclei enlargement and to assess the early changes of chromatin distribution. For example involvement of multiple transparent overlapping of nuclei may become the cause of confusion to infer the growth pattern of nuclei variations. Un-decidable nuclei eccentric properties may become one of the leading causes for misdiagnosis in Anaplast cancers. In-order to mitigate all above stated problems this paper proposes a novel methodology so called “Decision Support System for Anaplast Thyroid Cancer” and it proposes a medical data preparation algorithm AD (Analpast_Cancers which helps to select the appropriate features of Anaplast cancers such as (1 enlargement of nuclei, (2 persistence of irregularity in nuclei and existence of hyper chromatin. Proposed methodology comprises over four major layers, first layer deals with the noise reduction, detection of nuclei edges and object clusters. Second layer selects the features of object of interest such as nuclei enlargement, irregularity and hyper chromatin. Third layer constructs the decision model to extract the hidden patterns of disease associated variables and final layer evaluates the performance evaluation by using confusion matrix, precision and recall measures. The overall classification accuracy is measured about 97.2% with 10-k fold cross validation.

Improving clinical decision support using data mining techniques

Science.gov (United States)

Burn-Thornton, Kath E.; Thorpe, Simon I.

1999-02-01

Physicians, in their ever-demanding jobs, are looking to decision support systems for aid in clinical diagnosis. However, clinical decision support systems need to be of sufficiently high accuracy that they help, rather than hinder, the physician in his/her diagnosis. Decision support systems with accuracies, of patient state determination, of greater than 80 percent, are generally perceived to be sufficiently accurate to fulfill the role of helping the physician. We have previously shown that data mining techniques have the potential to provide the underpinning technology for clinical decision support systems. In this paper, an extension of the work in reverence 2, we describe how changes in data mining methodologies, for the analysis of 12-lead ECG data, improve the accuracy by which data mining algorithms determine which patients are suffering from heart disease. We show that the accuracy of patient state prediction, for all the algorithms, which we investigated, can be increased by up to 6 percent, using the combination of appropriate test training ratios and 5-fold cross-validation. The use of cross-validation greater than 5-fold, appears to reduce the improvement in algorithm classification accuracy gained by the use of this validation method. The accuracy of 84 percent in patient state predictions, obtained using the algorithm OCI, suggests that this algorithm will be capable of providing the required accuracy for clinical decision support systems.

Integrated Control System Engineering Support.

Science.gov (United States)

1984-12-01

Advanced Medium Range Air to Air Missile ASTEC Advanced Speech Technology Experimental Configuration BA Body Axis BCIU Bus Control Interface Unit BMU Bus...support nreeded to tie an ASTEC speech recognition system into the DIGISYN fJcility and support an FIGR experiment designed to investigate the voice...information passed to the PDP computer consisted of integers which represented words or phrases recognized by the ASTEC recognition system. An interface

Expert Systems: Implications for the Diagnosis and Treatment of Learning Disabilities.

Science.gov (United States)

Hofmeister, Alan M.; Lubke, Margaret M.

1986-01-01

Expert systems are briefly reviewed and applications in special education diagnosis and classification are described. Future applications are noted to include text interpretation and pupil performance monitoring. (CL)

An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system

International Nuclear Information System (INIS)

Shao, Meng; Zhu, Xin-Jian; Cao, Hong-Fei; Shen, Hai-Feng

2014-01-01

The commercial viability of PEMFC (proton exchange membrane fuel cell) systems depends on using effective fault diagnosis technologies in PEMFC systems. However, many researchers have experimentally studied PEMFC (proton exchange membrane fuel cell) systems without considering certain fault conditions. In this paper, an ANN (artificial neural network) ensemble method is presented that improves the stability and reliability of the PEMFC systems. In the first part, a transient model giving it flexibility in application to some exceptional conditions is built. The PEMFC dynamic model is built and simulated using MATLAB. In the second, using this model and experiments, the mechanisms of four different faults in PEMFC systems are analyzed in detail. Third, the ANN ensemble for the fault diagnosis is built and modeled. This model is trained and tested by the data. The test result shows that, compared with the previous method for fault diagnosis of PEMFC systems, the proposed fault diagnosis method has higher diagnostic rate and generalization ability. Moreover, the partial structure of this method can be altered easily, along with the change of the PEMFC systems. In general, this method for diagnosis of PEMFC has value for certain applications. - Highlights: • We analyze the principles and mechanisms of the four faults in PEMFC (proton exchange membrane fuel cell) system. • We design and model an ANN (artificial neural network) ensemble method for the fault diagnosis of PEMFC system. • This method has high diagnostic rate and strong generalization ability

Development of expert system on personal computer for diagnosis of nuclear reactor malfunctions

International Nuclear Information System (INIS)

Kameyama, Takanori; Uekata, Tomomichi; Oka, Yoshiaki; Kondo, Shunsuke; Togo, Yasumasa

1988-01-01

An expert system on a personal computer has been developed for diagnosis of malfunction of the fast experimental reactor 'JOYO'. Prolog-KABA is used as the language. The system diagnoses the event which causes scram or set-back of the control rod after an alarm at steady state operation. The knowledge base (KB) consists of several sub-KBs and a meta-KB. Using the forward chaining, the meta-KB decides which sub-KB should be accessed. The cause of the malfunction is identified in the sub-KB using the backward chaining. The terms expressing the characteristics of the events are involved in the production rules as attributes in order to use the Prolog function of pattern matching and back-tracking for efficient inference. The total number of the rules in the system is about 400. The experiments using the plant simulator of 'JOYO' have shown that malfunctions are successfully identified by the diagnosis system. It takes about 10s for each diagnosis using the 16-bits personal computer, PC-9801 VM. (author)

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.

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

Science.gov (United States)

Knox, W. Bradley; Mengshoel, Ole

2009-01-01

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

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.

[Development of image quality assurance support system using image recognition technology in radiography in lacked images of chest and abdomen].

Science.gov (United States)

Shibuya, Toru; Kato, Kyouichi; Eshima, Hidekazu; Sumi, Shinichirou; Kubo, Tadashi; Ishida, Hideki; Nakazawa, Yasuo

2012-01-01

In order to provide a precise radiography for diagnosis, it is required that we avoid radiography with defects by having enough evaluation. Conventionally, evaluation was performed only by observation of a radiological technologist (RT). The evaluation support system was developed for providing a high quality assurance without depending on RT observation only. The evaluation support system, called as the Image Quality Assurance Support System (IQASS), is characterized in that "image recognition technology" for the purpose of diagnostic radiography of chest and abdomen areas. The technique of the system used in this study. Of the 259 samples of posterior-anterior (AP) chest, lateral chest, and upright abdominal x-rays, the sensitivity and specificity was 93.1% and 91.8% in the chest AP, 93.3% and 93.6% in the chest lateral, and 95.0% and 93.8% in the upright abdominal x-rays. In the light of these results, it is suggested that AIQAS could be applied to practical usage for the RT.

Preparing for a decision support system.

Science.gov (United States)

Callan, K

2000-08-01

The increasing pressure to reduce costs and improve outcomes is driving the health care industry to view information as a competitive advantage. Timely information is required to help reduce inefficiencies and improve patient care. Numerous disparate operational or transactional information systems with inconsistent and often conflicting data are no longer adequate to meet the information needs of integrated care delivery systems and networks in competitive managed care environments. This article reviews decision support system characteristics and describes a process to assess the preparedness of an organization to implement and use decision support systems to achieve a more effective, information-based decision process. Decision support tools included in this article range from reports to data mining.

A customized head and neck support system

International Nuclear Information System (INIS)

Bentel, Gunilla C.; Marks, Lawrence B.; Sherouse, George W.; Spencer, David P.

1995-01-01

Purpose: To describe a customized head and neck immobilization system for patients receiving radiotherapy including a head support that conforms to the posterior contour of the head and neck. Methods: The system includes a customized headrest to support the posterior head and neck. This is fixed to a thermoplastic face mask that molds to the anterior head/face contours. The shape of these customized head and neck supports were compared to 'standard' supports. Results: This system is comfortable for the patients and appears to be effective in reproducing the setup of the treatment. Conclusions: The variability in the size and shape of the customized posterior supports exceeded that of 'standard' headrests. It is our clinical impression that the customized supports improve reproducibility and are now a standard part of our immobilization system. The quantitative analysis of the customized headrests and some commonly used 'standard' headrests suggests that the customized supports are better able to address variabilities in patient shape

Timely diagnosis of dairy calf respiratory disease using a standardized scoring system.

Science.gov (United States)

McGuirk, Sheila M; Peek, Simon F

2014-12-01

Respiratory disease of young dairy calves is a significant cause of morbidity, mortality, economic loss, and animal welfare concern but there is no gold standard diagnostic test for antemortem diagnosis. Clinical signs typically used to make a diagnosis of respiratory disease of calves are fever, cough, ocular or nasal discharge, abnormal breathing, and auscultation of abnormal lung sounds. Unfortunately, routine screening of calves for respiratory disease on the farm is rarely performed and until more comprehensive, practical and affordable respiratory disease-screening tools such as accelerometers, pedometers, appetite monitors, feed consumption detection systems, remote temperature recording devices, radiant heat detectors, electronic stethoscopes, and thoracic ultrasound are validated, timely diagnosis of respiratory disease can be facilitated using a standardized scoring system. We have developed a scoring system that attributes severity scores to each of four clinical parameters; rectal temperature, cough, nasal discharge, ocular discharge or ear position. A total respiratory score of five points or higher (provided that at least two abnormal parameters are observed) can be used to distinguish affected from unaffected calves. This can be applied as a screening tool twice-weekly to identify pre-weaned calves with respiratory disease thereby facilitating early detection. Coupled with effective treatment protocols, this scoring system will reduce post-weaning pneumonia, chronic pneumonia, and otitis media.

Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

Directory of Open Access Journals (Sweden)

Utku Kose

2016-03-01

Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.

A Negative Selection Immune System Inspired Methodology for Fault Diagnosis of Wind Turbines.

Science.gov (United States)

Alizadeh, Esmaeil; Meskin, Nader; Khorasani, Khashayar

2017-11-01

High operational and maintenance costs represent as major economic constraints in the wind turbine (WT) industry. These concerns have made investigation into fault diagnosis of WT systems an extremely important and active area of research. In this paper, an immune system (IS) inspired methodology for performing fault detection and isolation (FDI) of a WT system is proposed and developed. The proposed scheme is based on a self nonself discrimination paradigm of a biological IS. Specifically, the negative selection mechanism [negative selection algorithm (NSA)] of the human body is utilized. In this paper, a hierarchical bank of NSAs are designed to detect and isolate both individual as well as simultaneously occurring faults common to the WTs. A smoothing moving window filter is then utilized to further improve the reliability and performance of the FDI scheme. Moreover, the performance of our proposed scheme is compared with another state-of-the-art data-driven technique, namely the support vector machines (SVMs) to demonstrate and illustrate the superiority and advantages of our proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate our proposed methodology with that of the SVM under various fault severities.

ELECTRICAL SUPPORT SYSTEM DESCRIPTION DOCUMENT

Energy Technology Data Exchange (ETDEWEB)

S. Roy

2004-06-24

The purpose of this revision of the System Design Description (SDD) is to establish requirements that drive the design of the electrical support system and their bases to allow the design effort to proceed to License Application. This SDD is a living document that will be revised at strategic points as the design matures over time. This SDD identifies the requirements and describes the system design as they exist at this time, with emphasis on those attributes of the design provided to meet the requirements. This SDD has been developed to be an engineering tool for design control. Accordingly, the primary audience/users are design engineers. This type of SDD both ''leads'' and ''trails'' the design process. It leads the design process with regard to the flow down of upper tier requirements onto the system. Knowledge of these requirements is essential in performing the design process. The SDD trails the design with regard to the description of the system. The description provided in the SDD is a reflection of the results of the design process to date. Functional and operational requirements applicable to electrical support systems are obtained from the ''Project Functional and Operational Requirements'' (F&OR) (Siddoway 2003). Other requirements to support the design process have been taken from higher-level requirements documents such as the ''Project Design Criteria Document'' (PDC) (Doraswamy 2004), and fire hazards analyses. The above-mentioned low-level documents address ''Project Requirements Document'' (PRD) (Canon and Leitner 2003) requirements. This SDD contains several appendices that include supporting information. Appendix B lists key system charts, diagrams, drawings, and lists, and Appendix C includes a list of system procedures.

Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification

Science.gov (United States)

Li, Shuanghong; Cao, Hongliang; Yang, Yupu

2018-02-01

Fault diagnosis is a key process for the reliability and safety of solid oxide fuel cell (SOFC) systems. However, it is difficult to rapidly and accurately identify faults for complicated SOFC systems, especially when simultaneous faults appear. In this research, a data-driven Multi-Label (ML) pattern identification approach is proposed to address the simultaneous fault diagnosis of SOFC systems. The framework of the simultaneous-fault diagnosis primarily includes two components: feature extraction and ML-SVM classifier. The simultaneous-fault diagnosis approach can be trained to diagnose simultaneous SOFC faults, such as fuel leakage, air leakage in different positions in the SOFC system, by just using simple training data sets consisting only single fault and not demanding simultaneous faults data. The experimental result shows the proposed framework can diagnose the simultaneous SOFC system faults with high accuracy requiring small number training data and low computational burden. In addition, Fault Inference Tree Analysis (FITA) is employed to identify the correlations among possible faults and their corresponding symptoms at the system component level.

Review on the current trends in tongue diagnosis systems.

Science.gov (United States)

Jung, Chang Jin; Jeon, Young Ju; Kim, Jong Yeol; Kim, Keun Ho

2012-12-01

Tongue diagnosis is an essential process to noninvasively assess the condition of a patient's internal organs in traditional medicine. To obtain quantitative and objective diagnostic results, image acquisition and analysis devices called tongue diagnosis systems (TDSs) are required. These systems consist of hardware including cameras, light sources, and a ColorChecker, and software for color correction, segmentation of tongue region, and tongue classification. To improve the performance of TDSs, various types TDSs have been developed. Hyperspectral imaging TDSs have been suggested to acquire more information than a two-dimensional (2D) image with visible light waves, as it allows collection of data from multiple bands. Three-dimensional (3D) imaging TDSs have been suggested to provide 3D geometry. In the near future, mobile devices like the smart phone will offer applications for assessment of health condition using tongue images. Various technologies for the TDS have respective unique advantages and specificities according to the application and diagnostic environment, but this variation may cause inconsistent diagnoses in practical clinical applications. In this manuscript, we reviewed the current trends in TDSs for the standardization of systems. In conclusion, the standardization of TDSs can supply the general public and oriental medical doctors with convenient, prompt, and accurate information with diagnostic results for assessing the health condition.

Radioimmunoassay for medical diagnosis and research

Energy Technology Data Exchange (ETDEWEB)

Dudley, R A; Vavrejn, B [International Atomic Energy Agency, Vienna (Austria). Div. of Life Sciences

1982-12-01

Health and disease in living systems depend on the dynamic interplay of thousands of biochemical substances occurring in living systems in concentrations ranging from parts per hundred to parts per billion or trillion. Radioimmunoassay (RIA) is a highly specific and sensitive technique for measuring the concentration of such biochemical substances. It represents one of the most dramatically expanding areas of medical diagnosis and research. Reviewed here is the recent progress on RIA, with emphasis on methodology and on its adaptation and application in developing countries. The number of biological substances (ligands) being assayed by RIA continues to expand. RIA is central to diagnosis, epidemiology and research. It has been successfully applied in the study of parasitic and infectious diseases. Introduction of RIA into developing countries, for which the Agency's help is sought and given, presents numerous problems: personnel, equipment, adaptability of techniques to local needs, and public support.

Adaptive neural network/expert system that learns fault diagnosis for different structures

Science.gov (United States)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

Science.gov (United States)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

Directory of Open Access Journals (Sweden)

Kun He

2018-04-01

Full Text Available Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM. An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44% was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.

Science.gov (United States)

He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

2018-04-23

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

Science.gov (United States)

He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

2018-01-01

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers. PMID:29690641

A maintenance support system with document handling capability

International Nuclear Information System (INIS)

Fukumoto, A.; Tsumura, K.; Fujii, M.; Tai, I.; Makimo, M.; Watanabe, T.

1990-01-01

An operation and maintenance support system, called 'Advanced Man-Machine System for Nuclear Power Plants' (MMS-NPP) is under development with the support of the Japanese Government. Taking full advantage of Artificial Intelligence technology, the system aims to enhance the capability of already developed 'Computerized Operator Support System (COSS)' and gives wider and more advanced support for operation and maintenance. With a brief overview of MMS-NPP, this paper describes a support system for plant patrol and equipment inspection. The system gives guidance for plant patrol and for equipment inspection and provides easy access to plant drawings and documents. A unique knowledge acquisition method, utilizing image processing technology, was proposed in building the system

[Is psychiatry relevant in autism? A brief historical perspective on the role of psychiatry in diagnosis, and support to autistic people].

Science.gov (United States)

Mottron, Laurent

2015-01-01

Based on an overview of the recent history of professional roles in autism diagnosis and support in the province of Quebec, this paper supports the view that hearing what autistic people say, combined with interdisciplinary, but hierarchically ruled task sharing in clinical settings, and to a pacific confrontation between scientific and clinical demands, prevents the high jacking of autism for corporatist or ideological purposes.

DIAGNOSIS OF PITCH AND LOAD DEFECTS

DEFF Research Database (Denmark)

2009-01-01

The invention relates to a method, system and computer readable code for diagnosis of pitch and/or load defects of e.g. wind turbines as well as wind turbines using said diagnosis method and/or comprising said diagnosis system.......The invention relates to a method, system and computer readable code for diagnosis of pitch and/or load defects of e.g. wind turbines as well as wind turbines using said diagnosis method and/or comprising said diagnosis system....

Airport Information Retrieval System (AIRS) System Support Manual

Science.gov (United States)

1973-01-01

This handbook is a support manual for prototype air traffic flow control automation system developed for the FAA's Systems Command Center. The system is implemented on a time-sharing computer and is designed to provide airport traffic load prediction...

Remote Diagnosis of the International Space Station Utilizing Telemetry Data

Science.gov (United States)

Deb, Somnath; Ghoshal, Sudipto; Malepati, Venkat; Domagala, Chuck; Patterson-Hine, Ann; Alena, Richard; Norvig, Peter (Technical Monitor)

2000-01-01

Modern systems such as fly-by-wire aircraft, nuclear power plants, manufacturing facilities, battlefields, etc., are all examples of highly connected network enabled systems. Many of these systems are also mission critical and need to be monitored round the clock. Such systems typically consist of embedded sensors in networked subsystems that can transmit data to central (or remote) monitoring stations. Moreover, many legacy are safety systems were originally not designed for real-time onboard diagnosis, but a critical and would benefit from such a solution. Embedding additional software or hardware in such systems is often considered too intrusive and introduces flight safety and validation concerns. Such systems can be equipped to transmit the sensor data to a remote-processing center for continuous health monitoring. At Qualtech Systems, we are developing a Remote Diagnosis Server (RDS) that can support multiple simultaneous diagnostic sessions from a variety of remote subsystems.

A study on the development of an automatic fault diagnosis system for testing NPP digital electronic circuits

International Nuclear Information System (INIS)

Kim, Dae Sik

1993-02-01

This paper describes a study on the development of an automatic fault diagnosis system for testing digital electronic circuits of nuclear power plants. Compared with the other conventional fault diagnosis systems, the system described in this paper uses Artificial Intelligence technique of model based reasoning and corroboration, which makes fault diagnosis much more efficient. In order to reduce the testing time, an optimal testing set which means a minimal testing set to determine whether or not the circuit is fault-free and to locate the faulty gate was derived. Compared with the testing using an exhaustive testing set, the testing using the optimal testing set makes fault diagnosis much more fast. Since the system diagnoses the circuit boards bases only on input and output signals, it can be further developed for on-line testing. The system was implemented on a microprocessor and was applied for Universal Circuit board testing of the Solid State protection System in nuclear power plants

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis

International Nuclear Information System (INIS)

Heo, Min Suk; Kavitha, Muthu Subash; Asano, Akira; Taguchi, Akira

2013-01-01

To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

Fault diagnosis in nuclear power plants using an artificial neural network technique

International Nuclear Information System (INIS)

Chou, H.P.; Prock, J.; Bonfert, J.P.

1993-01-01

Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

Intelligent Mechatronic Systems Modeling, Control and Diagnosis

CERN Document Server

Merzouki, Rochdi; Pathak, Pushparaj Mani; Ould Bouamama, Belkacem

2013-01-01

Acting as a support resource for practitioners and professionals looking to advance their understanding of complex mechatronic systems, Intelligent Mechatronic Systems explains their design and recent developments from first principles to practical applications. Detailed descriptions of the mathematical models of complex mechatronic systems, developed from fundamental physical relationships, are built on to develop innovative solutions with particular emphasis on physical model-based control strategies. Following a concurrent engineering approach, supported by industrial case studies, and drawing on the practical experience of the authors, Intelligent Mechatronic Systems covers range of topic and includes:  • An explanation of a common graphical tool for integrated design and its uses from modeling and simulation to the control synthesis • Introductions to key concepts such as different means of achieving fault tolerance, robust overwhelming control and force and impedance control • Dedicated chapters ...

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.

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Science.gov (United States)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

Developing Sustainable Life Support System Concepts

Science.gov (United States)

Thomas, Evan A.

2010-01-01

Sustainable spacecraft life support concepts may allow the development of more reliable technologies for long duration space missions. Currently, life support technologies at different levels of development are not well evaluated against each other, and evaluation methods do not account for long term reliability and sustainability of the hardware. This paper presents point-of-departure sustainability evaluation criteria for life support systems, that may allow more robust technology development, testing and comparison. An example sustainable water recovery system concept is presented.

Automated Degradation Diagnosis in Character Recognition System Subject to Camera Vibration

Directory of Open Access Journals (Sweden)

Chunmei Liu

2014-01-01

Full Text Available Degradation diagnosis plays an important role for degraded character processing, which can tell the recognition difficulty of a given degraded character. In this paper, we present a framework for automated degraded character recognition system by statistical syntactic approach using 3D primitive symbol, which is integrated by degradation diagnosis to provide accurate and reliable recognition results. Our contribution is to design the framework to build the character recognition submodels corresponding to degradation subject to camera vibration or out of focus. In each character recognition submodel, statistical syntactic approach using 3D primitive symbol is proposed to improve degraded character recognition performance. In the experiments, we show attractive experimental results, highlighting the system efficiency and recognition performance by statistical syntactic approach using 3D primitive symbol on the degraded character dataset.

Artificial neural network application for space station power system fault diagnosis

Science.gov (United States)

Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.