Spent-fuel storage - MRS and/or on-site?

International Nuclear Information System (INIS)

Fuierer, A.A.

1991-01-01

The US government through the Office of Civilian Radioactive Waste Management (OCRWM) is seeking by the use of an authorized negotiator a site for a monitored retrievable storage (MRS) facility. Based on a public information document provided by the office of the negotiator, the MRS will be an integral part of the federal system for safe and permanent disposal of the nation's high-level radioactive wastes. It is planned that the MRS will accept and store spent fuel above ground until a repository opens and spent fuel that has been stored is shipped from the MRS to the repository. Additional spent fuel stored at reactor sites will be shipped to the MRS, which will be used as a staging area to assemble dedicated trains for shipment to the repository. The intent of the MRS is to reduce utilities' needs to expand on-site storage of spent fuel. A utility viewpoint may emphasize an alternate set of priorities. The waste management system must be considered as an overall system involving both the utility and DOE that begins with the first discharge of spent nuclear fuel from a commercial reactor and ends with high-level waste in a final repository. Many studies have been made on individual components of a waste system. This study, with the benefit of past hands-on experience as a guide, looks at costs and reliability for a total system concept with particular emphasis on the interface between the utility and Department of Energy

A bench-top hyperspectral imaging system to classify beef from Nellore cattle based on tenderness

Science.gov (United States)

Nubiato, Keni Eduardo Zanoni; Mazon, Madeline Rezende; Antonelo, Daniel Silva; Calkins, Chris R.; Naganathan, Govindarajan Konda; Subbiah, Jeyamkondan; da Luz e Silva, Saulo

2018-03-01

The aim of this study was to evaluate the accuracy of classification of Nellore beef aged for 0, 7, 14, or 21 days and classification based on tenderness and aging period using a bench-top hyperspectral imaging system. A hyperspectral imaging system (λ = 928-2524 nm) was used to collect hyperspectral images of the Longissimus thoracis et lumborum (aging n = 376 and tenderness n = 345) of Nellore cattle. The image processing steps included selection of region of interest, extraction of spectra, and indentification and evalution of selected wavelengths for classification. Six linear discriminant models were developed to classify samples based on tenderness and aging period. The model using the first derivative of partial absorbance spectra (give wavelength range spectra) was able to classify steaks based on the tenderness with an overall accuracy of 89.8%. The model using the first derivative of full absorbance spectra was able to classify steaks based on aging period with an overall accuracy of 84.8%. The results demonstrate that the HIS may be a viable technology for classifying beef based on tenderness and aging period.

Ensemble Classifiers for Predicting HIV-1 Resistance from Three Rule-Based Genotypic Resistance Interpretation Systems.

Science.gov (United States)

Raposo, Letícia M; Nobre, Flavio F

2017-08-30

Resistance to antiretrovirals (ARVs) is a major problem faced by HIV-infected individuals. Different rule-based algorithms were developed to infer HIV-1 susceptibility to antiretrovirals from genotypic data. However, there is discordance between them, resulting in difficulties for clinical decisions about which treatment to use. Here, we developed ensemble classifiers integrating three interpretation algorithms: Agence Nationale de Recherche sur le SIDA (ANRS), Rega, and the genotypic resistance interpretation system from Stanford HIV Drug Resistance Database (HIVdb). Three approaches were applied to develop a classifier with a single resistance profile: stacked generalization, a simple plurality vote scheme and the selection of the interpretation system with the best performance. The strategies were compared with the Friedman's test and the performance of the classifiers was evaluated using the F-measure, sensitivity and specificity values. We found that the three strategies had similar performances for the selected antiretrovirals. For some cases, the stacking technique with naïve Bayes as the learning algorithm showed a statistically superior F-measure. This study demonstrates that ensemble classifiers can be an alternative tool for clinical decision-making since they provide a single resistance profile from the most commonly used resistance interpretation systems.

Capacity Building in Open Medical Record System (OpenMRS) in ...

International Development Research Centre (IDRC) Digital Library (Canada)

Capacity Building in Open Medical Record System (OpenMRS) in Rwanda ... Partners in Health (PIH), an international nongovernmental organization, has demonstrated the usefulness of ... Journal articles ... will fund social science, population and public health, and health systems research relevant to the emerging crisis.

Safeguards and security issues at the MRS facility

International Nuclear Information System (INIS)

McGuinn, E.; Birch, M.; Jones, J.; Floyd, W.

1993-01-01

The U.S. Department of Energy's (DOE) Office of Civilian Radioactive Waste Management (OCRWM) is responsible for disposing of the nation's high level radioactive waste in a way that ensures the protection of the public from any unacceptable radiological risks and the maintenance of the national security. To achieve these objectives, OCRWM plans to institute a Nuclear Regulatory Commission (NRC)-approved security program at its facilities including the Monitored Retrievable Storage (MRS) facility. This program will safeguard nuclear information and provide not only for the physical protection of facilities but also for the nuclear material being handled and stored. Several key regulatory issues were identified during the development of the safeguards and security (S ampersand S) program for the MRS. These issues relate to developing a realistic definition of the security threat at the MRS and establishing a single set of regulatory requirements. Resolution of these issues is important to implement a realistic S ampersand S program who scope is commensurate with the potential risk at the MRS and complies with all appropriate regulatory requirements. OCRWM is working toward a timely resolution of these issues and on the formulation of an S ampersand S program for implementation at the MRS. As an initial step, DOE has proposed an S ampersand S strategy for the MRS based on a set of assumed resolutions to the key regulatory issues. With this approach, the facility designers will be able to evaluate possible S ampersand S concepts for integration into the MRS early in the design process

Application of discrete event simulation to MRS design

International Nuclear Information System (INIS)

Bali, M.; Standley, W.

1993-01-01

The application of discrete event simulation to the Monitored, Retrievable Storage (MRS) material handling operations supported the MRS conceptual design effort and established a set of tools for use during MRS detail design and license application. The effort to develop a design analysis tool to support the MRS project started in 1991. The MRS simulation has so far identified potential savings and suggested methods of improving operations to enhance throughput. Immediately, simulation aided the MRS conceptual design effort through the investigation of alternative cask handling operations and the sizing and sharing of expensive equipment. The simulation also helped analyze the operability of the current design of MRS under various waste acceptance scenarios. Throughout the simulation effort, the model development and experimentation resulted in early identification and resolution of several design and operational issues

Cooking up an open source EMR for developing countries: OpenMRS - a recipe for successful collaboration.

Science.gov (United States)

Mamlin, Burke W; Biondich, Paul G; Wolfe, Ben A; Fraser, Hamish; Jazayeri, Darius; Allen, Christian; Miranda, Justin; Tierney, William M

2006-01-01

Millions of people are continue to die each year from HIV/AIDS. The majority of infected persons (>95%) live in the developing world. A worthy response to this pandemic will require coordinated, scalable, and flexible information systems. We describe the OpenMRS system, an open source, collaborative effort that can serve as a foundation for EMR development in developing countries. We report our progress to date, lessons learned, and future directions.

Can-Evo-Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences.

Science.gov (United States)

Ali, Safdar; Majid, Abdul

2015-04-01

The diagnostic of human breast cancer is an intricate process and specific indicators may produce negative results. In order to avoid misleading results, accurate and reliable diagnostic system for breast cancer is indispensable. Recently, several interesting machine-learning (ML) approaches are proposed for prediction of breast cancer. To this end, we developed a novel classifier stacking based evolutionary ensemble system "Can-Evo-Ens" for predicting amino acid sequences associated with breast cancer. In this paper, first, we selected four diverse-type of ML algorithms of Naïve Bayes, K-Nearest Neighbor, Support Vector Machines, and Random Forest as base-level classifiers. These classifiers are trained individually in different feature spaces using physicochemical properties of amino acids. In order to exploit the decision spaces, the preliminary predictions of base-level classifiers are stacked. Genetic programming (GP) is then employed to develop a meta-classifier that optimal combine the predictions of the base classifiers. The most suitable threshold value of the best-evolved predictor is computed using Particle Swarm Optimization technique. Our experiments have demonstrated the robustness of Can-Evo-Ens system for independent validation dataset. The proposed system has achieved the highest value of Area Under Curve (AUC) of ROC Curve of 99.95% for cancer prediction. The comparative results revealed that proposed approach is better than individual ML approaches and conventional ensemble approaches of AdaBoostM1, Bagging, GentleBoost, and Random Subspace. It is expected that the proposed novel system would have a major impact on the fields of Biomedical, Genomics, Proteomics, Bioinformatics, and Drug Development. Copyright © 2015 Elsevier Inc. All rights reserved.

Development and Validation of the Nursing Home Minimum Data Set 3.0 Mortality Risk Score (MRS3).

Science.gov (United States)

Thomas, Kali S; Ogarek, Jessica A; Teno, Joan M; Gozalo, Pedro L; Mor, Vincent

2018-03-05

To develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments. We developed a training cohort of Medicare beneficiaries newly admitted to U.S. NHs during 2012 (N=1,426,815) and a testing cohort from 2013 (N=1,160,964). Data came from the MDS 3.0 assessments linked to the Medicare Beneficiary Summary File. Using the training dataset, we developed a composite MDS 3.0 Mortality Risk Score (MRS3) consisting of 17 clinical items and patients' age groups based on their relation to 30-day mortality. We assessed the calibration and discrimination of the MRS3 in predicting 30-day and 60-day mortality and compared its performance to the Charlson Comorbidity Index and the clinician's assessment of 6-month prognosis measured at admission. The 30-day and 60-day mortality rate for the testing population was 2.8% and 5.6%, respectively. Results from logistic regression models suggest that the MRS3 performed well in predicting death within 30 and 60 days (C-Statistics of 0.744 (95%CL = 0.741, 0.747) and 0.709 (95%CL=0.706, 0.711), respectively). The MRS3 was a superior predictor of mortality compared to the Charlson Comorbidity Index (C-statistics of 0.611 (95%CL=0.607, 0.615) and 0.608 (95%CL=0.605, 0.610)) and the clinicians' assessments of patients' 6-month prognoses (C-statistics of 0.543 (95%CL=0.542, 0.545) and 0.528 (95%CL=0.527, 0.529). The MRS3 is a good predictor of mortality and can be useful in guiding decision-making, informing plans of care, and adjusting for patients' risk of mortality.

Reinforcement Learning Based Artificial Immune Classifier

Directory of Open Access Journals (Sweden)

Mehmet Karakose

2013-01-01

Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

1H-MRS is useful to reinforce the suspicion of primary central nervous system lymphoma prior to surgery

International Nuclear Information System (INIS)

Mora, Paloma; Majos, Carles; Aguilera, Carles; Castaner, Sara; Sanchez, Juan J.; Gabarros, Andreu; Muntane, Amadeo; Arus, Carles

2014-01-01

To assess whether 1 H-MRS may be useful to reinforce the radiological suspicion of PCNSL. In this retrospective study, we included 546 patients with untreated brain tumours in which single-voxel spectroscopy at TE 30 ms and 136 ms had been performed. The patients were split into two subgroups: ''training set'' and ''test set.'' Differences between PCNSL and five other types of intracranial tumours were assessed in the test set of patients using the Mann-Whitney U nonparametric test and cut-off values for pair-wise comparisons defined by constructing receiver operating characteristic curves. These thresholds were used to construct classifiers for binary comparison between PCNSL and non-PCNSL. The performance of the obtained classifiers was assessed in the independent test set of patients. Significant differences were found between PCNSL and the other groups evaluated. All bilateral comparisons performed in the test set obtained accuracy values above 70 % (71-89 %). Lipids were found to be useful to discriminate between PCNSL and glioblastoma/metastasis at short TE. Myo-inositol resonance was found to be very consistent for discriminating between PCNSL and astrocytomas at short TE. 1 H-MRS is useful to reinforce diagnostic suspicion of PCNSL on MRI. (orig.)

Proton MRS in neurological disorders

Energy Technology Data Exchange (ETDEWEB)

Bonavita, S.; Di Salle, F.; Tedeschi, G

1999-05-01

Proton magnetic resonance spectroscopy ({sup 1}H MRS) permits the acquisition of the signal arising from several brain metabolites. At long echo-time (TE) {sup 1}H MRS can detect N-acetyl-aspartate containing compounds, choline containing compounds, creatine+phosphocreatine and lactate. At short TE, lipids, tryglicerides, alanine, glutamate, glutamine, GABA, scyllo-inositol, glucose, myo-inositol, carnosine and histydine are visible. {sup 1}H MRS can be performed with single-voxel, multivoxel, single slice and multislice techniques. With single voxel {sup 1}H MRS it is possible to measure metabolites relaxation time, which allows the measurement of metabolite concentrations. This technique can be useful in the study of focal lesions in the central nervous system (CNS) such as epilepsy (pre-surgical identification of epileptic focus), brain tumors (evaluation of recurrence and radiation necrosis), stroke, multiple sclerosis, etc. Single slice and multislice {sup 1}H MRS imaging ({sup 1}H MRSI) can be performed only at long TE and permits the mapping of the brain metabolites distribution which makes them particularly useful in studying diffuse diseases and heterogeneous lesions of the CNS. {sup 1}H MRS can also be useful in the evaluation of 'ischemic penumbra' of stroke; developmental (myelin and neuronal dysgenesis); head trauma (evaluation of cerebral damage not visible with MRI); degenerative disorders (identification of microscopic pathology not visible with MRI); and metabolic diseases (metabolic disturbances with specific metabolic patterns)

FACSIM/MRS-1: Cask receiving and consolidation model documentation and user's guide

International Nuclear Information System (INIS)

Lotz, T.L.; Shay, M.R.

1987-06-01

The Pacific Northwest Laboratory (PNL) has developed a stochastic computer model, FACSIM/MRS, to assist in assessing the operational performance of the Monitored Retrievable Storage (MRS) waste-handling facility. This report provides the documentation and user's guide for the component FACSIM/MRS-1, which is also referred to as the front-end model. The FACSIM/MRS-1 model simulates the MRS cask-receiving and spent-fuel consolidation activities. The results of the assessment of the operational performance of these activities are contained in a second report, FACSIM/MRS-1: Cask Receiving and Consolidation Performance Assessment (Lotz and Shay 1987). The model of MRS canister storage and shipping operations is presented in FACSIM/MRS-2: Storage and Shipping Model Documentation and User's Guide (Huber et al. 1987). The FACSIM/MRS model uses the commercially available FORTRAN-based SIMAN (SIMulation ANalysis language) simulation package (Pegden 1982). SIMAN provides a set of FORTRAN-coded commands, called block operations, which are used to build detailed models of continuous or discrete events that make up the operations of any process, such as the operation of an MRS facility. The FACSIM models were designed to run on either an IBM-PC or a VAX minicomputer. The FACSIM/MRS-1 model is flexible enough to collect statistics concerning almost any aspect of the cask receiving and consolidation operations of an MRS facility. The MRS model presently collects statistics on 51 quantities of interest during the simulation. SIMAN reports the statistics with two forms of output: a SIMAN simulation summary and an optional set of SIMAN output files containing data for use by more detailed post processors and report generators

Rethinking the MRS

International Nuclear Information System (INIS)

Colglazier, E.W.

1989-01-01

In this paper various options for utilizing monitored retrievable storage of civilian spent fuel have been compared using the criteria of maximizing the likelihood of implementing successfully a comprehensive U.S. nuclear waste management system (taking into account scientific and institutional uncertainties) and minimizing the costs, risks, and other impacts. The option that appears to be most robust for dealing with the key uncertainties has two components: an integrated management system that maximizes dry-storage at reactors using dual purpose casks and shipment via dedicated trains and a more experimental approach to the development of the repository at Yucca Mountain and authorization of an unconstrained MRS facility on the Nevada Test Site

MRS Action Plan Task B report: Analyses of alternative designs and operating approaches for a Monitored Retrievable Storage Facility

International Nuclear Information System (INIS)

Woods, W.D.; Jowdy, A.K.; Keehn, C.H.; Gale, R.M.; Smith, R.I.

1988-12-01

The Nuclear Waste Policy Amendments Act (NWPAA) instituted a number of changes in the DOE commercial nuclear waste management system. After passage of the Act, the DOE initiated a number of systems studies to reevaluate the role of Monitored Retrievable Storage (MRS) within the federal waste management system. This report summarizes the results of a study to determine the schedules and costs of developing those MRS facilities needed under a number of scenarios, with differing functions allocated to the MRS and/or different spent fuel acceptance schedules. Nine cases were defined for the system study, seven of which included an MRS Facility. The study cases or scenarios evaluated varied relative to the specific functions to be performed at the MRS. The scenarios ranged in magnitude from storage and shipment of bare, intact spent fuel to consolidating the spent fuel into repository emplacement containers prior to storage and shipment. Each scenario required specific modifications to be made to the design developed for the MRS proposal to Congress (the Conceptual Design Report). 41 figs., 326 tabs

Yucca Mountain Project waste package design for MRS [Monitored Retrievable Storage] system studies

International Nuclear Information System (INIS)

Nelson, T.; Russell, E.; Johnson, G.L.; Morissette, R.; Stahl, D.; LaMonica, L.; Hertel, G.

1989-04-01

This report, prepared by the Yucca Mountain Project, is the report for Task E of the MRS System Study. A number of assumptions were necessary prior to initiation of this system study. These assumptions have been defined in Section 2 for the packaging scenarios, the waste forms, and the waste package concepts and materials. Existing concepts were utilized because of schedule constraints. Section 3 provides a discussion of sensitivity considerations regarding the impact of different assumptions on the overall result of the system study. With the exception of rod consolidation considerations, the system study should not be sensitive to the parameters assumed for the waste package. The current reference waste package materials and concepts are presented in Section 4. Although stainless steel is assumed for this study, a container material has not yet been selected for Advanced Conceptual Design (ACD) from the six candidates currently under study. Section 5 discusses the current thinking for possible alternate waste package materials and concepts. These concepts are being considered in the event that the waste package emplacement environment is more severe than is currently anticipated. Task E also provides a concept in Section 6 for an MRS canister to contain consolidated fuel for storage at the MRS and eventual shipment to the repository. 5 refs., 14 figs., 10 tabs

A Supervised Multiclass Classifier for an Autocoding System

Directory of Open Access Journals (Sweden)

Yukako Toko

2017-11-01

Full Text Available Classification is often required in various contexts, including in the field of official statistics. In the previous study, we have developed a multiclass classifier that can classify short text descriptions with high accuracy. The algorithm borrows the concept of the naïve Bayes classifier and is so simple that its structure is easily understandable. The proposed classifier has the following two advantages. First, the processing times for both learning and classifying are extremely practical. Second, the proposed classifier yields high-accuracy results for a large portion of a dataset. We have previously developed an autocoding system for the Family Income and Expenditure Survey in Japan that has a better performing classifier. While the original system was developed in Perl in order to improve the efficiency of the coding process of short Japanese texts, the proposed system is implemented in the R programming language in order to explore versatility and is modified to make the system easily applicable to English text descriptions, in consideration of the increasing number of R users in the field of official statistics. We are planning to publish the proposed classifier as an R-package. The proposed classifier would be generally applicable to other classification tasks including coding activities in the field of official statistics, and it would contribute greatly to improving their efficiency.

Incorporating public outreach in the MRS Design

International Nuclear Information System (INIS)

Richardson, J.; Charles, C.

1993-01-01

As the design of the proposed DOE-OCRWM MRS facility progresses, facility layouts that allow for frequent and unhindered viewing of the MRS processes by the public should be developed. By allowing the public to observe operations, the DOE believes that trust and confidence in the program will greatly improve. A program should be developed to educate the public so that they can see for themselves that the MRS facility does not pose an undue risk to public health and that the DOE is open, honest, and can be trusted. At the same time, a positive safety message will be implicitly conveyed, which should engender more positive public feelings about nuclear energy. Visitory access to the MRS Facility should not be limited to a Visitors Center, but rather should be expanded to allow the general public a chance to view the actual processes, such as the cask handling and spent fuel transfers that go on within the facility. This paper will describe the desirable features of any approach to give unlimited public access to the MRS facility and operations and thereby enhance public understanding and acceptance

Multiple classifier systems in texton-based approach for the classification of CT images of Lung

DEFF Research Database (Denmark)

Gangeh, Mehrdad J.; Sørensen, Lauge; Shaker, Saher B.

2010-01-01

In this paper, we propose using texton signatures based on raw pixel representation along with a parallel multiple classifier system for the classification of emphysema in computed tomography images of the lung. The multiple classifier system is composed of support vector machines on the texton.......e., texton size and k value in k-means. Our results show that while aggregation of single decisions by SVMs over various k values using multiple classifier systems helps to improve the results compared to single SVMs, combining over different texton sizes is not beneficial. The performance of the proposed...

Mescalero Apache Tribe Monitored Retrievable Storage (MRS)

Energy Technology Data Exchange (ETDEWEB)

Peso, F.

1992-03-13

The Nuclear Waste Policy Act of 1982, as amended, authorizes the siting, construction and operation of a Monitored Retrievable Storage (MRS) facility. The MRS is intended to be used for the temporary storage of spent nuclear fuel from the nation's nuclear power plants beginning as early as 1998. Pursuant to the Nuclear Waste Policy Act, the Office of the Nuclear Waste Negotiator was created. On October 7, 1991, the Nuclear Waste Negotiator invited the governors of states and the Presidents of Indian tribes to apply for government grants in order to conduct a study to assess under what conditions, if any, they might consider hosting an MRS facility. Pursuant to this invitation, on October 11, 1991 the Mescalero Apache Indian Tribe of Mescalero, NM applied for a grant to conduct a phased, preliminary study of the safety, technical, political, environmental, social and economic feasibility of hosting an MRS. The preliminary study included: (1) An investigative education process to facilitate the Tribe's comprehensive understanding of the safety, environmental, technical, social, political, and economic aspects of hosting an MRS, and; (2) The development of an extensive program that is enabling the Tribe, in collaboration with the Negotiator, to reach an informed and carefully researched decision regarding the conditions, (if any), under which further pursuit of the MRS would be considered. The Phase 1 grant application enabled the Tribe to begin the initial activities necessary to determine whether further consideration is warranted for hosting the MRS facility. The Tribe intends to pursue continued study of the MRS in order to meet the following objectives: (1) Continuing the education process towards a comprehensive understanding of the safety, environmental, technical, social and economic aspects of the MRS; (2) Conducting an effective public participation and information program; (3) Participating in MRS meetings.

Mescalero Apache Tribe Monitored Retrievable Storage (MRS)

International Nuclear Information System (INIS)

Peso, F.

1992-01-01

The Nuclear Waste Policy Act of 1982, as amended, authorizes the siting, construction and operation of a Monitored Retrievable Storage (MRS) facility. The MRS is intended to be used for the temporary storage of spent nuclear fuel from the nation's nuclear power plants beginning as early as 1998. Pursuant to the Nuclear Waste Policy Act, the Office of the Nuclear Waste Negotiator was created. On October 7, 1991, the Nuclear Waste Negotiator invited the governors of states and the Presidents of Indian tribes to apply for government grants in order to conduct a study to assess under what conditions, if any, they might consider hosting an MRS facility. Pursuant to this invitation, on October 11, 1991 the Mescalero Apache Indian Tribe of Mescalero, NM applied for a grant to conduct a phased, preliminary study of the safety, technical, political, environmental, social and economic feasibility of hosting an MRS. The preliminary study included: (1) An investigative education process to facilitate the Tribe's comprehensive understanding of the safety, environmental, technical, social, political, and economic aspects of hosting an MRS, and; (2) The development of an extensive program that is enabling the Tribe, in collaboration with the Negotiator, to reach an informed and carefully researched decision regarding the conditions, (if any), under which further pursuit of the MRS would be considered. The Phase 1 grant application enabled the Tribe to begin the initial activities necessary to determine whether further consideration is warranted for hosting the MRS facility. The Tribe intends to pursue continued study of the MRS in order to meet the following objectives: (1) Continuing the education process towards a comprehensive understanding of the safety, environmental, technical, social and economic aspects of the MRS; (2) Conducting an effective public participation and information program; (3) Participating in MRS meetings

PREFACE: E-MRS 2012 Spring Meeting, Symposium M: More than Moore: Novel materials approaches for functionalized Silicon based Microelectronics

Science.gov (United States)

Wenger, Christian; Fompeyrine, Jean; Vallée, Christophe; Locquet, Jean-Pierre

2012-12-01

More than Moore explores a new area of Silicon based microelectronics, which reaches beyond the boundaries of conventional semiconductor applications. Creating new functionality to semiconductor circuits, More than Moore focuses on motivating new technological possibilities. In the past decades, the main stream of microelectronics progresses was mainly powered by Moore's law, with two focused development arenas, namely, IC miniaturization down to nano scale, and SoC based system integration. While the microelectronics community continues to invent new solutions around the world to keep Moore's law alive, there is increasing momentum for the development of 'More than Moore' technologies which are based on silicon technologies but do not simply scale with Moore's law. Typical examples are RF, Power/HV, Passives, Sensor/Actuator/MEMS or Bio-chips. The More than Moore strategy is driven by the increasing social needs for high level heterogeneous system integration including non-digital functions, the necessity to speed up innovative product creation and to broaden the product portfolio of wafer fabs, and the limiting cost and time factors of advanced SoC development. It is believed that More than Moore will add value to society on top of and beyond advanced CMOS with fast increasing marketing potentials. Important key challenges for the realization of the 'More than Moore' strategy are: perspective materials for future THz devices materials systems for embedded sensors and actuators perspective materials for epitaxial approaches material systems for embedded innovative memory technologies development of new materials with customized characteristics The Hot topics covered by the symposium M (More than Moore: Novel materials approaches for functionalized Silicon based Microelectronics) at E-MRS 2012 Spring Meeting, 14-18 May 2012 have been: development of functional ceramics thin films New dielectric materials for advanced microelectronics bio- and CMOS compatible

Leveraging the Value of Human Relationships to Improve Health Outcomes. Lessons learned from the OpenMRS Electronic Health Record System.

Science.gov (United States)

Kasthurirathne, Suranga N; Mamlin, Burke W; Cullen, Theresa

2017-02-01

Despite significant awareness on the value of leveraging patient relationships across the healthcare continuum, there is no research on the potential of using Electronic Health Record (EHR) systems to store structured patient relationship data, or its impact on enabling better healthcare. We sought to identify which EHR systems supported effective patient relationship data collection, and for systems that do, what types of relationship data is collected, how this data is used, and the perceived value of doing so. We performed a literature search to identify EHR systems that supported patient relationship data collection. Based on our results, we defined attributes of an effective patient relationship model. The Open Medical Record System (OpenMRS), an open source medical record platform for underserved settings met our eligibility criteria for effective patient relationship collection. We performed a survey to understand how the OpenMRS patient relationship model was used, and how it brought value to implementers. The OpenMRS patient relationship model has won widespread adoption across many implementations and is perceived to be valuable in enabling better health care delivery. Patient relationship information is widely used for community health programs and enabling chronic care. Additionally, many OpenMRS implementers were using this feature to collect custom relationship types for implementation specific needs. We believe that flexible patient relationship data collection is critical for better healthcare, and can inform community care and chronic care initiatives across the world. Additionally, patient relationship data could also be leveraged for many other initiatives such as patient centric care and in the field of precision medicine.

A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

Science.gov (United States)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

Evaluation of storage/transportation options to support criteria development for the Phase I MRS

International Nuclear Information System (INIS)

Sorenson, K.B.; Brown, N.N.; Bennett, P.C.; Lake, W.

1991-01-01

The DOE's Office of Civilian Waste Management (OCRWM) plans to develop an interim storage facility to enable acceptance of spent fuel in 1998. It is estimated that this interim storage facility would be needed for about two years. A Monitored Retrievable Storage (MRS) facility is anticipated in 2000 and a repository in 2010. Acceptance and transport of spent fuel by DOE/OCRWM in 1998 will require an operating transportation system. Because this interim storage facility is not yet defined, development of an optimally compatible transportation system is not a certainty. In order to assure a transport capability for 1998 acceptance of spent fuel, it was decided that the OCRWM transportation program had to identify likely options for an interim storage facility, including identification of the components needed for compatibility between likely interim storage facility options and transportation. Primary attention was given to existing hardware, although conceptual designs were also considered. A systems-based probabilistic decision model was suggested by Sandia National Labs. and accepted by DOE/OCRWM's transportation program. Performance of the evaluation task involved several elements of the transportation program. This paper describes the decision model developed to accomplish this task, along with some of the results and conclusions

Interfacing the existing cask fleet with the MRS

International Nuclear Information System (INIS)

Doman, J.W.; Hahn, R.E.

1992-01-01

This paper reports that the Department of Energy (DOE) is considering the possibility of using the existing fleet of casks to achieve spent fuel receipt at the Monitored Retrievable Storage (MRS) facility. The existing cask fleet includes the NLI-1/2, the NAC-LWT, the TN-8 (and TN-8L), the TN-9, and the IF-300 casks. Other casks may be available, but their status is not certain. Use of the existing cask fleet at the MRS places additional design requirements on the system, and specifically affects the cask-to-MRS interface. The decision to use the existing cask fleet also places additional demands on training needs and operator certification, and the configuration management system. Some existing cask designs may not be able to mate with a bottom opening hot cell MRS. Use of the existing cask fleet also greatly increases the number of shipments that must be received, to the point that a facility larger than originally envisioned may be required

{sup 1}H-MRS is useful to reinforce the suspicion of primary central nervous system lymphoma prior to surgery

Energy Technology Data Exchange (ETDEWEB)

Mora, Paloma [Hospital Universitari de Bellvitge, Department of Radiology, L' Hospitalet de Llobregat (Spain); Majos, Carles; Aguilera, Carles [Hospital Universitari de Bellvitge, Department of Radiology, Institut de Diagnostic per la Imatge (IDI), Centre Bellvitge, L' Hospitalet de Llobregat (Spain); Centro de Investigacion en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Valles (Spain); Castaner, Sara; Sanchez, Juan J. [Hospital Universitari de Bellvitge, Department of Radiology, Institut de Diagnostic per la Imatge (IDI), Centre Bellvitge, L' Hospitalet de Llobregat (Spain); Gabarros, Andreu [Hospital Universitari de Bellvitge, Department of Neurosurgery, L' Hospitalet de Llobregat (Spain); Muntane, Amadeo [Hospital Universitari de Bellvitge, Department of Radiology, L' Hospitalet de Llobregat (Spain); Arus, Carles [Centro de Investigacion en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Valles (Spain); Universitat Autonoma de Barcelona, Department de Bioquimica i Biologia Molecular, Unitat de Bioquimica de Biociencies, Cerdanyola del Valles (Spain); Universitat Autonoma de Barcelona, Institut de Biotecnologia i de Biomedicina, Cerdanyola del Valles (Spain)

2014-11-15

To assess whether {sup 1}H-MRS may be useful to reinforce the radiological suspicion of PCNSL. In this retrospective study, we included 546 patients with untreated brain tumours in which single-voxel spectroscopy at TE 30 ms and 136 ms had been performed. The patients were split into two subgroups: ''training set'' and ''test set.'' Differences between PCNSL and five other types of intracranial tumours were assessed in the test set of patients using the Mann-Whitney U nonparametric test and cut-off values for pair-wise comparisons defined by constructing receiver operating characteristic curves. These thresholds were used to construct classifiers for binary comparison between PCNSL and non-PCNSL. The performance of the obtained classifiers was assessed in the independent test set of patients. Significant differences were found between PCNSL and the other groups evaluated. All bilateral comparisons performed in the test set obtained accuracy values above 70 % (71-89 %). Lipids were found to be useful to discriminate between PCNSL and glioblastoma/metastasis at short TE. Myo-inositol resonance was found to be very consistent for discriminating between PCNSL and astrocytomas at short TE. {sup 1}H-MRS is useful to reinforce diagnostic suspicion of PCNSL on MRI. (orig.)

Monitored retrievable storage (MRS) facility and salt repository integration: Engineering study report

International Nuclear Information System (INIS)

1987-07-01

This MRS Facility and Salt Repository Integration Study evaluates the impacts of an integrated MRS/Salt Repository Waste Management System on the Salt Repository Surface facilities' design, operations, cost, and schedule. Eight separate cases were studied ranging from a two phase repository design with no MRS facility to a design in which the repository only received package waste from the MRS facility for emplacement. The addition of the MRS facility to the Waste Management System significantly reduced the capital cost of the salt repository. All but one of the cases studied were capable of meeting the waste acceptance data. The reduction in the size and complexity of the Salt Repository waste handling building with the integration of the MRS facility reduces the design and operating staff requirements. 7 refs., 35 figs., 43 tabs

Integral Monitored Retrievable Storage (MRS) Facility conceptual design report

International Nuclear Information System (INIS)

1985-09-01

This document, Volume 5 Book 1, contains cost estimate summaries for a monitored retrievable storage (MRS) facility. The cost estimate is based on the engineering performed during the conceptual design phase of the MRS Facility project

Application of NUHOMS to an integrated MRS/transportation system

International Nuclear Information System (INIS)

Rosa, J.M.; Lehnert, R.A.; Quinn, R.D.

1992-01-01

This paper reports that storage of spent fuel a reactor sites in weld sealed canisters has been increasing steadily since 1989, with three utilities now having ISFSIs which employ the NUHOMS technology. Expansion of existing Independent Spent Fuel Storage Installations (ISFSIs) and implementation of new ones will result in a substantial fraction of the at-reactor spent fuel inventory in dry storage canisters by 1998 and beyond. Since the same technology is readily applicable to the MRS, it is advantageous to transfer canisters directly from the ISFSI to the MRS for storage thee without the need to reopen the canisters or rehandle the fuel. A store-only MRS offers the possibility of a simple, economical facility which can more easily be demonstrated to be temporary and environmentally benign. Transportation of an intact canister is feasible in a shipping cask of conventional design that is compatible with existing railcars and railroad interchange rules. The use of canisterized fuel also has advantages for eventual shipment and disposal at the federal repository

Use of annotated outlines to prepare guidance for license applications for the MRS and MGDS

International Nuclear Information System (INIS)

Roberts, J.; Griffin, W.R.

1992-01-01

This paper reports that the Office of Civilian Radioactive Waste Management (OCRWM) has embarked on an aggressive program to develop guidance for preparation of the License Applications for the Mined Geological Disposal System (MGDS) and Monitored Retrievable Storage (MRS). The endeavor is a team effort that will utilize personnel and funding from the Office of Systems and Compliance at DOE Headquarters, the MRS Project (i.e., DOE Office of Storage and Transportation) and the Yucca Mountain Project (i.e., DOE Office of Geologic Disposal). The endeavor was initiated in the Spring of 1991. It will continue via an iterative process until License Applications are completed for the MRS and MGDS projects

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

Science.gov (United States)

Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

2018-04-01

Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods

Siting of an MRS facility: identification of a geographic region that reduces transportation requirements

International Nuclear Information System (INIS)

Holter, G.M.; Braitman, J.L.

1985-04-01

The study reported here was undertaken as part of the site screening and evaluation activities for the Monitored Retrievable Storage (MRS) Program of the Office of Civilian Radioactive Waste Management (OCRWM), Department of Energy (DOE). Its primary purpose was to determine: the location and shape of a preferred geographic region within which locating an MRS facility would minimize total shipment miles for spent fuel transported through the MRS facility to a repository, and the sensitivity of the location and shape of this region and the reduction in total shipment miles to possible variations in waste management system logistics. As a result of this analysis, a geographic region has been identified which is preferred for siting an MRS facility. This region will be referred to as the preferred region in this study. Siting an MRS facility in the preferred region will limit total shipment miles (i.e., the total miles traveled for all shipments of spent fuel) to and from the MRS facility to within 20% of the lowest achievable. The region is preferred for a mixed truck/rail system of transport from reactors to the MRS facility. It is assumed that rail will be used to ship spent fuel from the MRS facility to a geologic repository for disposal. Siting an MRS facility in the preferred region will reduce total shipment miles for all currently considered system logistics options which include an MRS facility in the system. These options include: any first repository location, the possible range of spent fuel consolidation at the MRS, use of multi-cask or single-cask train shipments, use of current or future spent fuel transport casks, servicing only the first or both the first and second repositories, and shipment of fuel from western reactors either through the MRS facility or to a western facility (a second, smaller MRS facility or the first repository)

Fusion of classifiers for REIS-based detection of suspicious breast lesions

Science.gov (United States)

Lederman, Dror; Wang, Xingwei; Zheng, Bin; Sumkin, Jules H.; Tublin, Mitchell; Gur, David

2011-03-01

After developing a multi-probe resonance-frequency electrical impedance spectroscopy (REIS) system aimed at detecting women with breast abnormalities that may indicate a developing breast cancer, we have been conducting a prospective clinical study to explore the feasibility of applying this REIS system to classify younger women (breast cancer. The system comprises one central probe placed in contact with the nipple, and six additional probes uniformly distributed along an outside circle to be placed in contact with six points on the outer breast skin surface. In this preliminary study, we selected an initial set of 174 examinations on participants that have completed REIS examinations and have clinical status verification. Among these, 66 examinations were recommended for biopsy due to findings of a highly suspicious breast lesion ("positives"), and 108 were determined as negative during imaging based procedures ("negatives"). A set of REIS-based features, extracted using a mirror-matched approach, was computed and fed into five machine learning classifiers. A genetic algorithm was used to select an optimal subset of features for each of the five classifiers. Three fusion rules, namely sum rule, weighted sum rule and weighted median rule, were used to combine the results of the classifiers. Performance evaluation was performed using a leave-one-case-out cross-validation method. The results indicated that REIS may provide a new technology to identify younger women with higher than average risk of having or developing breast cancer. Furthermore, it was shown that fusion rule, such as a weighted median fusion rule and a weighted sum fusion rule may improve performance as compared with the highest performing single classifier.

Integral Monitored Retrievable Storage (MRS) Facility conceptual design report

International Nuclear Information System (INIS)

1985-09-01

The Basis for Design established the functional requirements and design criteria for an Integral Monitored Retrievable Storage (MRS) facility. The MRS Facility design, described in this report, is based on those requirements and includes all infrastructure, facilities, and equipment required to routinely receive, unload, prepare for storage, and store spent fuel (SF), high-level waste (HLW), and transuranic waste (TRU), and to decontaminate and return shipping casks received by both rail and truck. The facility is complete with all supporting facilities to make the MRS Facility a self-sufficient installation

Alternate technologies for MRS design

International Nuclear Information System (INIS)

Smith, R.I.; Triplett, M.B.; Ashton, W.B.; Kelly, W.S.

1984-01-01

This paper describes the process conducted by the Pacific Northwest Laboratory (PNL) to evaluate candidate MRS concepts and to recommend the two most preferred concepts. The eight concepts studied are: metal cask (stationary and transportable); concrete cask (silo); concrete cask-in-trench; field drywell; tunnel drywell; open cycle vault; closed cycle vault; and tunnel rack vault. To achieve a more equitable comparison of the concepts, conceptual design analyses were performed for the candidate concepts using a common set of specified design requirements selected with consideration of the MRS mission. Using this normalized conceptual design information, the MRS concepts were evaluated and compared on the basis of their relative performance on seven criteria: flexibility, concept maturity, cost, environmental impacts, safety and licensing, socioeconomic impacts, and siting requirements. These seven criteria were judged to form a reasonable and complete basis for the evaluation of MRS concepts' ability to satisfy the MRS mission requirements. 5 references, 8 figures, 1 table

Quality of life and hormone use: new validation results of MRS scale

Directory of Open Access Journals (Sweden)

Heinemann Lothar AJ

2006-05-01

Full Text Available Abstract Background The Menopause Rating Scale is a health-related Quality of Life scale developed in the early 1990s and step-by-step validated since then. Recently the MRS scale was validated as outcomes measure for hormone therapy. The suspicion however was expressed that the data were too optimistic due to methodological problems of the study. A new study became available to check how founded this suspicion was. Method An open post-marketing study of 3282 women with pre- and post- treatment data of the self-administered version of the MRS scale was analyzed to evaluate the capacity of the scale to detect hormone treatment related effects with the MRS scale. The main results were then compared with the old study where the interview-based version of the MRS scale was used. Results The hormone-therapy related improvement of complaints relative to the baseline score was about or less than 30% in total or domain scores, whereas it exceeded 30% improvement in the old study. Similarly, the relative improvement after therapy, stratified by the degree of severity at baseline, was lower in the new than in the old study, but had the same slope. Although we cannot exclude different treatment effects with the study method used, this supports our hypothesis that the individual MRS interviews performed by the physician biased the results towards over-estimation of the treatment effects. This hypothesis is underlined by the degree of concordance of physician's assessment and patient's perception of treatment success (MRS results: Sensitivity (correct prediction of the positive assessment by the treating physician of the MRS and specificity (correct prediction of a negative assessment by the physician were lower than the results obtained with the interview-based MRS scale in the previous publication. Conclusion The study confirmed evidence for the capacity of the MRS scale to measure treatment effects on quality of life across the full range of severity of

Development of the network architecture of the Canadian MSAT system

Science.gov (United States)

Davies, N. George; Shoamanesh, Alireza; Leung, Victor C. M.

1988-05-01

A description is given of the present concept for the Canadian Mobile Satellite (MSAT) System and the development of the network architecture which will accommodate the planned family of three categories of service: a mobile radio service (MRS), a mobile telephone service (MTS), and a mobile data service (MDS). The MSAT satellite will have cross-strapped L-band and Ku-band transponders to provide communications services between L-band mobile terminals and fixed base stations supporting dispatcher-type MRS, gateway stations supporting MTS interconnections to the public telephone network, data hub stations supporting the MDS, and the network control center. The currently perceived centralized architecture with demand assignment multiple access for the circuit switched MRS, MTS and permanently assigned channels for the packet switched MDS is discussed.

Towards Standardized Patient Data Exchange: Integrating a FHIR Based API for the Open Medical Record System.

Science.gov (United States)

Kasthurirathne, Suranga N; Mamlin, Burke; Grieve, Grahame; Biondich, Paul

2015-01-01

Interoperability is essential to address limitations caused by the ad hoc implementation of clinical information systems and the distributed nature of modern medical care. The HL7 V2 and V3 standards have played a significant role in ensuring interoperability for healthcare. FHIR is a next generation standard created to address fundamental limitations in HL7 V2 and V3. FHIR is particularly relevant to OpenMRS, an Open Source Medical Record System widely used across emerging economies. FHIR has the potential to allow OpenMRS to move away from a bespoke, application specific API to a standards based API. We describe efforts to design and implement a FHIR based API for the OpenMRS platform. Lessons learned from this effort were used to define long term plans to transition from the legacy OpenMRS API to a FHIR based API that greatly reduces the learning curve for developers and helps enhance adhernce to standards.

Scintillation counter with MRS APD light readout

International Nuclear Information System (INIS)

Akindinov, A.; Bondarenko, G.; Golovin, V.; Grigoriev, E.; Grishuk, Yu.; Mal'kevich, D.; Martemiyanov, A.; Ryabinin, M.; Smirnitskiy, A.; Voloshin, K.

2005-01-01

START, a high-efficiency and low-noise scintillation detector for ionizing particles, was developed for the purpose of creating a high-granular system for triggering cosmic muons. Scintillation light in START is detected by MRS APDs (Avalanche Photo-Diodes with Metal-Resistance-Semiconductor Structure), operated in the Geiger mode, which have 1mm 2 sensitive areas. START is assembled from a 15x15x1cm 3 scintillating plastic plate, two MRS APDs and two pieces of wavelength-shifting optical fiber stacked in circular coils inside the plastic. The front-end electronic card is mounted directly on the detector. Tests with START have confirmed its operational consistency, over 99% efficiency of MIP registration and good homogeneity. START demonstrates a low intrinsic noise of about 10 -2 Hz. If these detectors are to be mass-produced, the cost of a mosaic array of STARTs is estimated at a moderate level of 2-3kUSD/m 2

Classifying threats with a 14-MeV neutron interrogation system.

Science.gov (United States)

Strellis, Dan; Gozani, Tsahi

2005-01-01

SeaPODDS (Sea Portable Drug Detection System) is a non-intrusive tool for detecting concealed threats in hidden compartments of maritime vessels. This system consists of an electronic neutron generator, a gamma-ray detector, a data acquisition computer, and a laptop computer user-interface. Although initially developed to detect narcotics, recent algorithm developments have shown that the system is capable of correctly classifying a threat into one of four distinct categories: narcotic, explosive, chemical weapon, or radiological dispersion device (RDD). Detection of narcotics, explosives, and chemical weapons is based on gamma-ray signatures unique to the chemical elements. Elements are identified by their characteristic prompt gamma-rays induced by fast and thermal neutrons. Detection of RDD is accomplished by detecting gamma-rays emitted by common radioisotopes and nuclear reactor fission products. The algorithm phenomenology for classifying threats into the proper categories is presented here.

(1)H MRS assessment of hepatic steatosis in overweight children and adolescents

DEFF Research Database (Denmark)

Chabanova, Elizaveta; Bille, Dorthe S; Thisted, Ebbe

2012-01-01

magnetic resonance systems due to limited space. The purpose of this study was to examine the ability of open 1T system to monitor liver fat with proton MRS and to compare hepatic fat fractions (HFFs) obtained using an open 1T system with assessment with 3T proton MRS. METHODS: The study included 23...

Application of NUHOMS reg-sign to an integrated MRS/transportation system

International Nuclear Information System (INIS)

Rosa, J.M.; Lehnert, R.A.; Quinn, R.D.

1992-01-01

Storage of spent fuel at reactor sites in weld sealed canisters has been increasing steadily since 1989, with three facilities now having ISFSIs which employ the NUHOMS reg-sign technology. Expansion of existing Independent Spent Fuel Storage Installations (ISFSIs) and implementation of new ones will result in a substantial fraction of the at-reactor spent fuel inventory in dry storage canisters by 1998 and beyond. Since this same technology is readily applicable to the MRS, it is advantageous to transfer canisters directly from the ISFSI to the MRS for storage there without the need to reopen the canisters or rehandle the fuel. A store-only MRS offers the possibility of a simple, economical facility which can more easily be demonstrated to be temporary and environmentally benign. Transportation of an intact canister is feasible in a shipping cask of conventional design that is compatible with existing railcars and railroad interchange rules. The use of canisterized fuel also has advantages for eventual shipment and disposal at the federal repository

Classifier-Guided Sampling for Complex Energy System Optimization

Energy Technology Data Exchange (ETDEWEB)

Backlund, Peter B. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

2015-09-01

This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of o bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.

Evaluation of storage/transportation options to support criteria development for the Phase I MRS [Monitored Retrievable Storage

International Nuclear Information System (INIS)

Sorenson, K.B.; Brown, N.N.; Bennett, P.C.; Lake, W.

1991-01-01

The Department of Energy's (DOE) Office of Civilian Waste Management (OCRWM) plans to develop an interim storage facility to enable acceptance of spent fuel in 1998. It is estimated that this interim storage facility would be needed for about two years. A Monitored Retrievable Storage (MRS) facility is anticipated in 2000 and a repository in 2010. Acceptance and transport of spent fuel by DOE/OCRWM in 1998 will require an operating transportation system. Because this interim storage facility is not yet defined, development of an optimally compatible transportation system is not a certainty. In order to assure a transport capability for 1998 acceptance of spent fuel, it was decided that the OCRWM transportation program had to identify likely options for an interim storage facility, including identification of the components needed for compatibility between likely interim storage facility options and transportation. Primary attention was given to existing hardware, although conceptual designs were also considered. A systems-based probabilistic decision model was suggested by Sandia National Laboratories and accepted by DOE/OCRWM's transportation program. Performance of the evaluation task involved several elements of the transportation program. This paper describes the decision model developed to accomplish this task, along with some of the results and conclusions. 1 ref., 4 figs

Mrs. Squandertime

Science.gov (United States)

Anstey, Josephine; Pape, Dave

2013-03-01

In this paper we discuss Mrs. Squandertime, a real-time, persistent simulation of a virtual character, her living room, and the view from her window, designed to be a wall-size, projected art installation. Through her large picture window, the eponymous Mrs. Squandertime watches the sea: boats, clouds, gulls, the tide going in and out, people on the sea wall. The hundreds of images that compose the view are drawn from historical printed sources. The program that assembles and animates these images is driven by weather, time, and tide data constantly updated from a real physical location. The character herself is rendered photographically in a series of slowly dissolving stills which correspond to the character's current behavior.

MRS role in reducing technical uncertainties in geological disposal

International Nuclear Information System (INIS)

Ramspott, L.D.

1990-06-01

A high-level nuclear waste repository has inherent technical uncertainty due to its first-of-a-kind nature and the unprecedented time over which it must function. Three possible technical modifications to the currently planned US high-level nuclear waste system are reviewed in this paper. These modifications would be facilitated by inclusion of a monitored retrievable storage (MRS) in the system. The modifications are (1) an underground MRS at Yucca Mountain, (2) a phased repository, and (3) a ''cold'' repository. These modifications are intended to enhance scientific confidence that a repository system would function satisfactorily despite technical uncertainty. 12 refs

Feature and score fusion based multiple classifier selection for iris recognition.

Science.gov (United States)

Islam, Md Rabiul

2014-01-01

The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

Directory of Open Access Journals (Sweden)

Md. Rabiul Islam

2014-01-01

Full Text Available The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

Classifier models and architectures for EEG-based neonatal seizure detection

International Nuclear Information System (INIS)

Greene, B R; Marnane, W P; Lightbody, G; Reilly, R B; Boylan, G B

2008-01-01

Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with a poor long-term outcome. Early detection and treatment may improve prognosis. This paper aims to develop an optimal set of parameters and a comprehensive scheme for patient-independent multi-channel EEG-based neonatal seizure detection. We employed a dataset containing 411 neonatal seizures. The dataset consists of multi-channel EEG recordings with a mean duration of 14.8 h from 17 neonatal patients. Early-integration and late-integration classifier architectures were considered for the combination of information across EEG channels. Three classifier models based on linear discriminants, quadratic discriminants and regularized discriminants were employed. Furthermore, the effect of electrode montage was considered. The best performing seizure detection system was found to be an early integration configuration employing a regularized discriminant classifier model. A referential EEG montage was found to outperform the more standard bipolar electrode montage for automated neonatal seizure detection. A cross-fold validation estimate of the classifier performance for the best performing system yielded 81.03% of seizures correctly detected with a false detection rate of 3.82%. With post-processing, the false detection rate was reduced to 1.30% with 59.49% of seizures correctly detected. These results represent a comprehensive illustration that robust reliable patient-independent neonatal seizure detection is possible using multi-channel EEG

Confirmation of MRS/MPC transfer facility sizing using simulation modeling

International Nuclear Information System (INIS)

Houston, E.S.; Hadley, J.D.

1994-01-01

The Nuclear Waste Policy Act (NWPA) of 1982, as amended, requires the Department of Energy to begin receiving spent nuclear fuel (SNF) from utilities in January 1998. A repository will not be completed in time for the scheduled receipt of SNF. A Monitored Retrievable Storage (MRS) Facility is therefore a feasible solution to bridge the gap between the 1998 date for fuel acceptance and the startup of the repository. SNF will be stored temporarily at the MRS and later retrieved from storage and shipped to the repository. To simplify fuel handling and to standardize components, the multi-purpose canister (MPC) concept was investigated. The MPC would be a sealed, metallic canister containing multiple SNF assemblies in a dry inert environment. MPCs would be placed into different overpacks for transportation, storage, and disposal at the repository. The MRS transfer facility MPC and SNF throughput requirements, assumptions, and operating concepts were used to initially determine the size of the facility and the major equipment contained within the facility. This initial estimate was based on simplified calculation techniques. The adequacy of the design configurations were then confirmed using SLAM simulation modeling software. Modeling incorporates uncertainties in task durations, the effects of equipment reliability, availability of personnel and equipment, and system breakdowns. This paper describes how the model was developed and how it is used to verify the transfer facility size. It also illustrates how problems with the facility design, operational concepts, and staffing are identified with the results of the model

Criticality safety considerations. Integral Monitored Retrievable Storage (MRS) Facility

International Nuclear Information System (INIS)

1986-09-01

This report summarizes the criticality analysis performed to address criticality safety concerns and to support facility design during the conceptual design phase of the Monitored Retrievable Storage (MRS) Facility. The report addresses the criticality safety concerns, the design features of the facility relative to criticality, and the results of the analysis of both normal operating and hypothetical off-normal conditions. Key references are provided (Appendix C) if additional information is desired by the reader. The MRS Facility design was developed and the related analysis was performed in accordance with the MRS Facility Functional Design Criteria and the Basis for Design. The detailed description and calculations are documented in the Integral MRS Facility Conceptual Design Report. In addition to the summary portion of this report, explanatary notes for various terms, calculation methodology, and design parameters are presented in Appendix A. Appendix B provides a brief glossary of technical terms

Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation.

Science.gov (United States)

O'Reilly, Martin; Duffin, Joe; Ward, Tomas; Caulfield, Brian

2017-08-21

Biofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability to objectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts of IMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring technique deviations may not be well detected. One method of combatting these issues is through the development of personalized exercise technique classifiers. We aimed to create a tablet app for physiotherapists and personal trainers that would automate the development of personalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete a preliminary investigation of the accuracy of such individualized systems in a real-world evaluation. A tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizing video and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exercise professional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system, 15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD 9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated. The tablet app successfully automated the process of creating individualized exercise biofeedback systems. The personalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificity for assessing aberrant and acceptable technique with a single IMU positioned on the left thigh. A tablet app was developed that automates the process required to create a personalized exercise technique classification system. This tool can be applied to any cyclical, repetitive exercise. The

Studies on hippocampal sclerosis by 1H MRS and MRI

International Nuclear Information System (INIS)

Qi Jing; Du Xiangke; Luan Guoming; Wang Dehang

2000-01-01

Objective: To determine the relative utility of 1 H MRS and MRI for pre-surgical diagnosis of hippocampal sclerosis by the study on metabolic abnormalities and anatomical alterations in the brain of patients with temporal lobe epilepsy (TLE). Methods: 1 H MRS and MRI were performed on 8 patients with pathologically confirmed hippocampal sclerosis and 8 healthy volunteers on 2.0 T 1 H MRS/MRI system. The values of NAA, Cr and Cho were calculated by integration of their peaks and the ratios of NAA/Cr, NAA/(Cr + Cho), and Cho/Cr were measured. The volumes of both hippocampal formations in every case were observed and the differences of hippocampal formation (DHF) were analyzed. Results: The ratios of NAA/Cr, NAA/(Cr + Cho), and Cho/Cr in ipsilateral side were 0.55, 1.77 and 1.38, and in control subjects were 0.77, 1.38 and 1.06 separately. The ratios of NAA/Cr and NAA/(Cr + Cho) were decreased on ipsilateral side (t = 2.15, 4.83 separately, P 1 H MRS and MRI, seven of eight cases could be lateralized. Conclusion: 1 H MRS is sensitive to the diagnosis of neuron abnormality and coincident well with the pathological results 1 H MRS and MRI correctly lateralize most patients with hippocampal sclerosis and complement each other in final lateralization. The combination of 1 H MRS and MRI can provide useful information for pre-surgical diagnosis of hippocampal sclerosis

FACSIM/MRS [Monitored Retrievable Storage]-2: Storage and shipping model documentation and user's guide

International Nuclear Information System (INIS)

Huber, H.D.; Chockie, A.D.; Hostick, C.J.; Otis, P.T.; Sovers, R.A.

1987-06-01

The Pacific Northwest Laboratory (PNL) has developed a stochastic computer model, FACSIM/MRS, to assist in assessing the operational performance of the Monitored Retrievable Storage (MRS) waste-handling facility. This report provides the documentation and user's guide for FACSIM/MRS-2, which is also referred to as the back-end model. The FACSIM/MRS-2 model simulates the MRS storage and shipping operations, which include handling canistered spent fuel and secondary waste in the shielded canyon cells, in onsite yard storage, and in repository shipping cask loading areas

An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

Science.gov (United States)

He, Jian; Bai, Shuang; Wang, Xiaoyi

2017-06-16

Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.

Evaluation of the need, feasibility, and siting of the MRS in Tennessee

Energy Technology Data Exchange (ETDEWEB)

Colglazier, E.W. Jr. (Tennessee Univ., Knoxville, TN (United States))

1985-12-16

This summary report outlines the results of an independent assessment of the need, feasibility, and siting of the proposed Monitored Retrievable Storage (MRS) facility. The detailed reports of this assessment are included as appendices to the summary report. The Department of Energy (DOE) has concluded that the MRS is not absolutely necessary, but preferred, in order to manage and move spent fuel from reactors to a repository. The team has attemped to assess need'' by comparing to advantages and disadvantages of various systems, with and without the MRS. Feasibility has been assessed by comparing the technical and economic advantages and disadvantages. The team was not asked to recommend a preferred system. That choice will depend on the importance that are used to compare alternatives. The five key criteria selected by the team for comparing alternate systems were: economic cost, radiological risk, non-radiological transportation impacts, the likelihood of successful implementation and operation of the system, and the likelihood of meeting the schedule in the Nuclear Waste Policy Act. The team compared twelve different systems and modeled the transportation impacts and risks with three repository sites and two MRS sites.

Evaluating a k-nearest neighbours-based classifier for locating faulty areas in power systems

Directory of Open Access Journals (Sweden)

Juan José Mora Flórez

2008-09-01

Full Text Available This paper reports a strategy for identifying and locating faults in a power distribution system. The strategy was based on the K-nearest neighbours technique. This technique simply helps to estimate a distance from the features used for describing a particu-lar fault being classified to the faults presented during the training stage. If new data is presented to the proposed fault locator, it is classified according to the nearest example recovered. A characterisation of the voltage and current measurements obtained at one single line end is also presented in this document for assigning the area in the case of a fault in a power system. The pro-posed strategy was tested in a real power distribution system, average 93% confidence indexes being obtained which gives a good indicator of the proposal’s high performance. The results showed how a fault could be located by using features obtained from voltage and current, improving utility response and thereby improving system continuity indexes in power distribution sys-tems.

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha

2013-11-25

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Integral Monitored Retrievable Storage (MRS) Facility conceptual design report

International Nuclear Information System (INIS)

1985-09-01

This report presents a summary design description of the Conceptual Design for an Integral Monitored Retrievable Storage (MRS) Facility, as prepared by The Ralph M. Parsons Company under an A-E services contract with the Richland Operations Office of the Department of Energy. More detailed design requirements and design data are set forth in the Basis for Design and Design Report, bound under separate cover and available for reference by those desiring such information. The design data provided in this Design Report Executive Summary, the Basis for Design, and the Design Report include contributions by the Waste Technology Services Division of Westinghouse Electric Corporation (WEC), which was responsible for the development of the waste receiving, packaging, and storage systems, and Golder Associates Incorporated (GAI), which supported the design development with program studies. The MRS Facility design requirements, which formed the basis for the design effort, were prepared by Pacific Northwest Laboratory for the US Department of Energy, Richland Operations Office, in the form of a Functional Design Criteria (FDC) document, Rev. 4, August 1985. 9 figs., 6 tabs

Adaptation of a web-based, open source electronic medical record system platform to support a large study of tuberculosis epidemiology

Directory of Open Access Journals (Sweden)

Fraser Hamish SF

2012-11-01

Full Text Available Abstract Background In 2006, we were funded by the US National Institutes of Health to implement a study of tuberculosis epidemiology in Peru. The study required a secure information system to manage data from a target goal of 16,000 subjects who needed to be followed for at least one year. With previous experience in the development and deployment of web-based medical record systems for TB treatment in Peru, we chose to use the OpenMRS open source electronic medical record system platform to develop the study information system. Supported by a core technical and management team and a large and growing worldwide community, OpenMRS is now being used in more than 40 developing countries. We adapted the OpenMRS platform to better support foreign languages. We added a new module to support double data entry, linkage to an existing laboratory information system, automatic upload of GPS data from handheld devices, and better security and auditing of data changes. We added new reports for study managers, and developed data extraction tools for research staff and statisticians. Further adaptation to handle direct entry of laboratory data occurred after the study was launched. Results Data collection in the OpenMRS system began in September 2009. By August 2011 a total of 9,256 participants had been enrolled, 102,274 forms and 13,829 laboratory results had been entered, and there were 208 users. The system is now entirely supported by the Peruvian study staff and programmers. Conclusions The information system served the study objectives well despite requiring some significant adaptations mid-stream. OpenMRS has more tools and capabilities than it did in 2008, and requires less adaptations for future projects. OpenMRS can be an effective research data system in resource poor environments, especially for organizations using or considering it for clinical care as well as research.

Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit.

Science.gov (United States)

Simpson, Robin; Devenyi, Gabriel A; Jezzard, Peter; Hennessy, T Jay; Near, Jamie

2017-01-01

To introduce a new toolkit for simulation and processing of magnetic resonance spectroscopy (MRS) data, and to demonstrate some of its novel features. The FID appliance (FID-A) is an open-source, MATLAB-based software toolkit for simulation and processing of MRS data. The software is designed specifically for processing data with multiple dimensions (eg, multiple radiofrequency channels, averages, spectral editing dimensions). It is equipped with functions for importing data in the formats of most major MRI vendors (eg, Siemens, Philips, GE, Agilent) and for exporting data into the formats of several common processing software packages (eg, LCModel, jMRUI, Tarquin). This paper introduces the FID-A software toolkit and uses examples to demonstrate its novel features, namely 1) the use of a spectral registration algorithm to carry out useful processing routines automatically, 2) automatic detection and removal of motion-corrupted scans, and 3) the ability to perform several major aspects of the MRS computational workflow from a single piece of software. This latter feature is illustrated through both high-level processing of in vivo GABA-edited MEGA-PRESS MRS data, as well as detailed quantum mechanical simulations to generate an accurate LCModel basis set for analysis of the same data. All of the described processing steps resulted in a marked improvement in spectral quality compared with unprocessed data. Fitting of MEGA-PRESS data using a customized basis set resulted in improved fitting accuracy compared with a generic MEGA-PRESS basis set. The FID-A software toolkit enables high-level processing of MRS data and accurate simulation of in vivo MRS experiments. Magn Reson Med 77:23-33, 2017. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Visit of Mrs Gisela Kopper

CERN Multimedia

Jimenez, Jose Miguel

2014-01-01

Mrs Gisela Kopper Minister of Science, Technology and Telecommunications, Republic of Costa Rica; Mrs Roxana Tinoco, Minister Counsellor, Permanent Mission of Costa Rica to the United Nations; Mr Mario Vega, Minister Counsellor, Permanent Mission of Costa Rica to the United Nations; Dr Jose Miguel Jimenez, Technology Department Head; Prof. Emmanuel Tsesmelis, Deputy Head of International Relations

A support vector machine (SVM) based voltage stability classifier

Energy Technology Data Exchange (ETDEWEB)

Dosano, R.D.; Song, H. [Kunsan National Univ., Kunsan, Jeonbuk (Korea, Republic of); Lee, B. [Korea Univ., Seoul (Korea, Republic of)

2007-07-01

Power system stability has become even more complex and critical with the advent of deregulated energy markets and the growing desire to completely employ existing transmission and infrastructure. The economic pressure on electricity markets forces the operation of power systems and components to their limit of capacity and performance. System conditions can be more exposed to instability due to greater uncertainty in day to day system operations and increase in the number of potential components for system disturbances potentially resulting in voltage stability. This paper proposed a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. It described the procedure for fast classification of long-term voltage stability using the SVM algorithm. The application of the SVM based voltage stability classifier was presented with reference to the choice of input parameters; input data preconditioning; moving window for feature vector; determination of learning samples; and other considerations in SVM applications. The paper presented a case study with numerical examples of an 11-bus test system. The test results for the feasibility study demonstrated that the classifier could offer an excellent performance in classification with time-series measurements in terms of long-term voltage stability. 9 refs., 14 figs.

BIOPHARMACEUTICS CLASSIFICATION SYSTEM: A STRATEGIC TOOL FOR CLASSIFYING DRUG SUBSTANCES

OpenAIRE

Rohilla Seema; Rohilla Ankur; Marwaha RK; Nanda Arun

2011-01-01

The biopharmaceutical classification system (BCS) is a scientific approach for classifying drug substances based on their dose/solubility ratio and intestinal permeability. The BCS has been developed to allow prediction of in vivo pharmacokinetic performance of drug products from measurements of permeability and solubility. Moreover, the drugs can be categorized into four classes of BCS on the basis of permeability and solubility namely; high permeability high solubility, high permeability lo...

Mescalero Apache Tribe Monitored Retrievable Storage (MRS). Phase 1 feasibility study report

Energy Technology Data Exchange (ETDEWEB)

Peso, F.

1992-03-13

The Nuclear Waste Policy Act of 1982, as amended, authorizes the siting, construction and operation of a Monitored Retrievable Storage (MRS) facility. The MRS is intended to be used for the temporary storage of spent nuclear fuel from the nation`s nuclear power plants beginning as early as 1998. Pursuant to the Nuclear Waste Policy Act, the Office of the Nuclear Waste Negotiator was created. On October 7, 1991, the Nuclear Waste Negotiator invited the governors of states and the Presidents of Indian tribes to apply for government grants in order to conduct a study to assess under what conditions, if any, they might consider hosting an MRS facility. Pursuant to this invitation, on October 11, 1991 the Mescalero Apache Indian Tribe of Mescalero, NM applied for a grant to conduct a phased, preliminary study of the safety, technical, political, environmental, social and economic feasibility of hosting an MRS. The preliminary study included: (1) An investigative education process to facilitate the Tribe`s comprehensive understanding of the safety, environmental, technical, social, political, and economic aspects of hosting an MRS, and; (2) The development of an extensive program that is enabling the Tribe, in collaboration with the Negotiator, to reach an informed and carefully researched decision regarding the conditions, (if any), under which further pursuit of the MRS would be considered. The Phase 1 grant application enabled the Tribe to begin the initial activities necessary to determine whether further consideration is warranted for hosting the MRS facility. The Tribe intends to pursue continued study of the MRS in order to meet the following objectives: (1) Continuing the education process towards a comprehensive understanding of the safety, environmental, technical, social and economic aspects of the MRS; (2) Conducting an effective public participation and information program; (3) Participating in MRS meetings.

Monitoring of aquifer pump tests with Magnetic Resonance Sounding (MRS): a synthetic case study

DEFF Research Database (Denmark)

Herckenrath, Daan; Auken, E.; Bauer-Gottwein, Peter

2011-01-01

Magnetic Resonance Sounding (MRS) can provide valuable data to constrain and calibrate groundwater flow and transport models. With this non-invasive geophysical technique, measurements of water content and hydraulic conductivity can be obtained. We developed a hydrogeophyiscal forward method, which...... calculates the MRS-signal generated by an aquifer pump test. A synthetic MRS-dataset was subsequently used to determine the hydrogeological parameters in an inverse parameter estimation approach. This was done for a virtual pump test with a partially and a fully penetrating well. With the MRS data we were...

Generating prior probabilities for classifiers of brain tumours using belief networks

Directory of Open Access Journals (Sweden)

Arvanitis Theodoros N

2007-09-01

Full Text Available Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET, germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

Network Intrusion Detection System (NIDS in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier

Directory of Open Access Journals (Sweden)

Hafza A. Mahmood

2018-04-01

Full Text Available Cloud Environment is next generation internet based computing system that supplies customiza-ble services to the end user to work or access to the various cloud applications. In order to provide security and decrease the damage of information system, network and computer system it is im-portant to provide intrusion detection system (IDS. Now Cloud environment are under threads from network intrusions, as one of most prevalent and offensive means Denial of Service (DoS attacks that cause dangerous impact on cloud computing systems. This paper propose Hidden naïve Bayes (HNB Classifier to handle DoS attacks which is a data mining (DM model used to relaxes the conditional independence assumption of Naïve Bayes classifier (NB, proposed sys-tem used HNB Classifier supported with discretization and feature selection where select the best feature enhance the performance of the system and reduce consuming time. To evaluate the per-formance of proposal system, KDD 99 CUP and NSL KDD Datasets has been used. The experi-mental results show that the HNB classifier improves the performance of NIDS in terms of accu-racy and detecting DoS attacks, where the accuracy of detect DoS is 100% in three test KDD cup 99 dataset by used only 12 feature that selected by use gain ratio while in NSL KDD Dataset the accuracy of detect DoS attack is 90 % in three Experimental NSL KDD dataset by select 10 fea-ture only.

FACSIM/MRS (Monitored Retrievable Storage)-2: Storage and shipping model documentation and user's guide

Energy Technology Data Exchange (ETDEWEB)

Huber, H.D.; Chockie, A.D.; Hostick, C.J.; Otis, P.T.; Sovers, R.A.

1987-06-01

The Pacific Northwest Laboratory (PNL) has developed a stochastic computer model, FACSIM/MRS, to assist in assessing the operational performance of the Monitored Retrievable Storage (MRS) waste-handling facility. This report provides the documentation and user's guide for FACSIM/MRS-2, which is also referred to as the back-end model. The FACSIM/MRS-2 model simulates the MRS storage and shipping operations, which include handling canistered spent fuel and secondary waste in the shielded canyon cells, in onsite yard storage, and in repository shipping cask loading areas.

Detection of inflammatory bowel disease by proton magnetic resonance spectroscopy (1H MRS using an animal model

Directory of Open Access Journals (Sweden)

Dolenko Brion

2007-11-01

Full Text Available Abstract Background The aim of this study was to analyze the potential of proton magnetic resonance spectroscopy (1H MRS in diagnosing early inflammatory bowel disease (IBD. Methods Thirty male Sprague Dawley rats were fed 2% carrageenan in their diet for either 1 or 2 weeks. 1H MRS was performed ex-vivo on colonic mucosal samples (n = 123 and the spectra were analyzed by a multivariate method of analysis. The results of the multivariate analysis were correlated with histological analysis performed using H & E stain for the presence of inflammation in the samples from each group. Results Multivariate analysis classified the samples in their respective groups with an accuracy of 82%. Our region selection algorithm identified four regions in the spectra as being discriminatory. The metabolites assigned to these regions include creatine, phosphatidylcholine, the -CH2HC= group in fatty acyl chain, and the glycerol backbone of lipids. The differences in concentration of these metabolites in each group offer insight into the biochemical changes occurring during IBD and confer diagnostic potential to 1H MRS as a tool to study colonic inflammation in conjunction with biopsy. Conclusion 1H MRS is a sensitive tool to detect early colonic inflammation in an animal model of IBD.

MRS algorithm: a new method for searching myocardial region in SPECT myocardial perfusion images.

Science.gov (United States)

He, Yuan-Lie; Tian, Lian-Fang; Chen, Ping; Li, Bin; Mao, Zhong-Yuan

2005-10-01

First, the necessity of automatically segmenting myocardium from myocardial SPECT image is discussed in Section 1. To eliminate the influence of the background, the optimal threshold segmentation method modified for the MRS algorithm is explained in Section 2. Then, the image erosion structure is applied to identify the myocardium region and the liver region. The contour tracing method is introduced to extract the myocardial contour. To locate the centriod of the myocardium, the myocardial centriod searching method is developed. The protocol of the MRS algorithm is summarized in Section 6. The performance of the MRS algorithm is investigated and the conclusion is drawn in Section 7. Finally, the importance of the MRS algorithm and the improvement of the MRS algorithm are discussed.

A unified classifier for robust face recognition based on combining multiple subspace algorithms

Science.gov (United States)

Ijaz Bajwa, Usama; Ahmad Taj, Imtiaz; Waqas Anwar, Muhammad

2012-10-01

Face recognition being the fastest growing biometric technology has expanded manifold in the last few years. Various new algorithms and commercial systems have been proposed and developed. However, none of the proposed or developed algorithm is a complete solution because it may work very well on one set of images with say illumination changes but may not work properly on another set of image variations like expression variations. This study is motivated by the fact that any single classifier cannot claim to show generally better performance against all facial image variations. To overcome this shortcoming and achieve generality, combining several classifiers using various strategies has been studied extensively also incorporating the question of suitability of any classifier for this task. The study is based on the outcome of a comprehensive comparative analysis conducted on a combination of six subspace extraction algorithms and four distance metrics on three facial databases. The analysis leads to the selection of the most suitable classifiers which performs better on one task or the other. These classifiers are then combined together onto an ensemble classifier by two different strategies of weighted sum and re-ranking. The results of the ensemble classifier show that these strategies can be effectively used to construct a single classifier that can successfully handle varying facial image conditions of illumination, aging and facial expressions.

A distributed approach for optimizing cascaded classifier topologies in real-time stream mining systems.

Science.gov (United States)

Foo, Brian; van der Schaar, Mihaela

2010-11-01

In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.

Radiation dose-dependent change in brain 1H-MRS

International Nuclear Information System (INIS)

Matsushima, Shigeru; Muroka, Mamoru; Uchiyama, Yukio; Morita, Kozo; Nomoto, Yoshihito; Kinosada, Yasutomi.

1994-01-01

We have investigated the usefulness of 1 H-magnetic resonance spectroscopy ( 1 H-MRS) for the assessment of acute radiation damage of the human brain. Nineteen patients were treated with the whole brain irradiation. Biochemical changes in white matter were measured by in vivo 1 H-MRS. The measurement was performed 1 or 2 times in each case at radiation doses ranging from 0 to 44.4 Gy with conventional fractionation (2 Gy per fraction, once a day) or accelerated hyperfractionation (1.5 Gy per fraction, twice a day). For the measurement of 1 H-MRS, 1.5T whole body MR system was used and stimulated echo acquisition mode (STEAM) with chemical shift selective (CHESS) pulse was applied. Volume of the interest (VOI) was 2.5 x 2.5 x 2.5 cm 3 , and the repetition time and echo time were 2000 ms and 272 ms, respectively. The acute radiation damage of the brain was evaluated by the change of peak area ratio (PAR) of choline, creatine and N-acetylaspartate (NAA). 1 H-MR spectra obtained before irradiation were different from those observed during irradiation. There were statistically significant (p 1 H-MRS can be useful for assessment of acute radiation damage. (author)

Development of a computer writing system based on EOG

OpenAIRE

López, A.; Ferrero, F.; Yangüela, D.; Álvarez, C.; Postolache, O.

2017-01-01

WOS:000407517600044 (Nº de Acesso Web of Science) The development of a novel computer writing system based on eye movements is introduced herein. A system of these characteristics requires the consideration of three subsystems: (1) A hardware device for the acquisition and transmission of the signals generated by eye movement to the computer; (2) A software application that allows, among other functions, data processing in order to minimize noise and classify signals; and (3) A graphical i...

High-field proton MRS of human brain

Energy Technology Data Exchange (ETDEWEB)

Di Costanzo, Alfonso E-mail: alfonso.dicostanzo@unina2.it; Trojsi, F.; Tosetti, M.; Giannatempo, G.M.; Nemore, F.; Piccirillo, M.; Bonavita, S.; Tedeschi, G.; Scarabino, T

2003-11-01

Proton magnetic resonance spectroscopy ({sup 1}H-MRS) of the brain reveals specific biochemical information about cerebral metabolites, which may support clinical diagnoses and enhance the understanding of neurological disorders. The advantages of performing {sup 1}H-MRS at higher field strengths include better signal to noise ratio (SNR) and increased spectral, spatial and temporal resolution, allowing the acquisition of high quality, easily quantifiable spectra in acceptable imaging times. In addition to improved measurement precision of N-acetylaspartate, choline, creatine and myo-inositol, high-field systems allow the high-resolution measurement of other metabolites, such as glutamate, glutamine, {gamma}-aminobutyric acid, scyllo-inositol, aspartate, taurine, N-acetylaspartylglutamate, glucose and branched amino acids, thus extending the range of metabolic information. However, these advantages may be hampered by intrinsic field-dependent technical difficulties, such as decreased T2 signal, chemical shift dispersion errors, J-modulation anomalies, increased magnetic susceptibility, eddy current artifacts, limitations in the design of homogeneous and sensitive radiofrequency (RF) coils, magnetic field instability and safety issues. Several studies demonstrated that these limitations could be overcome, suggesting that the appropriate optimization of high-field {sup 1}H-MRS would expand the application in the fields of clinical research and diagnostic routine.

Safeguards and security design guidelines for conceptual monitored retrievable storage (MRS) facilities

International Nuclear Information System (INIS)

Byers, K.R.; Clark, R.G.; Harms, N.L.; Roberts, F.P.

1984-07-01

Existing safeguards/security regulations and licensing requirements that may be applicable to an MRS facility are not currently well-defined. Protection requirements consistent with the NRC-graded safeguards approach are identified, as a baseline safeguards system with a comparison of the impacts on safeguards and security of salient features of the different storage concepts. In addition, MRS facility design features and operational considerations are proposed that would enhance facility protection and provide additional assurance that protection systems and procedures would be effectively implemented. 3 figures

Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

Directory of Open Access Journals (Sweden)

Dawei Li

2017-01-01

Full Text Available This study develops a tree augmented naive Bayesian (TAN classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN based and multilayer feed forward (MLF neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

Evaluation of the need, feasibility, and siting of the MRS in Tennessee. Draft final report

Energy Technology Data Exchange (ETDEWEB)

Colglazier, E.W. Jr. [Tennessee Univ., Knoxville, TN (United States)

1985-12-16

This summary report outlines the results of an independent assessment of the need, feasibility, and siting of the proposed Monitored Retrievable Storage (MRS) facility. The detailed reports of this assessment are included as appendices to the summary report. The Department of Energy (DOE) has concluded that the MRS is not absolutely necessary, but preferred, in order to manage and move spent fuel from reactors to a repository. The team has attemped to assess ``need`` by comparing to advantages and disadvantages of various systems, with and without the MRS. ``Feasibility has been assessed by comparing the technical and economic advantages and disadvantages. The team was not asked to recommend a preferred system. That choice will depend on the importance that are used to compare alternatives. The five key criteria selected by the team for comparing alternate systems were: economic cost, radiological risk, non-radiological transportation impacts, the likelihood of successful implementation and operation of the system, and the likelihood of meeting the schedule in the Nuclear Waste Policy Act. The team compared twelve different systems and modeled the transportation impacts and risks with three repository sites and two MRS sites.

MRS on Indian lands? Congress shuffles feet

International Nuclear Information System (INIS)

Hudson, M.

1995-01-01

This article discusses the political, social, and economic aspects of siting the Monitored Retrieval Storage (MRS) facility. The first focus is on siting in American Indian lands, specifically in Mescalero, New Mexico, followed by a more general discussion of the problems surrounding MRS siting

MRS system study for the repository: Yucca Mountain Site Characterization Project

International Nuclear Information System (INIS)

Sinagra, T.A.; Harig, R.

1990-12-01

The US Department of Energy (DOE), Office of Civilian Radioactive Waste Management (OCRWM), has initiated a waste management system study to identify the impacts of the presence or absence of a monitored retrievable storage facility (hereinafter referred to as ''MRS'') on system costs and program schedules. To support this study, life-cycle cost estimates and construction schedules have been prepared for the surface and underground facilities and operations geologic nuclear waste repository at Yucca Mountain, Nye County, Nevada. Nine different operating scenarios (cases) have been identified by OCRWM for inclusion in this study. For each case, the following items are determined: the repository design and construction costs, operating costs, closure and decommissioning costs, required staffing, construction schedules, uncertainties associated with the costs and schedules, and shipping cask and disposal container throughputs. This document contains A-D

MRS system study for the repository: Yucca Mountain Site Characterization Project

International Nuclear Information System (INIS)

Sinagra, T.A.; Harig, R.

1990-12-01

The US Department of Energy (DOE), Office of Civilian Radioactive Waste Management (OCRWM), has initiated a waste management system study to identify the impacts of the presence or absence of a monitored retrievable storage facility (hereinafter referred to as ''MRS'') on system costs and program schedules. To support this study, life-cycle cost estimates and construction schedules have been prepared for the surface and underground facilities and operations of a geologic nuclear waste repository at Yucca Mountain, Nye County, Nevada. Nine different operating scenarios (cases) have been identified by OCRWM for inclusion in this study. For each case, the following items are determined: the repository design and construction costs, operating costs, closure and decommissioning costs, required staffing, construction schedules, uncertainties associated with the costs and schedules, and shipping cask and disposal container throughputs. 6 refs., 83 figs., 57 tabs

Silicon avalanche photodiodes on the base of metal-resistor-semiconductor (MRS) structures

CERN Document Server

Saveliev, V

2000-01-01

The development of a high quantum efficiency, fast photodetector, with internal gain amplification for the wavelength range 450-600 nm is one of the critical issues for experimental physics - registration of low-intensity light photons flux. The new structure of Silicon Avalanche Detectors with high internal amplification (10 sup 5 -10 sup 6) has been designed, manufactured and tested for registration of visible light photons and charge particles. The main features of Metal-Resistor-Semiconductor (MRS) structures are the high charge multiplication in nonuniform electric field near the 'needle' pn-junction and negative feedback for stabilization of avalanche process due to resistive layer.

Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis

International Nuclear Information System (INIS)

Li Qiang; Doi Kunio

2006-01-01

Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists detect various lesions in medical images. In CAD schemes, classifiers play a key role in achieving a high lesion detection rate and a low false-positive rate. Although many popular classifiers such as linear discriminant analysis and artificial neural networks have been employed in CAD schemes for reduction of false positives, a rule-based classifier has probably been the simplest and most frequently used one since the early days of development of various CAD schemes. However, with existing rule-based classifiers, there are major disadvantages that significantly reduce their practicality and credibility. The disadvantages include manual design, poor reproducibility, poor evaluation methods such as resubstitution, and a large overtraining effect. An automated rule-based classifier with a minimized overtraining effect can overcome or significantly reduce the extent of the above-mentioned disadvantages. In this study, we developed an 'optimal' method for the selection of cutoff thresholds and a fully automated rule-based classifier. Experimental results performed with Monte Carlo simulation and a real lung nodule CT data set demonstrated that the automated threshold selection method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the constructed rule-based classifier. We believe that this threshold selection method is very useful in the construction of automated rule-based classifiers with minimized overtraining effect

Proton MRS imaging in pediatric brain tumors

Energy Technology Data Exchange (ETDEWEB)

Zarifi, Maria [Aghia Sophia Children' s Hospital, Department of Radiology, Athens (Greece); Tzika, A.A. [Harvard Medical School, Department of Surgery, Massachusetts General Hospital, Boston, MA (United States); Shriners Burn Hospital, Boston, MA (United States)

2016-06-15

Magnetic resonance (MR) techniques offer a noninvasive, non-irradiating yet sensitive approach to diagnosing and monitoring pediatric brain tumors. Proton MR spectroscopy (MRS), as an adjunct to MRI, is being more widely applied to monitor the metabolic aspects of brain cancer. In vivo MRS biomarkers represent a promising advance and may influence treatment choice at both initial diagnosis and follow-up, given the inherent difficulties of sequential biopsies to monitor therapeutic response. When combined with anatomical or other types of imaging, MRS provides unique information regarding biochemistry in inoperable brain tumors and can complement neuropathological data, guide biopsies and enhance insight into therapeutic options. The combination of noninvasively acquired prognostic information and the high-resolution anatomical imaging provided by conventional MRI is expected to surpass molecular analysis and DNA microarray gene profiling, both of which, although promising, depend on invasive biopsy. This review focuses on recent data in the field of MRS in children with brain tumors. (orig.)

Generalization in the XCSF classifier system: analysis, improvement, and extension.

Science.gov (United States)

Lanzi, Pier Luca; Loiacono, Daniele; Wilson, Stewart W; Goldberg, David E

2007-01-01

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.

A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System.

Science.gov (United States)

Gichoya, Judy W; Kohli, Marc; Ivange, Larry; Schmidt, Teri S; Purkayastha, Saptarshi

2018-05-10

Open-source development can provide a platform for innovation by seeking feedback from community members as well as providing tools and infrastructure to test new standards. Vendors of proprietary systems may delay adoption of new standards until there are sufficient incentives such as legal mandates or financial incentives to encourage/mandate adoption. Moreover, open-source systems in healthcare have been widely adopted in low- and middle-income countries and can be used to bridge gaps that exist in global health radiology. Since 2011, the authors, along with a community of open-source contributors, have worked on developing an open-source radiology information system (RIS) across two communities-OpenMRS and LibreHealth. The main purpose of the RIS is to implement core radiology workflows, on which others can build and test new radiology standards. This work has resulted in three major releases of the system, with current architectural changes driven by changing technology, development of new standards in health and imaging informatics, and changing user needs. At their core, both these communities are focused on building general-purpose EHR systems, but based on user contributions from the fringes, we have been able to create an innovative system that has been used by hospitals and clinics in four different countries. We provide an overview of the history of the LibreHealth RIS, the architecture of the system, overview of standards integration, describe challenges of developing an open-source product, and future directions. Our goal is to attract more participation and involvement to further develop the LibreHealth RIS into an Enterprise Imaging System that can be used in other clinical imaging including pathology and dermatology.

MRS transfer facility feasibility study

International Nuclear Information System (INIS)

Jowdy, A.K.; Smith, R.I.

1990-12-01

Under contract to the US Department of Energy, Parsons was requested to evaluate the feasibility of building a simple hot cell (waste handling) transfer facility at the Monitored Retrievable Storage (MRS) site to facilitate acceptance of spent fuel into the Federal Waste Management System starting in early 1998. The Transfer Facility was intended to provide a receiving and transfer to storage capability at a relatively low throughput rate (approximately 500 MTU/yr) and to provide the recovery capability needed on the site in the event of a transport or storage cask seal failure during a period of about two years while the larger Spent Fuel Handling Building (SFHB) was being completed. Although the original study basis postulated an incremental addition to the larger, previously considered MRS configurations, study results show that the Transfer Facility may be capable of receiving and storing spent fuel at annual rates of 3000 MTU/yr or more, making a larger fuel handling structure unnecessary. In addition, the study analyses showed that the Transfer Facility could be constructed and put into service in 15--17 months and would cost less than the previous configurations. 2 figs., 2 tabs

Security Enrichment in Intrusion Detection System Using Classifier Ensemble

Directory of Open Access Journals (Sweden)

Uma R. Salunkhe

2017-01-01

Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

Data Stream Classification Based on the Gamma Classifier

Directory of Open Access Journals (Sweden)

Abril Valeria Uriarte-Arcia

2015-01-01

Full Text Available The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

Science.gov (United States)

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

A web-based non-intrusive ambient system to measure and classify activities of daily living.

Science.gov (United States)

Stucki, Reto A; Urwyler, Prabitha; Rampa, Luca; Müri, René; Mosimann, Urs P; Nef, Tobias

2014-07-21

The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. In this study, 10 healthy participants (6 women

Evaluation of acute radiation damage of the human brain by 1H-MRS

International Nuclear Information System (INIS)

Matsushima, Shigeru; Kinosada, Yasutomi.

1993-01-01

Fourteen patients (17 cases) were treated with the whole brain irradiation. Physiological changes in white matter were measured by in vivo 1 H magnetic resonance spectroscopy ( 1 H-MRS). Phantom examination proved the accuracy of our 1 H-MRS method to be valid. The measurement was performed 2 or 3 times in each case at the radiation doses ranging from 0 to 40 Gy with 2 Gy daily fractionation. For the measurement of 1 H-MRS, 1.5 T whole body MR system was used and stimulated echo acquisition mode (STEAM) with chemical shift selective (CHESS) pulse was applied. Volume of the interest (VOI) was 2.5x2.5x2.5 cm 3 , and the repetition time and echo time were 2000 ms and 272 ms, respectively. Acute radiation damage of the brain was evaluated by the change of peak area ratio (PAR) of choline, creatine and N-acetyl aspartate (NAA). 1 H-MRS spectra before irradiation were different from those observed during irradiation. There were statistically significant (p 1 H-MRS is a powerful modality, detecting the subtle physiological change which is difficult to evaluate with conventional images. (author)

Integral Monitored Retrievable Storage (MRS) Facility conceptual design report

International Nuclear Information System (INIS)

1985-09-01

In April 1985, the Department of Energy (DOE) selected the Clinch River site as its preferred site for the construction and operation of the monitored retrievable storage (MRS) facility (USDOE, 1985). In support of the DOE MRS conceptual design activity, available data describing the site have been gathered and analyzed. A composite geotechnical description of the Clinch River site has been developed and is presented herein. This report presents Clinch River site description data in the following sections: general site description, surface hydrologic characteristics, groundwater characteristics, geologic characteristics, vibratory ground motion, surface faulting, stability of subsurface materials, slope stability, and references. 48 refs., 35 figs., 6 tabs

Small serine recombination systems ParA-MRS and CinH-RS2 perform precise excision of plastid DNA

Science.gov (United States)

Selectable marker genes (SMGs) are necessary for selection of transgenic plants. However, once stable transformants have been identified, the marker gene is no longer needed. In this study, we demonstrate the use of the small serine recombination systems, ParA-MRS and CinH-RS2, to precisely excise ...

LOCALIZATION AND RECOGNITION OF DYNAMIC HAND GESTURES BASED ON HIERARCHY OF MANIFOLD CLASSIFIERS

OpenAIRE

M. Favorskaya; A. Nosov; A. Popov

2015-01-01

Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin dete...

Lineshape estimation for magnetic resonance spectroscopy (MRS) signals: self-deconvolution revisited

International Nuclear Information System (INIS)

Sima, D M; Garcia, M I Osorio; Poullet, J; Van Huffel, S; Suvichakorn, A; Antoine, J-P; Van Ormondt, D

2009-01-01

Magnetic resonance spectroscopy (MRS) is an effective diagnostic technique for monitoring biochemical changes in an organism. The lineshape of MRS signals can deviate from the theoretical Lorentzian lineshape due to inhomogeneities of the magnetic field applied to patients and to tissue heterogeneity. We call this deviation a distortion and study the self-deconvolution method for automatic estimation of the unknown lineshape distortion. The method is embedded within a time-domain metabolite quantitation algorithm for short-echo-time MRS signals. Monte Carlo simulations are used to analyze whether estimation of the unknown lineshape can improve the overall quantitation result. We use a signal with eight metabolic components inspired by typical MRS signals from healthy human brain and allocate special attention to the step of denoising and spike removal in the self-deconvolution technique. To this end, we compare several modeling techniques, based on complex damped exponentials, splines and wavelets. Our results show that self-deconvolution performs well, provided that some unavoidable hyper-parameters of the denoising methods are well chosen. Comparison of the first and last iterations shows an improvement when considering iterations instead of a single step of self-deconvolution

Speaker gender identification based on majority vote classifiers

Science.gov (United States)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2017-03-01

Speaker gender identification is considered among the most important tools in several multimedia applications namely in automatic speech recognition, interactive voice response systems and audio browsing systems. Gender identification systems performance is closely linked to the selected feature set and the employed classification model. Typical techniques are based on selecting the best performing classification method or searching optimum tuning of one classifier parameters through experimentation. In this paper, we consider a relevant and rich set of features involving pitch, MFCCs as well as other temporal and frequency-domain descriptors. Five classification models including decision tree, discriminant analysis, nave Bayes, support vector machine and k-nearest neighbor was experimented. The three best perming classifiers among the five ones will contribute by majority voting between their scores. Experimentations were performed on three different datasets spoken in three languages: English, German and Arabic in order to validate language independency of the proposed scheme. Results confirm that the presented system has reached a satisfying accuracy rate and promising classification performance thanks to the discriminating abilities and diversity of the used features combined with mid-level statistics.

Integral Monitored Retrievable Storage (MRS) Facility conceptual design report

International Nuclear Information System (INIS)

1985-09-01

This document, Volume 6 Book 1, contains information on design studies of a Monitored Retrievable Storage (MRS) facility. Topics include materials handling; processing; support systems; support utilities; spent fuel; high-level waste and alpha-bearing waste storage facilities; and field drywell storage

In vivo MRS metabolite quantification using genetic optimization

Science.gov (United States)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; van Ormondt, D.; Graveron-Demilly, D.

2011-11-01

The in vivo quantification of metabolites' concentrations, revealed in magnetic resonance spectroscopy (MRS) spectra, constitutes the main subject under investigation in this work. Significant contributions based on artificial intelligence tools, such as neural networks (NNs), with good results have been presented lately but have shown several drawbacks, regarding their quantification accuracy under difficult conditions. A general framework that encounters the quantification procedure as an optimization problem, which is solved using a genetic algorithm (GA), is proposed in this paper. Two different lineshape models are examined, while two GA configurations are applied on artificial data. Moreover, the introduced quantification technique deals with metabolite peaks' overlapping, a considerably difficult situation occurring under real conditions. Appropriate experiments have proved the efficiency of the introduced methodology, in artificial MRS data, by establishing it as a generic metabolite quantification procedure.

In vivo MRS metabolite quantification using genetic optimization

International Nuclear Information System (INIS)

Papakostas, G A; Mertzios, B G; Karras, D A; Van Ormondt, D; Graveron-Demilly, D

2011-01-01

The in vivo quantification of metabolites' concentrations, revealed in magnetic resonance spectroscopy (MRS) spectra, constitutes the main subject under investigation in this work. Significant contributions based on artificial intelligence tools, such as neural networks (NNs), with good results have been presented lately but have shown several drawbacks, regarding their quantification accuracy under difficult conditions. A general framework that encounters the quantification procedure as an optimization problem, which is solved using a genetic algorithm (GA), is proposed in this paper. Two different lineshape models are examined, while two GA configurations are applied on artificial data. Moreover, the introduced quantification technique deals with metabolite peaks' overlapping, a considerably difficult situation occurring under real conditions. Appropriate experiments have proved the efficiency of the introduced methodology, in artificial MRS data, by establishing it as a generic metabolite quantification procedure

Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

Directory of Open Access Journals (Sweden)

Bogdan Gliwa

2011-01-01

Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

Just-in-time adaptive classifiers-part II: designing the classifier.

Science.gov (United States)

Alippi, Cesare; Roveri, Manuel

2008-12-01

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

Literary aesthetics: beauty, the brain, and Mrs. Dalloway.

Science.gov (United States)

Hogan, Patrick Colm

2013-01-01

Empirical research indicates that beauty is in part a matter of prototype approximation. Some research suggests that unanticipated pattern recognition is important as well. This essay begins by briefly outlining an account of beauty based on these factors. It goes on to consider complications. Minor complications include the partial incompatibility of these accounts and the importance of differentiating judgments of beauty from aesthetic response. More serious issues include the relative neglect of literature in neurologically-based discussions of beauty, which tend to focus on music or visual art. There is also a relative neglect of emotion, beyond the reward system. Finally, there is the almost complete absence of the sublime. After considering these problems broadly, the essay turns to Virginia Woolf's Mrs. Dalloway, examining its treatment of beauty and sublimity. The aim of this section is not merely to illuminate Woolf's novel by reference to neuroscientific research. It is equally, perhaps more fully, to expand our neuroscientifically grounded account of aesthetic response by drawing on Woolf's novel. In Mrs. Dalloway, there are gestures toward prototypes and patterns in beauty. But the key features are clearly emotional. Specifically, the emotions at issue in feelings of beauty and sublimity appear to be primarily attachment, on the one hand, and a profound sense of isolation, on the other. Woolf's novel also points us toward other features of aesthetic experience, crucially including the emotion-sharing that is a key function of the production and circulation of art. © 2013 Elsevier B.V. All rights reserved.

ALARA for cask MRS by remote operations

International Nuclear Information System (INIS)

Wells, A.H.; Vick, D.E.

1985-01-01

Radiation dose rates in a monitored retrievable storage (MRS) facility are high enough to warrant the evaluation of robotic systems to achieve personnel dose reductions. Robots with sufficient mobility and dexterity to perform Health Physics surveys and maintenance are currently in use. The addition of artificial intelligence computer methods to the robot removes the need for a human operator for normal surveillance activities. Use of an Expert System creates a robot with sufficient flexibility to recognize and respond to off-normal conditions such as radiation leaks

PREFACE: Brazil MRS Meeting 2014

Science.gov (United States)

2015-11-01

The annual meetings, organized by the Brazilian materials research society - B-MRS, are amongst the most import discussion forums in the area of materials science and engineering in Brazil, with a growing interest from the national and international scientific society. In the last 4 years, more than 1,500 participants have attended the B-MRS meetings, promoting an auspicious environment for presentation and discussion of scientific and technological works in the materials science area. The XIII Brazilian Materials Research Society Meeting was held from 28 September to 02 October, 2014, in João Pessoa, PB, Brazil. The Meeting congregated more than 1650 participants from the whole of Brazil and from 28 other countries. More than 2100 abstracts were accepted for presentation, distributed along 19 Symposia following the format used in traditional meetings of Materials Research Societies. These involved topics such as: synthesis of new materials, computer simulations, optical, magnetic and electronic properties, traditional materials as clays and cements, advanced metals, carbon and graphene nanostructures, nanomaterials for nanostructures, energy storage systems, composites, surface engineering and others. A novelty was a symposium dedicated to innovation and technology transfer in materials research. The program also included 7 Plenary Lectures presented by internationally renowned researchers: Alberto Salleo from Stanford University, United States of America; Roberto Dovesi from Universita' degli Studi di Torino, Italy; Luís Antonio F. M. Dias Carlos from Universidade de Aveiro, Portugal; Jean Marie Dubois from Institut Jean-Lamour, France; Sir Colin Humphreys from University of Cambridge, England; Karl Leo from Technische Universität Dresden, Germany; Robert Chang from Northwestern University, Evanston, United States of America. The numbers of participants in the B-MRS meetings have been growing continuously, and in this meeting we had almost 2200 presentations

A mutation in the gene encoding mitochondrial Mg²+ channel MRS2 results in demyelination in the rat.

Directory of Open Access Journals (Sweden)

Takashi Kuramoto

2011-01-01

Full Text Available The rat demyelination (dmy mutation serves as a unique model system to investigate the maintenance of myelin, because it provokes severe myelin breakdown in the central nervous system (CNS after normal postnatal completion of myelination. Here, we report the molecular characterization of this mutation and discuss the possible pathomechanisms underlying demyelination. By positional cloning, we found that a G-to-A transition, 177 bp downstream of exon 3 of the Mrs2 (MRS2 magnesium homeostasis factor (Saccharomyces cerevisiae gene, generated a novel splice acceptor site which resulted in functional inactivation of the mutant allele. Transgenic rescue with wild-type Mrs2-cDNA validated our findings. Mrs2 encodes an essential component of the major Mg²+ influx system in mitochondria of yeast as well as human cells. We showed that the dmy/dmy rats have major mitochondrial deficits with a markedly elevated lactic acid concentration in the cerebrospinal fluid, a 60% reduction in ATP, and increased numbers of mitochondria in the swollen cytoplasm of oligodendrocytes. MRS2-GFP recombinant BAC transgenic rats showed that MRS2 was dominantly expressed in neurons rather than oligodendrocytes and was ultrastructurally observed in the inner membrane of mitochondria. Our observations led to the conclusion that dmy/dmy rats suffer from a mitochondrial disease and that the maintenance of myelin has a different mechanism from its initial production. They also established that Mg²+ homeostasis in CNS mitochondria is essential for the maintenance of myelin.

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

Science.gov (United States)

Li, Qiang; Gu, Yu; Jia, Jing

2017-01-30

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

Directory of Open Access Journals (Sweden)

Qiang Li

2017-01-01

Full Text Available Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS and support vector machine (SVM algorithms in a quartz crystal microbalance (QCM-based electronic nose (e-nose we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3% showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN classifier (93.3% and moving average-linear discriminant analysis (MA-LDA classifier (87.6%. The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

Virk: An Active Learning-based System for Bootstrapping Knowledge Base Development in the Neurosciences

Directory of Open Access Journals (Sweden)

Kyle H. Ambert

2013-12-01

Full Text Available The frequency and volume of newly-published scientific literature is quickly making manual maintenance of publicly-available databases of primary data unrealistic and costly. Although machine learning can be useful for developing automated approaches to identifying scientific publications containing relevant information for a database, developing such tools necessitates manually annotating an unrealistic number of documents. One approach to this problem, active learning, builds classification models by iteratively identifying documents that provide the most information to a classifier. Although this approach has been shown to be effective for related problems, in the context of scientific databases curation, it falls short. We present Virk, an active learning system that, while being trained, simultaneously learns a classification model and identifies documents having information of interest for a knowledge base. Our approach uses a support vector machine classifier with input features derived from neuroscience-related publications from the primary literature. Using our approach, we were able to increase the size of the Neuron Registry, a knowledge base of neuron-related information, by a factor of 90%, a knowledge base of neuron-related information, in 3 months. Using standard biocuration methods, it would have taken between 1-2 years to make the same number of contributions to the Neuron Registry. Here, we describe the system pipeline in detail, and evaluate its performance against other approaches to sampling in active learning.

Hybrid classifiers methods of data, knowledge, and classifier combination

CERN Document Server

Wozniak, Michal

2014-01-01

This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software

Directory of Open Access Journals (Sweden)

Manh Cong Tran

2016-01-01

Full Text Available HTTP is recognized as the most widely used protocol on the Internet when applications are being transferred more and more by developers onto the web. Due to increasingly complex computer systems, diversity HTTP automated software (autoware thrives. Unfortunately, besides normal autoware, HTTP malware and greyware are also spreading rapidly in web environment. Consequently, network communication is not just rigorously controlled by users intention. This raises the demand for analyzing HTTP autoware communication behaviour to detect and classify malicious and normal activities via HTTP traffic. Hence, in this paper, based on many studies and analysis of the autoware communication behaviour through access graph, a new method to detect and classify HTTP autoware communication at network level is presented. The proposal system includes combination of MapReduce of Hadoop and MarkLogic NoSQL database along with xQuery to deal with huge HTTP traffic generated each day in a large network. The method is examined with real outbound HTTP traffic data collected through a proxy server of a private network. Experimental results obtained for proposed method showed that promised outcomes are achieved since 95.1% of suspicious autoware are classified and detected. This finding may assist network and system administrator in inspecting early the internal threats caused by HTTP autoware.

Detecting Dutch political tweets : A classifier based on voting system using supervised learning

NARCIS (Netherlands)

de Mello Araújo, Eric Fernandes; Ebbelaar, Dave

The task of classifying political tweets has been shown to be very difficult, with controversial results in many works and with non-replicable methods. Most of the works with this goal use rule-based methods to identify political tweets. We propose here two methods, being one rule-based approach,

On an efficient modification of singular value decomposition using independent component analysis for improved MRS denoising and quantification

International Nuclear Information System (INIS)

Stamatopoulos, V G; Karras, D A; Mertzios, B G

2009-01-01

An efficient modification of singular value decomposition (SVD) is proposed in this paper aiming at denoising and more importantly at quantifying more accurately the statistically independent spectra of metabolite sources in magnetic resonance spectroscopy (MRS). Although SVD is known in MRS applications and several efficient algorithms exist for estimating SVD summation terms in which the raw MRS data are analyzed, however, it would be more beneficial for such an analysis if techniques with the ability to estimate statistically independent spectra could be employed. SVD is known to separate signal and noise subspaces but it assumes orthogonal properties for the components comprising signal subspace, which is not always the case, and might impose heavy constraints for the MRS case. A much more relaxing constraint would be to assume statistically independent components. Therefore, a modification of the main methodology incorporating techniques for calculating the assumed statistically independent spectra is proposed by applying SVD on the MRS spectrogram through application of the short time Fourier transform (STFT). This approach is based on combining SVD on STFT spectrogram followed by an iterative application of independent component analysis (ICA). Moreover, it is shown that the proposed methodology combined with a regression analysis would lead to improved quantification of the MRS signals. An experimental study based on synthetic MRS signals has been conducted to evaluate the herein proposed methodologies. The results obtained have been discussed and it is shown to be quite promising

Quality assessment in in vivo NMR spectroscopy: V. Multicentre evaluation of prototype test objects and protocols for performance assessment in small bore MRS equipment

DEFF Research Database (Denmark)

Howe, F.A.; Canese, R; Podo, F

1995-01-01

This paper reports the results of multicentre studies aimed at designing, constructing, and evaluating prototype test objects for performance assessment in small-bore MRS systems, by utilizing the test protocols already proposed by the EEC COMAC-BME Concerted Action for clinical MRS equipment...... using ISIS as volume localization sequence in 31P MRS. The results suggested the interest of adopting some of these prototypes for improving the comparison of spectroscopy data obtained from different sites, for providing useful means of quality assurance in experimental MRS, and facilitating....... Three classes of test objects were considered: (1) a multicompartment test object for 31P MRS measurements performed with slice-selective sequences; (2) a two-compartment test object for volume-selection 1H MRS; and (3) two-compartment test objects for assessing the performance of experimental systems...

How large a training set is needed to develop a classifier for microarray data?

Science.gov (United States)

Dobbin, Kevin K; Zhao, Yingdong; Simon, Richard M

2008-01-01

A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. We present a model-based approach to determining the sample size required to adequately train a classifier. It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

Directory of Open Access Journals (Sweden)

Yanzhen Zhou

2016-09-01

Full Text Available Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems.

A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING

Directory of Open Access Journals (Sweden)

JORGE FLORES CRUZ

2017-01-01

Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.

Monitoring of aquifer pump tests with Magnetic Resonance Sounding (MRS)

DEFF Research Database (Denmark)

Herckenrath, Daan; Auken, Esben; Bauer-Gottwein, Peter

2009-01-01

Magnetic Resonance Sounding (MRS) can provide valuable data to constrain and calibrate groundwater flow and transport models. With this non-invasive geophysical technique, field measurements of water content and hydraulic conductivities can be obtained. We developed a hydrogeophyiscal forward...

Quality management in in vivo proton MRS.

Science.gov (United States)

Pedrosa de Barros, Nuno; Slotboom, Johannes

2017-07-15

The quality of MR-Spectroscopy data can easily be affected in in vivo applications. Several factors may produce signal artefacts, and often these are not easily detected, not even by experienced spectroscopists. Reliable and reproducible in vivo MRS-data requires the definition of quality requirements and goals, implementation of measures to guarantee quality standards, regular control of data quality, and a continuous search for quality improvement. The first part of this review includes a general introduction to different aspects of quality management in MRS. It is followed by the description of a series of tests and phantoms that can be used to assure the quality of the MR system. In the third part, several methods and strategies used for quality control of the spectroscopy data are presented. This review concludes with a reference to a few interesting techniques and aspects that may help to further improve the quality of in vivo MR-spectra. Copyright © 2017 Elsevier Inc. All rights reserved.

Case base classification on digital mammograms: improving the performance of case base classifier

Science.gov (United States)

Raman, Valliappan; Then, H. H.; Sumari, Putra; Venkatesa Mohan, N.

2011-10-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. In this paper we build a case-based Classifier in order to diagnose mammographic images. We explain different methods and behaviors that have been added to the classifier to improve the performance of the classifier. Currently the initial Performance base Classifier with Bagging is proposed in the paper and it's been implemented and it shows an improvement in specificity and sensitivity.

A Linguistic Image of Nature: The Burmese Numerative Classifier System

Science.gov (United States)

Becker, Alton L.

1975-01-01

The Burmese classifier system is coherent because it is based upon a single elementary semantic dimension: deixis. On that dimension, four distances are distinguished, distances which metaphorically substitute for other conceptual relations between people and other living beings, people and things, and people and concepts. (Author/RM)

CT, MRI and MRS of Epstein-Barr virus infection: case report

Energy Technology Data Exchange (ETDEWEB)

Cecil, K.M.; Jones, B.V.; Hedlund, G.L. [Dept. of Radiology, Children' s Hospital Medical Center, Cincinnati, OH (United States); Williams, S. [Dept. of Neurology, Children' s Hospital Medical Center, Cincinnati, OH (United States)

2000-08-01

We report MRI and proton MR spectroscopy (MRS) findings in a 12-month-old girl with Epstein-Barr virus encephalitis. CT and MRI showed focal lesions in the basal ganglia. MRS of the lesions showed decreased N-acetyl aspartate and elevation of some amino acids, indicating an infectious rather than ischemic etiology. This case illustrates the use of MRS to narrow differential diagnosis. (orig.)

CT, MRI and MRS of Epstein-Barr virus infection: case report

International Nuclear Information System (INIS)

Cecil, K.M.; Jones, B.V.; Hedlund, G.L.; Williams, S.

2000-01-01

We report MRI and proton MR spectroscopy (MRS) findings in a 12-month-old girl with Epstein-Barr virus encephalitis. CT and MRI showed focal lesions in the basal ganglia. MRS of the lesions showed decreased N-acetyl aspartate and elevation of some amino acids, indicating an infectious rather than ischemic etiology. This case illustrates the use of MRS to narrow differential diagnosis. (orig.)

(1) H-MRS processing parameters affect metabolite quantification

DEFF Research Database (Denmark)

Bhogal, Alex A; Schür, Remmelt R; Houtepen, Lotte C

2017-01-01

investigated the influence of model parameters and spectral quantification software on fitted metabolite concentration values. Sixty spectra in 30 individuals (repeated measures) were acquired using a 7-T MRI scanner. Data were processed by four independent research groups with the freedom to choose their own...... + NAAG/Cr + PCr and Glu/Cr + PCr, respectively. Metabolite quantification using identical (1) H-MRS data was influenced by processing parameters, basis sets and software choice. Locally preferred processing choices affected metabolite quantification, even when using identical software. Our results......Proton magnetic resonance spectroscopy ((1) H-MRS) can be used to quantify in vivo metabolite levels, such as lactate, γ-aminobutyric acid (GABA) and glutamate (Glu). However, there are considerable analysis choices which can alter the accuracy or precision of (1) H-MRS metabolite quantification...

A method of distributed avionics data processing based on SVM classifier

Science.gov (United States)

Guo, Hangyu; Wang, Jinyan; Kang, Minyang; Xu, Guojing

2018-03-01

Under the environment of system combat, in order to solve the problem on management and analysis of the massive heterogeneous data on multi-platform avionics system, this paper proposes a management solution which called avionics "resource cloud" based on big data technology, and designs an aided decision classifier based on SVM algorithm. We design an experiment with STK simulation, the result shows that this method has a high accuracy and a broad application prospect.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

Science.gov (United States)

Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.

2014-03-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

International Nuclear Information System (INIS)

Blanco, A; Rodriguez, R; Martinez-Maranon, I

2014-01-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity

An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost

Directory of Open Access Journals (Sweden)

Yinan Zhang

2016-01-01

Full Text Available Diabetic retinopathy (DR screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%–35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and K Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.

Textual and shape-based feature extraction and neuro-fuzzy classifier for nuclear track recognition

Science.gov (United States)

Khayat, Omid; Afarideh, Hossein

2013-04-01

Track counting algorithms as one of the fundamental principles of nuclear science have been emphasized in the recent years. Accurate measurement of nuclear tracks on solid-state nuclear track detectors is the aim of track counting systems. Commonly track counting systems comprise a hardware system for the task of imaging and software for analysing the track images. In this paper, a track recognition algorithm based on 12 defined textual and shape-based features and a neuro-fuzzy classifier is proposed. Features are defined so as to discern the tracks from the background and small objects. Then, according to the defined features, tracks are detected using a trained neuro-fuzzy system. Features and the classifier are finally validated via 100 Alpha track images and 40 training samples. It is shown that principle textual and shape-based features concomitantly yield a high rate of track detection compared with the single-feature based methods.

Gender and classifiers in concurrent systems: Refining the typology of nominal classification

Directory of Open Access Journals (Sweden)

Sebastian Fedden

2017-04-01

Full Text Available Some languages have both gender and classifiers, contrary to what was once believed possible. We use these interesting languages as a unique window onto nominal classification. They provide the impetus for a new typology, based on the degree of orthogonality of the semantic systems and the degree of difference of the forms realizing them. This nine-way typology integrates traditional gender, traditional classifiers and – importantly – the many recently attested phenomena lying between. Besides progress specifically in understanding nominal classification, our approach provides clarity on the wider theoretical issue of single versus concurrent featural systems.

A multicenter reproducibility study of single-voxel 1H-MRS of the medial temporal lobe

International Nuclear Information System (INIS)

Traeber, Frank; Block, Wolfgang; Guer, Okan; Schild, Hans H.; Freymann, Nikolaus; Heun, Reinhard; Jessen, Frank; Kucinski, Thomas; Hammen, Thilo; Ende, Gabriele; Pilatus, Ulrich; Hampel, Harald

2006-01-01

Proton magnetic resonance spectroscopy ( 1 H-MRS) has provided evidence for a reduction of N-acetyl-aspartate (NAA) in the medial temporal lobe (MTL) in cerebral disorders such as Alzheimer's Disease. Within the 1 H-MRS study of the German Research Network on Dementia, we determined the multicenter reproducibility of single-voxel 1 H-MRS of the MTL. At five sites with 1.5T MR systems, single-voxel 1 H spectra from the MTL of an identical healthy subject were measured. The same subject was also examined at one of the sites five times to assess intracenter stability. The protocol included water-suppressed spectra with TE 272 ms and TE 30 ms and unsuppressed spectra for absolute quantification of metabolite concentrations. The intracenter reproducibility of absolute NAA concentration, expressed as coefficient of variation (CV), was 1.8%. CV for the concentrations of creatine (Cr), choline (Cho), and myoinositol (MI) and for the ratios NAA/Cr, NAA/Cho, and MI/NAA varied by 11-16%. Intercenter CV was 3.9% for NAA and were below 10% for all other metabolites and metabolic ratios. Our study demonstrates that quantitative assessment of NAA with single-voxel MRS can be performed with high intercenter reproducibility. This is the basis for applying 1 H-MRS in large-scale early recognition and treatment studies in MTL affecting disorders. (orig.)

Monte Carlo Modeling for in vivo MRS : Generating and quantifying simulations via the Windows, Linux and Android platform

NARCIS (Netherlands)

De Beer, R.; Van Ormondt, D.

2014-01-01

Work in context of European Union TRANSACT project. We have developed a Java/JNI/C/Fortran based software application, called MonteCarlo, with which the users can carry out Monte Carlo studies in the field of \\emph{in vivo} MRS. The application is supposed to be used as a tool for supporting the

mrsFAST-Ultra: a compact, SNP-aware mapper for high performance sequencing applications.

Science.gov (United States)

Hach, Faraz; Sarrafi, Iman; Hormozdiari, Farhad; Alkan, Can; Eichler, Evan E; Sahinalp, S Cenk

2014-07-01

High throughput sequencing (HTS) platforms generate unprecedented amounts of data that introduce challenges for processing and downstream analysis. While tools that report the 'best' mapping location of each read provide a fast way to process HTS data, they are not suitable for many types of downstream analysis such as structural variation detection, where it is important to report multiple mapping loci for each read. For this purpose we introduce mrsFAST-Ultra, a fast, cache oblivious, SNP-aware aligner that can handle the multi-mapping of HTS reads very efficiently. mrsFAST-Ultra improves mrsFAST, our first cache oblivious read aligner capable of handling multi-mapping reads, through new and compact index structures that reduce not only the overall memory usage but also the number of CPU operations per alignment. In fact the size of the index generated by mrsFAST-Ultra is 10 times smaller than that of mrsFAST. As importantly, mrsFAST-Ultra introduces new features such as being able to (i) obtain the best mapping loci for each read, and (ii) return all reads that have at most n mapping loci (within an error threshold), together with these loci, for any user specified n. Furthermore, mrsFAST-Ultra is SNP-aware, i.e. it can map reads to reference genome while discounting the mismatches that occur at common SNP locations provided by db-SNP; this significantly increases the number of reads that can be mapped to the reference genome. Notice that all of the above features are implemented within the index structure and are not simple post-processing steps and thus are performed highly efficiently. Finally, mrsFAST-Ultra utilizes multiple available cores and processors and can be tuned for various memory settings. Our results show that mrsFAST-Ultra is roughly five times faster than its predecessor mrsFAST. In comparison to newly enhanced popular tools such as Bowtie2, it is more sensitive (it can report 10 times or more mappings per read) and much faster (six times or

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

Science.gov (United States)

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

Science.gov (United States)

Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

2014-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

Intuitive Action Set Formation in Learning Classifier Systems with Memory Registers

NARCIS (Netherlands)

Simões, L.F.; Schut, M.C.; Haasdijk, E.W.

2008-01-01

An important design goal in Learning Classifier Systems (LCS) is to equally reinforce those classifiers which cause the level of reward supplied by the environment. In this paper, we propose a new method for action set formation in LCS. When applied to a Zeroth Level Classifier System with Memory

Classifying Microorganisms

DEFF Research Database (Denmark)

Sommerlund, Julie

2006-01-01

This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... characteristics. The coexistence of the classification systems does not lead to a conflict between them. Rather, the systems seem to co-exist in different configurations, through which they are complementary, contradictory and inclusive in different situations-sometimes simultaneously. The systems come...

Stress fracture development classified by bone scintigraphy

International Nuclear Information System (INIS)

Zwas, S.T.; Elkanovich, R.; Frank, G.; Aharonson, Z.

1985-01-01

There is no consensus on classifying stress fractures (SF) appearing on bone scans. The authors present a system of classification based on grading the severity and development of bone lesions by visual inspection, according to three main scintigraphic criteria: focality and size, intensity of uptake compare to adjacent bone, and local medular extension. Four grades of development (I-IV) were ranked, ranging from ill defined slightly increased cortical uptake to well defined regions with markedly increased uptake extending transversely bicortically. 310 male subjects aged 19-2, suffering several weeks from leg pains occurring during intensive physical training underwent bone scans of the pelvis and lower extremities using Tc-99-m-MDP. 76% of the scans were positive with 354 lesions, of which 88% were in th4e mild (I-II) grades and 12% in the moderate (III) and severe (IV) grades. Post-treatment scans were obtained in 65 cases having 78 lesions during 1- to 6-month intervals. Complete resolution was found after 1-2 months in 36% of the mild lesions but in only 12% of the moderate and severe ones, and after 3-6 months in 55% of the mild lesions and 15% of the severe ones. 75% of the moderate and severe lesions showed residual uptake in various stages throughout the follow-up period. Early recognition and treatment of mild SF lesions in this study prevented protracted disability and progression of the lesions and facilitated complete healing

The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

Directory of Open Access Journals (Sweden)

Mercadal Guillem

2010-11-01

Full Text Available Abstract Background Proton Magnetic Resonance (MR Spectroscopy (MRS is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS in MRS data analysis has been explored. Results This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with any new case from any MR scanner manufacturer and 2nd, improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI. Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA were trained and tested. Conclusions The INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses.

SVM classifier on chip for melanoma detection.

Science.gov (United States)

Afifi, Shereen; GholamHosseini, Hamid; Sinha, Roopak

2017-07-01

Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.

MRI and MRS and outcome in infants with hypoxic ischaemic cerebral injury

International Nuclear Information System (INIS)

Saddick, D.; Charlton, M.; Carse, E.; Barfield, C.; Coleman, L.; Goergen, S.

2002-01-01

Full text: To audit clinical outcome at 18 to 30 months in infants with hypoxic ischaemic encephalopathy (HIE) and who had MRI and proton MR spectroscopy (MRS) in infancy. 7 infants diagnosed with HIE at birth were examined prior to day 10 of life (mean = 4.5 days) with cranial MRI and MRS. MRS consisted of a single voxel placed over the basal ganglia and a STEAM (TE = 20ms) or PRESS (TE = 270ms) technique. A TE = 135 was used if a lactate doublet was identified at 1.3ppm. T1, T2, PD, FLAIR and diffusion weighted (DW) MR images were scored independently by two radiologists blinded to outcome. Metabolite peak areas were calculated for lactate, NAA, and creatine and a qualitative assessment of glutamine/ glutamate elevation was made. Of the 7 children, one died on day 6 and the others were invited to participate in neurodevelopmental assessment between 18 and 30 months of age. The MDI and PDI of the Bayley scales of infant development were used to assess intellectual and fine and gross motor development respectively and 3 children have attended the clinic thus far. A neurologist performed a standard neurological examination and graded the result on a six-point scale. The two children who had a very poor outcome on the MDI, PDI and/or neurological assessment and the infant died all had Lac: NAA greater than 1.0 or NAA: Cr less than 0.7 and extensive white matter injury. One child had normal MRS and MRI but has not yet presented for assessment at 18 months. The DW images were abnormal in only one child. The findings of marked lactate elevation and extensive white matter injury correlated with poor prognosis in our small group of patients. Diffusion weighted images were frequently normal. Copyright (2002) Blackwell Science Pty Ltd

Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

Energy Technology Data Exchange (ETDEWEB)

Islam, Md. Shafiqul, E-mail: shafique@eng.ukm.my [Dept. of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangore (Malaysia); Hannan, M.A., E-mail: hannan@eng.ukm.my [Dept. of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangore (Malaysia); Basri, Hassan [Dept. of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangore (Malaysia); Hussain, Aini; Arebey, Maher [Dept. of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangore (Malaysia)

2014-02-15

Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.

A literature-based preliminary characterization of risks in the nuclear waste management system

International Nuclear Information System (INIS)

Daling, P.M.; Rhoads, R.E.; Van Luik, A.E.

1990-04-01

The objectives of this study were to (1) review the literature containing information on risks in the nuclear waste management system and (2) use this information to develop preliminary estimates of the potential magnitudes of these risks. Information was collected on a broad range of risk categories to assist the US Department of Energy (DOE) in communicating information about the risks in the waste management system. The study, which was completed prior to passage of the Nuclear Waste Policy Amendments Act of 1987, examined all of the portions of the nuclear waste management system envisioned by the DOE in the 1985 ''Mission Plant for the Civilian Radioactive Waste Management Program.'' As such, there may be statements in this paper that are not consistent with current DOE positions. The scope of this paper includes the repository, the integral Monitored Retrievable Storage (MRS) facility, and the transportation system that supports the repository and the MRS facility. Based on the results of this analysis, it is concluded that the radiological risks in the waste management system are small relative to nonradiological risks and relative to the risks of exposure to natural background radiation. 6 refs., 2 figs., 2 tabs

A multicenter reproducibility study of single-voxel {sup 1}H-MRS of the medial temporal lobe

Energy Technology Data Exchange (ETDEWEB)

Traeber, Frank; Block, Wolfgang; Guer, Okan; Schild, Hans H. [University of Bonn, Department of Radiology, Bonn (Germany); Freymann, Nikolaus; Heun, Reinhard; Jessen, Frank [University of Bonn, Department of Psychiatry, Bonn (Germany); Kucinski, Thomas [University of Hamburg, Department of Neuroradiology, Hamburg (Germany); Hammen, Thilo [University of Erlangen, Department of Psychiatry, Erlangen (Germany); Ende, Gabriele [Central Institute of Mental Health, NMR Research in Psychiatry, Mannheim (Germany); Pilatus, Ulrich [University of Frankfurt, Department of Neuroradiology, Frankfurt (Germany); Hampel, Harald [University of Munich, Department of Psychiatry, Munich (Germany)

2006-05-15

Proton magnetic resonance spectroscopy ({sup 1}H-MRS) has provided evidence for a reduction of N-acetyl-aspartate (NAA) in the medial temporal lobe (MTL) in cerebral disorders such as Alzheimer's Disease. Within the {sup 1}H-MRS study of the German Research Network on Dementia, we determined the multicenter reproducibility of single-voxel {sup 1}H-MRS of the MTL. At five sites with 1.5T MR systems, single-voxel {sup 1}H spectra from the MTL of an identical healthy subject were measured. The same subject was also examined at one of the sites five times to assess intracenter stability. The protocol included water-suppressed spectra with TE 272 ms and TE 30 ms and unsuppressed spectra for absolute quantification of metabolite concentrations. The intracenter reproducibility of absolute NAA concentration, expressed as coefficient of variation (CV), was 1.8%. CV for the concentrations of creatine (Cr), choline (Cho), and myoinositol (MI) and for the ratios NAA/Cr, NAA/Cho, and MI/NAA varied by 11-16%. Intercenter CV was 3.9% for NAA and were below 10% for all other metabolites and metabolic ratios. Our study demonstrates that quantitative assessment of NAA with single-voxel MRS can be performed with high intercenter reproducibility. This is the basis for applying {sup 1}H-MRS in large-scale early recognition and treatment studies in MTL affecting disorders. (orig.)

Monitored Retrievable Storage System Requirements Document

International Nuclear Information System (INIS)

1994-03-01

This Monitored Retrievable Storage System Requirements Document (MRS-SRD) describes the functions to be performed and technical requirements for a Monitored Retrievable Storage (MRS) facility subelement and the On-Site Transfer and Storage (OSTS) subelement. The MRS facility subelement provides for temporary storage, at a Civilian Radioactive Waste Management System (CRWMS) operated site, of spent nuclear fuel (SNF) contained in an NRC-approved Multi-Purpose Canister (MPC) storage mode, or other NRC-approved storage modes. The OSTS subelement provides for transfer and storage, at Purchaser sites, of spent nuclear fuel (SNF) contained in MPCs. Both the MRS facility subelement and the OSTS subelement are in support of the CRWMS. The purpose of the MRS-SRD is to define the top-level requirements for the development of the MRS facility and the OSTS. These requirements include design, operation, and decommissioning requirements to the extent they impact on the physical development of the MRS facility and the OSTS. The document also presents an overall description of the MRS facility and the OSTS, their functions (derived by extending the functional analysis documented by the Physical System Requirements (PSR) Store Waste Document), their segments, and the requirements allocated to the segments. In addition, the top-level interface requirements of the MRS facility and the OSTS are included. As such, the MRS-SRD provides the technical baseline for the MRS Safety Analysis Report (SAR) design and the OSTS Safety Analysis Report design

Development of a Computer Writing System Based on EOG.

Science.gov (United States)

López, Alberto; Ferrero, Francisco; Yangüela, David; Álvarez, Constantina; Postolache, Octavian

2017-06-26

The development of a novel computer writing system based on eye movements is introduced herein. A system of these characteristics requires the consideration of three subsystems: (1) A hardware device for the acquisition and transmission of the signals generated by eye movement to the computer; (2) A software application that allows, among other functions, data processing in order to minimize noise and classify signals; and (3) A graphical interface that allows the user to write text easily on the computer screen using eye movements only. This work analyzes these three subsystems and proposes innovative and low cost solutions for each one of them. This computer writing system was tested with 20 users and its efficiency was compared to a traditional virtual keyboard. The results have shown an important reduction in the time spent on writing, which can be very useful, especially for people with severe motor disorders.

Development of a Computer Writing System Based on EOG

Directory of Open Access Journals (Sweden)

Alberto López

2017-06-01

Full Text Available The development of a novel computer writing system based on eye movements is introduced herein. A system of these characteristics requires the consideration of three subsystems: (1 A hardware device for the acquisition and transmission of the signals generated by eye movement to the computer; (2 A software application that allows, among other functions, data processing in order to minimize noise and classify signals; and (3 A graphical interface that allows the user to write text easily on the computer screen using eye movements only. This work analyzes these three subsystems and proposes innovative and low cost solutions for each one of them. This computer writing system was tested with 20 users and its efficiency was compared to a traditional virtual keyboard. The results have shown an important reduction in the time spent on writing, which can be very useful, especially for people with severe motor disorders.

Comparison of 1H-MRS-detected metabolic characteristics in single metastatic brain tumors of different origin

International Nuclear Information System (INIS)

Chernov, M.F.; Ono, Yuko; Kubo, Osami; Hori, Tomokatsu

2006-01-01

Various types of intracranial metastases exhibit different growth patterns, which can be reflected in their metabolic characteristics and investigated noninvasively by proton magnetic resonance spectroscopy ( 1 H-MRS). The objective of the present study was comparison of the 1 H-MRS-detected metabolic parameters in brain metastases of different origin. Twenty-five patients (15 men and 10 women; mean age, 62.0 years) with single, previously nontreated metastatic brain tumors were investigated by long-echo single-voxel volume-selected 1 H-MRS. The primary cancer was located in the lungs (10 cases), colon and rectum (8 cases), breast (3 cases), kidney (2 cases), prostate (1 case), and cardiac muscle (1 case). Comparison of clinical and radiological variables, including type of tumor contrast enhancement and extension of peritumoral edema, did not disclose statistically significant differences in metastatic brain tumors of different origin. At the same time, comparison of 1 H-MRS-detected metabolic characteristics revealed that metastases of colorectal carcinoma have greater content of mobile lipids (Lip) compared to other neoplasms. In conclusion, high Lip content in the viable brain metastases of colorectal carcinoma can be used as an additional diagnostic clue for noninvasive identification of these tumors and should be taken into consideration in cases of 1 H-MRS-based differentiation of their recurrence and radiation-induced necrosis after radiosurgical or radiotherapeutic treatment. (author)

Classification of EEG signals using a genetic-based machine learning classifier.

Science.gov (United States)

Skinner, B T; Nguyen, H T; Liu, D K

2007-01-01

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

Mutations in the Arabidopsis AtMRS2-11/AtMGT10/VAR5 Gene Cause Leaf Reticulation

Directory of Open Access Journals (Sweden)

Shuang Liang

2017-11-01

Full Text Available In higher plants, the development of functional chloroplasts is essential for photosynthesis and many other physiological processes. With a long-term goal of elucidating the genetic regulation of chloroplast development, we identified two allelic leaf variegation mutants, variegated5-1 (var5-1 and var5-2. Both mutants showed a distinct leaf reticulation phenotype of yellow paraveinal regions and green interveinal regions, and the leaf reticulation phenotype correlated with photosynthetic defects. Through the identification of mutation sites in the two mutant alleles and the molecular complementation, we confirmed that VAR5 encodes a CorA family of Mg2+ transporters also known as AtMRS2-11/AtMGT10. Using protoplast transient expression and biochemical fractionation assays, we demonstrated that AtMRS2-11/AtMGT10/VAR5 likely localizes to the chloroplast envelope. Moreover, we established that AtMRS2-11/AtMGT10/VAR5 forms large molecular weight complexes in the chloroplast and the sizes of these complexes clearly exceed those of their bacterial counterparts, suggesting the compositions of CorA Mg2+ transporter complex is different between the chloroplast and bacteria. Our findings indicate that AtMRS2-11/AtMGT10/VAR5 plays an important role in the tissue specific regulation of chloroplast development.

SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

Directory of Open Access Journals (Sweden)

Zhang Xinzheng

2017-10-01

Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

Diagnostic value of MRS-quantified brain tissue lactate level in identifying children with mitochondrial disorders

International Nuclear Information System (INIS)

Lunsing, Roelineke J.; Strating, Kim; Koning, Tom J. de; Sijens, Paul E.

2017-01-01

Magnetic resonance spectroscopy (MRS) of children with or without neurometabolic disease is used for the first time for quantitative assessment of brain tissue lactate signals, to elaborate on previous suggestions of MRS-detected lactate as a marker of mitochondrial disease. Multivoxel MRS of a transverse plane of brain tissue cranial to the ventricles was performed in 88 children suspected of having neurometabolic disease, divided into 'definite' (n = 17, ≥1 major criteria), 'probable' (n = 10, ≥2 minor criteria), 'possible' (n = 17, 1 minor criterion) and 'unlikely' mitochondrial disease (n = 44, none of the criteria). Lactate levels, expressed in standardized arbitrary units or relative to creatine, were derived from summed signals from all voxels. Ten 'unlikely' children with a normal neurological exam served as the MRS reference subgroup. For 61 of 88 children, CSF lactate values were obtained. MRS lactate level (>12 arbitrary units) and the lactate-to-creatine ratio (L/Cr >0.22) differed significantly between the definite and the unlikely group (p = 0.015 and p = 0.001, respectively). MRS L/Cr also differentiated between the probable and the MRS reference subgroup (p = 0.03). No significant group differences were found for CSF lactate. MRS-quantified brain tissue lactate levels can serve as diagnostic marker for identifying mitochondrial disease in children. (orig.)

Performance of classification confidence measures in dynamic classifier systems

Czech Academy of Sciences Publication Activity Database

Štefka, D.; Holeňa, Martin

2013-01-01

Roč. 23, č. 4 (2013), s. 299-319 ISSN 1210-0552 R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : classifier combining * dynamic classifier systems * classification confidence Subject RIV: IN - Informatics, Computer Science Impact factor: 0.412, year: 2013

Metabolic changes assessed by MRS accurately reflect brain function during drug-induced epilepsy in mice in contrast to fMRI-based hemodynamic readouts.

Science.gov (United States)

Seuwen, Aline; Schroeter, Aileen; Grandjean, Joanes; Rudin, Markus

2015-10-15

Functional proton magnetic resonance spectroscopy (1H-MRS) enables the non-invasive assessment of neural activity by measuring signals arising from endogenous metabolites in a time resolved manner. Proof-of-principle of this approach has been demonstrated in humans and rats; yet functional 1H-MRS has not been applied in mice so far, although it would be of considerable interest given the many genetically engineered models of neurological disorders established in this species only. Mouse 1H-MRS is challenging as the high demands on spatial resolution typically result in long data acquisition times not commensurable with functional studies. Here, we propose an approach based on spectroscopic imaging in combination with the acquisition of the free induction decay to maximize signal intensity. Highly resolved metabolite maps have been recorded from mouse brain with 12 min temporal resolution. This enabled monitoring of metabolic changes following the administration of bicuculline, a GABA-A receptor antagonist. Changes in levels of metabolites involved in energy metabolism (lactate and phosphocreatine) and neurotransmitters (glutamate) were investigated in a region-dependent manner and shown to scale with the bicuculline dose. GABAergic inhibition induced spectral changes characteristic for increased neurotransmitter turnover and oxidative stress. In contrast to metabolic readouts, BOLD and CBV fMRI responses did not scale with the bicuculline dose indicative of the failure of neurovascular coupling. Nevertheless fMRI measurements supported the notion of increased oxidative stress revealed by functional MRS. Hence, the combined analysis of metabolic and hemodynamic changes in response to stimulation provides complementary insight into processes associated with neural activity. Copyright © 2015 Elsevier Inc. All rights reserved.

Development of multicriteria models to classify energy efficiency alternatives

International Nuclear Information System (INIS)

Neves, Luis Pires; Antunes, Carlos Henggeler; Dias, Luis Candido; Martins, Antonio Gomes

2005-01-01

This paper aims at describing a novel constructive approach to develop decision support models to classify energy efficiency initiatives, including traditional Demand-Side Management and Market Transformation initiatives, overcoming the limitations and drawbacks of Cost-Benefit Analysis. A multicriteria approach based on the ELECTRE-TRI method is used, focusing on four perspectives: - an independent Agency with the aim of promoting energy efficiency; - Distribution-only utilities under a regulated framework; - the Regulator; - Supply companies in a competitive liberalized market. These perspectives were chosen after a system analysis of the decision situation regarding the implementation of energy efficiency initiatives, looking for the main roles and power relations, with the purpose of structuring the decision problem by identifying the actors, the decision makers, the decision paradigm, and the relevant criteria. The multicriteria models developed allow considering different kinds of impacts, but avoiding difficult measurements and unit conversions due to the nature of the multicriteria method chosen. The decision is then based on all the significant effects of the initiative, both positive and negative ones, including ancillary effects often forgotten in cost-benefit analysis. The ELECTRE-TRI, as most multicriteria methods, provides to the Decision Maker the ability of controlling the relevance each impact can have on the final decision. The decision support process encompasses a robustness analysis, which, together with a good documentation of the parameters supplied into the model, should support sound decisions. The models were tested with a set of real-world initiatives and compared with possible decisions based on Cost-Benefit analysis

Thai Finger-Spelling Recognition Using a Cascaded Classifier Based on Histogram of Orientation Gradient Features

Directory of Open Access Journals (Sweden)

Kittasil Silanon

2017-01-01

Full Text Available Hand posture recognition is an essential module in applications such as human-computer interaction (HCI, games, and sign language systems, in which performance and robustness are the primary requirements. In this paper, we proposed automatic classification to recognize 21 hand postures that represent letters in Thai finger-spelling based on Histogram of Orientation Gradient (HOG feature (which is applied with more focus on the information within certain region of the image rather than each single pixel and Adaptive Boost (i.e., AdaBoost learning technique to select the best weak classifier and to construct a strong classifier that consists of several weak classifiers to be cascaded in detection architecture. We collected 21 static hand posture images from 10 subjects for testing and training in Thai letters finger-spelling. The parameters for the training process have been adjusted in three experiments, false positive rates (FPR, true positive rates (TPR, and number of training stages (N, to achieve the most suitable training model for each hand posture. All cascaded classifiers are loaded into the system simultaneously to classify different hand postures. A correlation coefficient is computed to distinguish the hand postures that are similar. The system achieves approximately 78% accuracy on average on all classifier experiments.

Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

OpenAIRE

Luqman, Muhammad Muzzamil; Brouard, Thierry; Ramel, Jean-Yves

2010-01-01

We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational g...

A decision support system using combined-classifier for high-speed data stream in smart grid

Science.gov (United States)

Yang, Hang; Li, Peng; He, Zhian; Guo, Xiaobin; Fong, Simon; Chen, Huajun

2016-11-01

Large volume of high-speed streaming data is generated by big power grids continuously. In order to detect and avoid power grid failure, decision support systems (DSSs) are commonly adopted in power grid enterprises. Among all the decision-making algorithms, incremental decision tree is the most widely used one. In this paper, we propose a combined classifier that is a composite of a cache-based classifier (CBC) and a main tree classifier (MTC). We integrate this classifier into a stream processing engine on top of the DSS such that high-speed steaming data can be transformed into operational intelligence efficiently. Experimental results show that our proposed classifier can return more accurate answers than other existing ones.

Monitored Retrievable Storage (MRS) Facility and its impact on spent fuel transportation

International Nuclear Information System (INIS)

Joy, D.S.; Jolley, R.L.

1986-01-01

The Department of Energy has identified nine potential sites for a repository to permanently dispose of radioactive wastes. DOE has released several sets of maps and tables identifying expected transportation routes between nuclear reactors and repository sites. More recently, the DOE has announced three potential Monitored Retrievable Storage Facility (MRS) sites in the state of Tennessee. Obviously, if a large portion of the spent fuel is routed to Tennessee for consolidation and repackaging, there will be significant changes in the estimated routes. For typical scenarios, the number of shipments in the vicinity of the repository will be reduced. For example, with direct reactor to repository shipments, 995 highway and 262 rail shipments are expected to arrive at the repository annually. With a MRS these numbers are reduced to 201 and 30, respectively. The remaining consolidated fuel would be transported from the MRS in 22 dedicated trains (each train transporting five casks). Conversely, the MRS would result in an increase in the number of spent fuel shipments traveling through the eastern part of Tennessee. However, the operation of a MRS would significantly reduce the number of shipments through the central and western parts of the state

Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

Directory of Open Access Journals (Sweden)

A.V. Faria

2011-02-01

Full Text Available High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2

Directory of Open Access Journals (Sweden)

Shin Sook-Il

2011-01-01

Full Text Available Abstract Background Metabolic reconstructions (MRs are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Results Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i development and implementation of a community-based workflow for MR annotation and reconciliation; ii incorporation of thermodynamic information; and iii use of the consensus MR to identify potential multi-target drug therapy approaches. Conclusion Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2.

Science.gov (United States)

Thiele, Ines; Hyduke, Daniel R; Steeb, Benjamin; Fankam, Guy; Allen, Douglas K; Bazzani, Susanna; Charusanti, Pep; Chen, Feng-Chi; Fleming, Ronan M T; Hsiung, Chao A; De Keersmaecker, Sigrid C J; Liao, Yu-Chieh; Marchal, Kathleen; Mo, Monica L; Özdemir, Emre; Raghunathan, Anu; Reed, Jennifer L; Shin, Sook-il; Sigurbjörnsdóttir, Sara; Steinmann, Jonas; Sudarsan, Suresh; Swainston, Neil; Thijs, Inge M; Zengler, Karsten; Palsson, Bernhard O; Adkins, Joshua N; Bumann, Dirk

2011-01-18

Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2

Energy Technology Data Exchange (ETDEWEB)

Thiele, Ines; Hyduke, Daniel R.; Steeb, Benjamin; Fankam, Guy; Allen, Douglas K.; Bazzani, Susanna; Charusanti, Pep; Chen, Feng-Chi; Fleming, Ronan MT; Hsiung, Chao A.; De Keersmaecker, Sigrid CJ; Liao, Yu-Chieh; Marchal, Kathleen; Mo, Monica L.; Özdemir, Emre; Raghunathan, Anu; Reed, Jennifer L.; Shin, Sook-Il; Sigurbjörnsdóttir, Sara; Steinmann, Jonas; Sudarsan, Suresh; Swainston, Neil; Thijs, Inge M.; Zengler, Karsten; Palsson, Bernhard O.; Adkins, Joshua N.; Bumann, Dirk

2011-01-01

Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Finally, taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.

Diagnostic value of MRS-quantified brain tissue lactate level in identifying children with mitochondrial disorders

Energy Technology Data Exchange (ETDEWEB)

Lunsing, Roelineke J.; Strating, Kim [University Medical Centre Groningen, University of Groningen, Department of Child Neurology, Groningen (Netherlands); Koning, Tom J. de [University Medical Centre Groningen, University of Groningen, Department of Pediatric Metabolic Diseases, Groningen (Netherlands); Sijens, Paul E. [University Medical Centre Groningen, University of Groningen, Department of Radiology, Groningen (Netherlands)

2017-03-15

Magnetic resonance spectroscopy (MRS) of children with or without neurometabolic disease is used for the first time for quantitative assessment of brain tissue lactate signals, to elaborate on previous suggestions of MRS-detected lactate as a marker of mitochondrial disease. Multivoxel MRS of a transverse plane of brain tissue cranial to the ventricles was performed in 88 children suspected of having neurometabolic disease, divided into 'definite' (n = 17, ≥1 major criteria), 'probable' (n = 10, ≥2 minor criteria), 'possible' (n = 17, 1 minor criterion) and 'unlikely' mitochondrial disease (n = 44, none of the criteria). Lactate levels, expressed in standardized arbitrary units or relative to creatine, were derived from summed signals from all voxels. Ten 'unlikely' children with a normal neurological exam served as the MRS reference subgroup. For 61 of 88 children, CSF lactate values were obtained. MRS lactate level (>12 arbitrary units) and the lactate-to-creatine ratio (L/Cr >0.22) differed significantly between the definite and the unlikely group (p = 0.015 and p = 0.001, respectively). MRS L/Cr also differentiated between the probable and the MRS reference subgroup (p = 0.03). No significant group differences were found for CSF lactate. MRS-quantified brain tissue lactate levels can serve as diagnostic marker for identifying mitochondrial disease in children. (orig.)

Noninvasive brain metabolism measurement using carbon-13 magnetic resonance spectroscopy ({sup 13}C-MRS); Tanso 13 jiki kyomei spectroscopy ({sup 13}C-MRS) ni yoru mushinshuteki notaisha keisoku

Energy Technology Data Exchange (ETDEWEB)

Okamoto, K.; Tsukada, Y. [Toshiba Corp., Tokyo (Japan)

1998-10-10

Carbon-13 magnetic resonance spectroscopy ({sup 13}C-MRS) and research and development efforts for brain metabolism measurement are described. Brain metabolism is a process characterized in that it not only extracts energy by disintegrating grape sugar that is the practically sole source of energy into H2O, CO2, etc., but also vigorously synthesizes amino acids that perform important functions in neural transmission, such as glutamic acid, glutamine, and {gamma}-amino acid. MRS is a technique that utilizes the magnetic resonance, which is generated when an atomic nucleus with a spin is placed in a magnetic field, for the isolation and identification of chemicals in a living body through examining the delicate difference in the magnetic resonance frequencies of the nuclei under observation. Since the signals from {sup 13}C are low in intensity as compared with those from other nuclides, a method was contrived around 1980, which observes {sup 1}H combined with {sup 13}C in grape sugar and amino acids, named the HSQC (heteronuclear single quantum coherence) method. The author et al., combining gradient magnetic pulses with HSQC, actually measure Homo sapiens brain metabolism using {sup 13}C-MRS, and now believe that the technology will be put to practical application. 7 refs., 10 figs., 1 tab.

Spectral data de-noising using semi-classical signal analysis: application to localized MRS

KAUST Repository

Laleg-Kirati, Taous-Meriem

2016-09-05

In this paper, we propose a new post-processing technique called semi-classical signal analysis (SCSA) for MRS data de-noising. Similar to Fourier transformation, SCSA decomposes the input real positive MR spectrum into a set of linear combinations of squared eigenfunctions equivalently represented by localized functions with shape derived from the potential function of the Schrodinger operator. In this manner, the MRS spectral peaks represented as a sum of these \\'shaped like\\' functions are efficiently separated from noise and accurately analyzed. The performance of the method is tested by analyzing simulated and real MRS data. The results obtained demonstrate that the SCSA method is highly efficient in localized MRS data de-noising and allows for an accurate data quantification.

Spectral data de-noising using semi-classical signal analysis: application to localized MRS

KAUST Repository

Laleg-Kirati, Taous-Meriem; Zhang, Jiayu; Achten, Eric; Serrai, Hacene

2016-01-01

In this paper, we propose a new post-processing technique called semi-classical signal analysis (SCSA) for MRS data de-noising. Similar to Fourier transformation, SCSA decomposes the input real positive MR spectrum into a set of linear combinations of squared eigenfunctions equivalently represented by localized functions with shape derived from the potential function of the Schrodinger operator. In this manner, the MRS spectral peaks represented as a sum of these 'shaped like' functions are efficiently separated from noise and accurately analyzed. The performance of the method is tested by analyzing simulated and real MRS data. The results obtained demonstrate that the SCSA method is highly efficient in localized MRS data de-noising and allows for an accurate data quantification.

Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media

Science.gov (United States)

Candra Permana, Fahmi; Rosmansyah, Yusep; Setiawan Abdullah, Atje

2017-10-01

Students activity on social media can provide implicit knowledge and new perspectives for an educational system. Sentiment analysis is a part of text mining that can help to analyze and classify the opinion data. This research uses text mining and naive Bayes method as opinion classifier, to be used as an alternative methods in the process of evaluating studentss satisfaction for educational institution. Based on test results, this system can determine the opinion classification in Bahasa Indonesia using naive Bayes as opinion classifier with accuracy level of 84% correct, and the comparison between the existing system and the proposed system to evaluate students satisfaction in learning process, there is only a difference of 16.49%.

Monitored Retrievable Storage System Requirements Document. Revision 1

Energy Technology Data Exchange (ETDEWEB)

1994-03-01

This Monitored Retrievable Storage System Requirements Document (MRS-SRD) describes the functions to be performed and technical requirements for a Monitored Retrievable Storage (MRS) facility subelement and the On-Site Transfer and Storage (OSTS) subelement. The MRS facility subelement provides for temporary storage, at a Civilian Radioactive Waste Management System (CRWMS) operated site, of spent nuclear fuel (SNF) contained in an NRC-approved Multi-Purpose Canister (MPC) storage mode, or other NRC-approved storage modes. The OSTS subelement provides for transfer and storage, at Purchaser sites, of spent nuclear fuel (SNF) contained in MPCs. Both the MRS facility subelement and the OSTS subelement are in support of the CRWMS. The purpose of the MRS-SRD is to define the top-level requirements for the development of the MRS facility and the OSTS. These requirements include design, operation, and decommissioning requirements to the extent they impact on the physical development of the MRS facility and the OSTS. The document also presents an overall description of the MRS facility and the OSTS, their functions (derived by extending the functional analysis documented by the Physical System Requirements (PSR) Store Waste Document), their segments, and the requirements allocated to the segments. In addition, the top-level interface requirements of the MRS facility and the OSTS are included. As such, the MRS-SRD provides the technical baseline for the MRS Safety Analysis Report (SAR) design and the OSTS Safety Analysis Report design.

Detecting and classifying method based on similarity matching of Android malware behavior with profile.

Science.gov (United States)

Jang, Jae-Wook; Yun, Jaesung; Mohaisen, Aziz; Woo, Jiyoung; Kim, Huy Kang

2016-01-01

Mass-market mobile security threats have increased recently due to the growth of mobile technologies and the popularity of mobile devices. Accordingly, techniques have been introduced for identifying, classifying, and defending against mobile threats utilizing static, dynamic, on-device, and off-device techniques. Static techniques are easy to evade, while dynamic techniques are expensive. On-device techniques are evasion, while off-device techniques need being always online. To address some of those shortcomings, we introduce Andro-profiler, a hybrid behavior based analysis and classification system for mobile malware. Andro-profiler main goals are efficiency, scalability, and accuracy. For that, Andro-profiler classifies malware by exploiting the behavior profiling extracted from the integrated system logs including system calls. Andro-profiler executes a malicious application on an emulator in order to generate the integrated system logs, and creates human-readable behavior profiles by analyzing the integrated system logs. By comparing the behavior profile of malicious application with representative behavior profile for each malware family using a weighted similarity matching technique, Andro-profiler detects and classifies it into malware families. The experiment results demonstrate that Andro-profiler is scalable, performs well in detecting and classifying malware with accuracy greater than 98 %, outperforms the existing state-of-the-art work, and is capable of identifying 0-day mobile malware samples.

A comprehensive review of the 1H-MRS metabolite spectrum in autism spectrum disorder.

Directory of Open Access Journals (Sweden)

Talitha eFord

2016-03-01

Full Text Available Neuroimaging studies of neuropsychiatric behaviour biomarkers across spectrum disorders are typically based on diagnosis, thus failing to account for the heterogeneity of multi-dimensional spectrum disorders such as autism (ASD. Control group trait phenotypes are also seldom reported. Proton magnetic resonance spectroscopy (1H-MRS measures the abundance of neurochemicals such as neurotransmitters and metabolites and hence can probe disorder phenotypes at clinical and sub-clinical levels. This detailed review summarises and critiques the current 1H-MRS research in ASD. The literature reports reduced N-acetylaspartate (NAA, glutamate and glutamine (Glx, gamma-aminobutyric acid (GABA, creatine and choline, and increased glutamate for children with ASD. Adult studies are few and results are inconclusive. Overall, the literature has several limitations arising from differences in 1H-MRS methodology and sample demographics. We argue that more consistent methods and greater emphasis on phenotype studies will advance understanding of underlying cortical metabolite disturbance in ASD, and the detection, diagnosis and treatment of ASD and other multi-dimensional psychiatric disorders.

In vivo proton MRS of normal pancreas metabolites during breath-holding and free-breathing

International Nuclear Information System (INIS)

Su, T.-H.; Jin, E.-H.; Shen, H.; Zhang, Y.; He, W.

2012-01-01

Aim: To characterize normal pancreas metabolites using in vivo proton magnetic resonance spectroscopy ( 1 H MRS) at 3 T under conditions of breath-holding and free-breathing. Materials and methods: The pancreases of 32 healthy volunteers were examined using 1 H MRS during breath-holding and free-breathing acquisitions in a single-voxel point-resolved selective spectroscopy sequence (PRESS) technique using a 3 T MRI system. Resonances were compared between paired spectra of the two breathing modes. Furthermore, correlations between lipid (Lip) content and age, body-mass index (BMI), as well as choline (Cho) peak visibility of the normal pancreas were analysed during breath-holding. Results: Twenty-nine pairs of spectra were successfully obtained showing three major resonances, Lip, Cho, cholesterol and the unsaturated parts of the olefinic region of fatty acids (Chol + Unsat). Breath-hold spectra were generally better, with higher signal-to-noise ratios (SNR; Z=–2.646, p = 0.008) and Cho peak visible status (Z=–2.449, p = 0.014). Correlations were significant between spectra acquired by the two breathing modes, especially for Lip height, Lip area, and the area of other peaks at 1.9–4.1 ppm. However, the Lip resonance was significantly different between the spectra of the two breathing modes (p 1 H MRS of the normal pancreas at 3 T is technically feasible and can characterize several metabolites. 1 H MRS during breath-holding acquisition is superior to that during free-breathing acquisition.

Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data

Directory of Open Access Journals (Sweden)

Jaewook Jung

2016-12-01

Full Text Available Railways have been used as one of the most crucial means of transportation in public mobility and economic development. For safe railway operation, the electrification system in the railway infrastructure, which supplies electric power to trains, is an essential facility for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS data into ten target classes representing overhead wires, movable brackets and poles, which are key objects in the electrification system. In general, the layout of the railway electrification system shows strong spatial regularity relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF, which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds are converted into a set of line segments to which the labeling process is applied. Support Vector Machine (SVM is used as a local classifier considering only node features for producing the unary potentials of the CRF model. As the short-range pairwise contextual term, the Potts model is applied to enforce a local smoothness in the short-range graph; while long-range pairwise potentials are designed to enhance the spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by the naive Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables, which represent the co-occurrence rate of spatial relations in the horizontal and vertical

MRS [monitored retrievable storage] systems study Task G report: The role and functions of surface storage of radioactive material in the federal waste management system

International Nuclear Information System (INIS)

Wood, T.W.; Short, S.M.; Woodruff, M.G.; Altenhofen, M.K.; MacKay, C.A.

1989-04-01

This is one of nine studies undertaken by contractors to the US Department of Energy (DOE), Office of Civilian Radioactive Waste Management (OCRWM), to provide a technical basis for re-evaluating the role of a monitored retrievable storage (MRS) facility. The study investigates the functions that could be performed by surface storage of radioactive material within the federal radioactive waste management system, including enabling acceptance of spent fuel from utility owners, scheduling of waste-preparation processes within the system, enhancement of system operating reliability, and conditioning the thermal (decay heat) characteristics of spent fuel emplaced in a repository. The analysis focuses particularly on the effects of storage capacity and DOE acceptance schedule on power reactors. Figures of merit developed include the storage capacity [in metric tons of uranium (MTU)] required to be added beyond currently estimated maximum spent fuel storage capacities and its associated cost, and the number of years that spent fuel pools would remain open after last discharge (in pool-years) and the cost of this period of operation. 27 refs., 36 figs., 18 tabs

On-line computing in a classified environment

International Nuclear Information System (INIS)

O'Callaghan, P.B.

1982-01-01

Westinghouse Hanford Company (WHC) recently developed a Department of Energy (DOE) approved real-time, on-line computer system to control nuclear material. The system simultaneously processes both classified and unclassified information. Implementation of this system required application of many security techniques. The system has a secure, but user friendly interface. Many software applications protect the integrity of the data base from malevolent or accidental errors. Programming practices ensure the integrity of the computer system software. The audit trail and the reports generation capability record user actions and status of the nuclear material inventory

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy.

Directory of Open Access Journals (Sweden)

Lina Zhang

Full Text Available Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information, PSSM (Position Specific Scoring Matrix, RSA (Relative Solvent Accessibility, and CTD (Composition, Transition, Distribution. The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest, SMO (Sequential Minimal Optimization, NNA (Nearest Neighbor Algorithm, and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew's Correlation Coefficient of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc.

Utilización del medio Mrs-s en el aislamiento de bacterias lácticas mesofilas en leche de cabra Utilización del medio Mrs-s en el aislamiento de bacterias lacticas mesofilas en leche de cabra

Directory of Open Access Journals (Sweden)

Fortes F. Celia L. de Luces

1990-12-01

Full Text Available Con el fin de evaluar el MRS-S (Sorbato al 0.10% en el aislamiento de bacterias lácticas se cultivaron muestras de leche de cabra cruda en MRS-S y PCA en profundidad y se incubaron en aerobiósis a 320C durante 48 horas. Los cocos gram positivos, catalasa negativos que crecieron en MRS-S se aislaron y sometieron a caracterización preliminar a través del crecimiento en agar MRS-S, MRS-T (tetraciclina 0.20 µg/ml, N-L (bacterias aromáticas, reducción de la leche tornasolada a 40 y 21oC y crecimiento a 45 y 10oC.Las cepas seleccionadas se sometieron a caracterización fisiológica y Bioquímica. El medio MRS-S mostró ser efectivo con un porcentaje de inhibición de la flora indeseable del 86.56%, y adecuado por el aislamiento de Lactococcus. De acuerdo con los perfiles taxonómicos se consiguió aislar de un total de 156 colonias dos Lactococcus lactis subsp. lactis y un Lactococcus lactis biovar diacetilactis.With the objective of evaluating the MRS-S (0.10% of sorbate in the isolation of lactic bacteria samples of raw goat milk were cultivated in MRS-S and PCA in deep and incubated in aerobic conditions for 48 hours al 32oC. Gram positive coccus, negative catalase which grew in MRS-S were isolated and preliminarly characterized through the growing process in agar MRS-S, MRS-T (O.20ltg/ml tetracycline, N-L (aromatic bacteria, litmus milk reduction at 40 and 210C and growing at 45 and 10oC. Selected strains were subject to the physiological and biochemical characterization. MRS-S media showed to be effective with an 86.56% of inhibition for indesirable bacteria and adequated for Lactococcus isolation. Related to taxonomic profiles from 156 colonies were isolated two lactococcus lactis subsp. lactis and one Lactococcus luctis biovar diacetilactis.

A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier

Directory of Open Access Journals (Sweden)

Haryati Jaafar

2015-01-01

Full Text Available Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN, was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

1H-MRS for the diagnosis of acute disseminated encephalomyelitis: insight into the acute-disease stage

International Nuclear Information System (INIS)

Ben Sira, Liat; Miller, Elka; Artzi, Moran; Fattal-Valevski, Aviva; Constantini, Shlomi; Ben Bashat, Dafna

2010-01-01

Acute disseminated encephalomyelitis (ADEM) is a demyelinating disorder of the central nervous system (CNS). Differentiating ADEM from other inflammatory disorders, such as multiple sclerosis, is not always conclusive using conventional MRI. To evaluate longitudinal magnetic resonance spectroscopy (MRS) changes that distinguish ADEM from other inflammatory disorders. MRI/MRS scans were performed in seven patients with ADEM during the acute and chronic phases of the disease. Partial recovery was detected between the acute and chronic phases in choline/creatine ratio. Major elevation of lipids and reduction in myo-inositol/creatine ratio was detected in all patients during the acute phase, followed by a reduction in lipids peak and elevation above normal in myo-inositol/creatine ratio during the chronic phase. Consistent and unique MRS changes in metabolite ratios between the acute and chronic presentations of the disease were found. To the best of our knowledge, these patterns have not been described in other inflammatory disorders and might assist in the early diagnosis of ADEM. (orig.)

Integral Monitored Retrievable Storage (MRS) Facility conceptual basis for design

International Nuclear Information System (INIS)

1985-10-01

The purpose of the Conceptual Basis for Design is to provide a control document that establishes the basis for executing the conceptual design of the Integral Monitored Retrievable Storage (MRS) Facility. This conceptual design shall provide the basis for preparation of a proposal to Congress by the Department of Energy (DOE) for construction of one or more MRS Facilities for storage of spent nuclear fuel, high-level radioactive waste, and transuranic (TRU) waste. 4 figs., 25 tabs

J-difference-edited MRS measures of γ-aminobutyric acid before and after acute caffeine administration.

Science.gov (United States)

Oeltzschner, Georg; Zöllner, Helge J; Jonuscheit, Marc; Lanzman, Rotem S; Schnitzler, Alfons; Wittsack, Hans-Jörg

2018-05-12

The aim of this study was to investigate potential effects of acute caffeine intake on J-difference-edited MRS measures of the primary inhibitory neurotransmitter γ-aminobutyric acid (GABA). J-difference-edited Mescher-Garwood PRESS (MEGA-PRESS) and conventional PRESS data were acquired at 3T from voxels in the anterior cingulate and occipital area of the brain in 15 healthy subjects, before and after oral intake of a 200-mg caffeine dose. MEGA-PRESS data were analyzed with the MATLAB-based Gannet tool to estimate GABA+ macromolecule (GABA+) levels, while PRESS data were analyzed with LCModel to estimate levels of glutamate, glutamate+glutamine, N-acetylaspartate, and myo-inositol. All metabolites were quantified with respect to the internal reference compounds creatine and tissue water, and compared between the pre- and post-caffeine intake condition. For both MRS voxels, mean GABA+ estimates did not differ before and after caffeine intake. Slightly lower estimates of myo-inositol were observed after caffeine intake in both voxels. N-acetylaspartate, glutamate, and glutamate+glutamine did not show significant differences between conditions. Mean GABA+ estimates from J-difference-edited MRS in two different brain regions are not altered by acute oral administration of caffeine. These findings may increase subject recruitment efficiency for MRS studies. © 2018 International Society for Magnetic Resonance in Medicine.

Simulation of the MRS receiving and handling facility

International Nuclear Information System (INIS)

Triplett, M.B.; Imhoff, C.H.; Hostick, C.J.

1984-02-01

Monitored retrievable storage (MRS) will be required to handle a large volume of spent fuel or high-level waste (HLW) in case of delays in repository deployment. The quantities of materials to be received and repackaged for storage far exceed the requirements of existing waste mangement facilities. A computer simulation model of the MRS receiving and handling (R and H) fcility has been constructed and used to evaluate design alternatives. Studies have identified processes or activities which may constrain throughput performance. In addition, the model has helped to assess design tradeoffs such as those to be made among improved process times, redundant service lines, and improved component availability. 1 reference, 5 figures

A fuzzy hill-climbing algorithm for the development of a compact associative classifier

Science.gov (United States)

Mitra, Soumyaroop; Lam, Sarah S.

2012-02-01

Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

Brain metabolic impairment of OSAS: evidence from MRS

International Nuclear Information System (INIS)

Shen Jie; Long Miaomiao; Shen Wen; Qi Ji

2011-01-01

Objective: To evaluate the impact of obstructive sleep apnea syndrome (OSAS) on human cerebral metabolism by using magnetic resonance spectroscopy (MRS). Materials and methods: Twenty-one severe OSAS patients, 14 mild-moderate OSAS patients, and 15 healthy control subjects were included. All subjects underwent MRS using the point-resolved echo spin spectroscopy (PRESS). Proton volumes of interest were placed in the bilateral frontal lobes and left temporal -parietal-occipital cortex, and left hippocampus. Results: 1. Compared to the controls, the NAA/Cr ratio was significantly decreased in the left frontal lobe in the severe OSAS group (P=0.004), and in the right frontal lobe in the severe (P=0.002) and mild-moderate (P=0.007) OSAS patients. The NAA/Cr ratio trended to be decreased in the left hippocampus in the OSAS patients compared to controls. 2. A significant increase in the ml/Cr ratio was detected in the right frontal regions in the severe (P=0.008) and mild-moderate (P<0.001) OSAS groups. 3. Clx/Cr ratio values were significantly smaller than controls in the left (P=0.006) and right (P=0.027) frontal regions. Conclusion: Bilateral frontal lobes are the vulnerable location in patients with OSAS. MRS can be used to screen the brain metabolic impairment. (authors)

An Ethical Interpretation of Virginia Woolf's Mrs.Dalloway%An Ethical Interpretation of Virginia Woolf's Mrs. Dalloway

Institute of Scientific and Technical Information of China (English)

朱蕾

2011-01-01

As a stream-of-consciousness novelist,Woolf is usually seen as being far away from social and political reality.This thesis attempts to apply Ethical Literary Criticism to analyze by rereading the text in detail in order to explore the ethical thinking embodied in Woolfs Mrs.Dalloway,exploring those ethical connotations usually ignored by former critics and scholars.

nRC: non-coding RNA Classifier based on structural features.

Science.gov (United States)

Fiannaca, Antonino; La Rosa, Massimo; La Paglia, Laura; Rizzo, Riccardo; Urso, Alfonso

2017-01-01

Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

The assessment of fetus in distress using MRI and 1H MRS - based on performed observation

International Nuclear Information System (INIS)

Borowska-Matwiejczuk, K.; Tarasow, E.; Walecki, J.; Lemancewicz, A.; Kubas, B.; Urban, R.

2003-01-01

Hypoxia is one of the basic factor that cause lesions and intrauterine fetus' death. This is why the recognition of early changes before the appearance of irreversible damages should be the main aim of the assessment and monitoring of a fetus. Apart from the methods of the assessment of a fetus' state that have been applied so far, a new non-invasive imaging technique in obstetrics as MR has appeared. This method makes it possible to assess morphologic structures of a brain, and metabolic processes with use of magnetic resonance spectroscopy (MRS). The study was carried out in 20 pregnant women with pregnancy-induced hypertension (11 cases), chronic hypertension (2 cases), gestational diabetes (6 cases) and IUGR (6 cases). The cardiotocography color Doppler flow assessment of umbilical artery and medial cerebral artery were performed. In cases of abnormal cardiotocography, and Doppler examinations suggested ischemic lesions 5 cases of focal ischemic areas in MR and 6 abnormal MRS spectra were demonstrated. (author)

EVALUATING A COMPUTER BASED SKILLS ACQUISITION TRAINER TO CLASSIFY BADMINTON PLAYERS

Directory of Open Access Journals (Sweden)

Minh Vu Huynh

2011-09-01

Full Text Available The aim of the present study was to compare the statistical ability of both neural networks and discriminant function analysis on the newly developed SATB program. Using these statistical tools, we identified the accuracy of the SATB in classifying badminton players into different skill level groups. Forty-one participants, classified as advanced, intermediate, or beginner skilled level, participated in this study. Results indicated neural networks are more effective in predicting group membership, and displayed higher predictive validity when compared to discriminant analysis. Using these outcomes, in conjunction with the physiological and biomechanical variables of the participants, we assessed the authenticity and accuracy of the SATB and commented on the overall effectiveness of the visual based training approach to training badminton athletes

Radiation induced early delayed changes in mice brain: a 1h MRS and behavioral evaluation study

International Nuclear Information System (INIS)

Gupta, Mamta; Rana, Poonam; Haridas, Seenu; Manda, Kailash; Hemanth Kumar, B.S.; Khushu, Subash

2014-01-01

Radiation induced CNS injury can be classified as acute, early delayed and late delayed. Most of the studies suggest that acute injury is reversible whereas early delayed and late delayed injury is irreversible leading to metabolic and cognitive impairment. Extensive research has been carried out on cranial radiation induced early and late delayed changes, there are no reports on whole body radiation induced early and delayed changes. The present study was designed to observe early delayed effects of radiation during whole body radiation exposure. A total of 20 C57 male mice were divided in two groups of 10 animals each. One group was exposed to a dose of 5 Gy whole body radiation through Tele 60 Co irradiation facility with source operating at 2.496 Gy/min, while other group served as sham irradiated control. Behavioral and MR spectroscopy was carried out 3 months post irradiation. Behavioral parameters such as locomotor activity and working memory were evaluated first then followed by MR spectroscopy at 7T animal MRI system. For MRS, voxel was localised in the cortex-hippocampus region of mouse brain. MR spectra were acquired using PRESS sequence, FID was processed using LC model for quantitation. The data showed impaired cognitive functions and altered metabolite levels during early delayed phase of whole body radiation induced injury. In behavioural experiments, there was a significant impairment in the cognitive as well as exploratory functions at 3 months post irradiation in irradiated group as compared to controls. MRS results explained changes in mI, glutamine and glx levels in irradiated animals compared to controls. Altered mI level has been found to be associated with reduced cognitive abilities in many brain disorders including MCI and Alzheimer's disease. The findings of this study suggest that whole body radiation exposure may have long lasting effect on the cognitive performance. (author)

Identifying and Classifying Mobile Business Models Based on Meta-Synthesis Approach

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Porrandokht Niroomand

2012-03-01

Full Text Available The appearance of mobile has provided unique opportunities and fields through the development and creation of businesses and has been able to create the new job opportunities. The current research tries to familiarize entrepreneures who are running the businesses especially in the area of mobile services with business models. These business models can familiarize them for implementing the new ideas and designs since they can enter to business market. Searching in many papers shows that there are no propitiated papers and researches that can identify, categorize and analyze the mobile business models. Consequently, this paper involves innovation. The first part of this paper presents the review about the concepts and theories about the different mobile generations, the mobile commerce and business models. Afterwards, 92 models are compared, interpreted, translated and combined using 33 papers, books based on two different criteria that are expert criterion and kind of product criterion. In the classification of models according to models that are presented by experts, the models are classified based on criteria such as business fields, business partners, the rate of dynamism, the kind of activity, the focus areas, the mobile generations, transparency, the type of operator activities, marketing and advertisements. The models that are classified based on the kind of product have been analyzed and classified at four different areas of mobile commerce including the content production, technology (software and hardware, network and synthetic.

Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS

DEFF Research Database (Denmark)

Vinding, Mads Sloth; Laustsen, Christoffer; Maximov, Ivan I.

2013-01-01

. This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-13C]pyruvate on a 4.7T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region...

The use of short-echo-time 1H MRS for childhood cerebellar tumours prior to histopathological diagnosis

International Nuclear Information System (INIS)

Harris, Lisa M.; Peet, Andrew C.; Davies, Nigel; Natarajan, Kal; MacPherson, Lesley; Foster, Katharine; Lateef, Shaheen; Sgouros, Spyridon; Brundler, Marie-Anne; Arvanitis, Theodoros N.; Grundy, Richard G.

2007-01-01

Proton magnetic resonance spectroscopy (MRS) measures concentrations of metabolites in vivo and provides a powerful method for identifying tumours. MRS has not entered routine clinical use partly due to the difficulty of analysing the spectra. To create a straightforward method for interpreting short-echo-time MRS of childhood cerebellar tumours. Single-voxel MRS (1.5-T Siemens Symphony NUM4, TR/TE 1,500/30 ms) was performed at presentation in 30 children with cerebellar tumours. The MRS results were analysed for comparison with histological diagnosis. Peak heights for N-acetyl aspartate (NAA), creatine (Cr), choline (Cho) and myo-inositol (mIns) were determined and receiver operator characteristic curves used to select ratios that best discriminated between the tumour types. The method was implemented by a group of clinicians and scientists, blinded to the results. A total of 27 MRS studies met the quality control criteria. NAA/Cr >4.0 distinguished all but one of the astrocytomas from the other tumours. A combination of Cr/Cho <0.75 and mIns/NAA <2.1 separated all the medulloblastomas from the ependymomas. Peak height ratios from short-echo-time MRS can accurately predict the histopathology of childhood cerebellar tumours. (orig.)

Developing a Web-Based Advisory Expert System for Implementing Traffic Calming Strategies

Directory of Open Access Journals (Sweden)

Amir Falamarzi

2014-01-01

Full Text Available Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented.

Combined MRI and MRS improves pre-therapeutic diagnoses of pediatric brain tumors over MRI alone

Energy Technology Data Exchange (ETDEWEB)

Shiroishi, Mark S.; Nelson, Marvin D. [Children' s Hospital Los Angeles/Keck School of Medicine of USC, Department of Radiology, Los Angeles, CA (United States); Panigrahy, Ashok [Children' s Hospital Los Angeles/Keck School of Medicine of USC, Department of Radiology, Los Angeles, CA (United States); Children' s Hospital of Pittsburgh of University of Pittsburgh Medical Center, Department of Pediatric Radiology, Pittsburgh, PA (United States); Moore, Kevin R. [Primary Children' s Medical Center, Department of Radiology, Salt Lake City, UT (United States); Gilles, Floyd H. [Children' s Hospital Los Angeles/Keck School of Medicine of USC, Department of Pathology, Los Angeles, CA (United States); Gonzalez-Gomez, Ignacio [All Children' s Hospital, Department of Pathology, St. Petersburg, FL (United States); Blueml, Stefan [Children' s Hospital Los Angeles/Keck School of Medicine of USC, Department of Radiology, Los Angeles, CA (United States); Rudi Schulte Research Institute, Santa Barbara, CA (United States)

2015-09-15

The specific goal of this study was to determine whether the inclusion of MRS had a measureable and positive impact on the accuracy of pre-surgical MR examinations of untreated pediatric brain tumors over that of MRI alone in clinical practice. Final imaging reports of 120 pediatric patients with newly detected brain tumors who underwent combined MRI/MRS examinations were retrospectively reviewed. Final pathology was available in all cases. Group A comprised 60 subjects studied between June 2001 and January 2005, when MRS was considered exploratory and radiologists utilized only conventional MRI to arrive at a diagnosis. For group B, comprising 60 subjects studied between January 2005 and March 2008, the radiologists utilized information from both MRI and MRS. Furthermore, radiologists revisited group A (blind review, time lapse >4 years) to determine whether the additional information from MRS would have altered their interpretation. Sixty-three percent of patients in group A were diagnosed correctly, whereas in 10 % the report was partially correct with the final tumor type mentioned (but not mentioned as most likely tumor), while in 27 % of cases the reports were wrong. For group B, the diagnoses were correct in 87 %, partially correct in 5 %, and incorrect in 8 % of the cases, which is a significant improvement (p < 0.005). Re-review of combined MRI and MRS of group A resulted 87 % correct, 7 % partially correct, and 7 % incorrect diagnoses, which is a significant improvement over the original diagnoses (p < 0.05). Adding MRS to conventional MRI significantly improved diagnostic accuracy in preoperative pediatric patients with untreated brain tumors. (orig.)

Combined MRI and MRS improves pre-therapeutic diagnoses of pediatric brain tumors over MRI alone

International Nuclear Information System (INIS)

Shiroishi, Mark S.; Nelson, Marvin D.; Panigrahy, Ashok; Moore, Kevin R.; Gilles, Floyd H.; Gonzalez-Gomez, Ignacio; Blueml, Stefan

2015-01-01

The specific goal of this study was to determine whether the inclusion of MRS had a measureable and positive impact on the accuracy of pre-surgical MR examinations of untreated pediatric brain tumors over that of MRI alone in clinical practice. Final imaging reports of 120 pediatric patients with newly detected brain tumors who underwent combined MRI/MRS examinations were retrospectively reviewed. Final pathology was available in all cases. Group A comprised 60 subjects studied between June 2001 and January 2005, when MRS was considered exploratory and radiologists utilized only conventional MRI to arrive at a diagnosis. For group B, comprising 60 subjects studied between January 2005 and March 2008, the radiologists utilized information from both MRI and MRS. Furthermore, radiologists revisited group A (blind review, time lapse >4 years) to determine whether the additional information from MRS would have altered their interpretation. Sixty-three percent of patients in group A were diagnosed correctly, whereas in 10 % the report was partially correct with the final tumor type mentioned (but not mentioned as most likely tumor), while in 27 % of cases the reports were wrong. For group B, the diagnoses were correct in 87 %, partially correct in 5 %, and incorrect in 8 % of the cases, which is a significant improvement (p < 0.005). Re-review of combined MRI and MRS of group A resulted 87 % correct, 7 % partially correct, and 7 % incorrect diagnoses, which is a significant improvement over the original diagnoses (p < 0.05). Adding MRS to conventional MRI significantly improved diagnostic accuracy in preoperative pediatric patients with untreated brain tumors. (orig.)

Interface Prostheses With Classifier-Feedback-Based User Training.

Science.gov (United States)

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well

MRS/IS facility co-located with a repository: preconceptual design and life-cycle cost estimates

International Nuclear Information System (INIS)

Smith, R.I.; Nesbitt, J.F.

1982-11-01

A program is described to examine the various alternatives for monitored retrievable storage (MRS) and interim storage (IS) of spent nuclear fuel, solidified high-level waste (HLW), and transuranic (TRU) waste until appropriate geologic repository/repositories are available. The objectives of this study are: (1) to develop a preconceptual design for an MRS/IS facility that would become the principal surface facility for a deep geologic repository when the repository is opened, (2) to examine various issues such as transportation of wastes, licensing of the facility, and environmental concerns associated with operation of such a facility, and (3) to estimate the life cycle costs of the facility when operated in response to a set of scenarios which define the quantities and types of waste requiring storage in specific time periods, which generally span the years from 1990 until 2016. The life cycle costs estimated in this study include: the capital expenditures for structures, casks and/or drywells, storage areas and pads, and transfer equipment; the cost of staff labor, supplies, and services; and the incremental cost of transporting the waste materials from the site of origin to the MRS/IS facility. Three scenarios are examined to develop estimates of life cycle costs of the MRS/IS facility. In the first scenario, HLW canisters are stored, starting in 1990, until the co-located repository is opened in the year 1998. Additional reprocessing plants and repositories are placed in service at various intervals. In the second scenario, spent fuel is stored, starting in 1990, because the reprocessing plants are delayed in starting operations by 10 years, but no HLW is stored because the repositories open on schedule. In the third scenario, HLW is stored, starting in 1990, because the repositories are delayed 10 years, but the reprocessing plants open on schedule

Ontology-Based Data Integration of Open Source Electronic Medical Record and Data Capture Systems

Science.gov (United States)

Guidry, Alicia F.

2013-01-01

In low-resource settings, the prioritization of clinical care funding is often determined by immediate health priorities. As a result, investment directed towards the development of standards for clinical data representation and exchange are rare and accordingly, data management systems are often redundant. Open-source systems such as OpenMRS and…

Localization and Recognition of Dynamic Hand Gestures Based on Hierarchy of Manifold Classifiers

Science.gov (United States)

Favorskaya, M.; Nosov, A.; Popov, A.

2015-05-01

Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case). Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset "Multi-modal Gesture Recognition Challenge 2013: Dataset and Results" including 393 dynamic hand-gestures was chosen. The proposed method yielded 84-91% recognition accuracy, in average, for restricted set of dynamic gestures.

LOCALIZATION AND RECOGNITION OF DYNAMIC HAND GESTURES BASED ON HIERARCHY OF MANIFOLD CLASSIFIERS

Directory of Open Access Journals (Sweden)

M. Favorskaya

2015-05-01

Full Text Available Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case. Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset “Multi-modal Gesture Recognition Challenge 2013: Dataset and Results” including 393 dynamic hand-gestures was chosen. The proposed method yielded 84–91% recognition accuracy, in average, for restricted set of dynamic gestures.

ELM BASED CAD SYSTEM TO CLASSIFY MAMMOGRAMS BY THE COMBINATION OF CLBP AND CONTOURLET

Directory of Open Access Journals (Sweden)

S Venkatalakshmi

2017-05-01

Full Text Available Breast cancer is a serious life threat to the womanhood, worldwide. Mammography is the promising screening tool, which can show the abnormality being detected. However, the physicians find it difficult to detect the affected regions, as the size of microcalcifications is very small. Hence it would be better, if a CAD system can accompany the physician in detecting the malicious regions. Taking this as a challenge, this paper presents a CAD system for mammogram classification which is proven to be accurate and reliable. The entire work is decomposed into four different stages and the outcome of a phase is passed as the input of the following phase. Initially, the mammogram is pre-processed by adaptive median filter and the segmentation is done by GHFCM. The features are extracted by combining the texture feature descriptors Completed Local Binary Pattern (CLBP and contourlet to frame the feature sets. In the training phase, Extreme Learning Machine (ELM is trained with the feature sets. During the testing phase, the ELM can classify between normal, malignant and benign type of cancer. The performance of the proposed approach is analysed by varying the classifier, feature extractors and parameters of the feature extractor. From the experimental analysis, it is evident that the proposed work outperforms the analogous techniques in terms of accuracy, sensitivity and specificity.

Preconceptual design for a Monitored Retrievable Storage (MRS) transfer facility

International Nuclear Information System (INIS)

Woods, W.D.; Jowdy, A.K.; Smith, R.I.

1990-09-01

The contract between the DOE and the utilities specifies that the DOE will receive spent fuel from the nuclear utilities in 1998. This study investigates the feasibility of employing a simple Transfer Facility which can be constructed quickly, and operate while the full-scale MRS facilities are being constructed. The Transfer Facility is a hot cell designed only for the purpose of transferring spent fuel assemblies from the Office of Civilian Radioactive Waste Management (OCRWM) transport casks (shipped from the utility sites) into onsite concrete storage casks. No operational functions other than spent fuel assembly transfers and the associated cask handling, opening, and closing would be performed in this facility. Radioactive waste collected in the Transfer Facility during operations would be stored until the treatment facilities in the full-scale MRS facility became operational, approximately 2 years after the Transfer Facility started operation. An alternate wherein the Transfer Facility was the only waste handling building on the MRS site was also examined and evaluated. 6 figs., 26 tabs

Intelligent Garbage Classifier

Directory of Open Access Journals (Sweden)

Ignacio Rodríguez Novelle

2008-12-01

Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.

Automating the construction of scene classifiers for content-based video retrieval

NARCIS (Netherlands)

Khan, L.; Israël, Menno; Petrushin, V.A.; van den Broek, Egon; van der Putten, Peter

2004-01-01

This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a

Retrospective on lessons from 1985 Tennessee MRS attempted siting: The local view

International Nuclear Information System (INIS)

Peelle, E.

1991-01-01

Six years have elapsed since the unsuccessful US Department of Energy (DOE) effort to site a monitored retrievable storage (MRS) facility in Tennessee. The local MRS task force (TF) effort in 1985 has been described extensively in prior publications. These articles described the successful interactions between DOE and the local TF in the context of extensive state and regional opposition. The MRS TF found that while the proposed facility could be safe, many conditions were needed to ensure its long-term safety and to change the net benefit balance from negative to positive. The DOE and the TF reached agreement on many of the TF conditions but were still far apart on others when negotiations ended in 1985. The local governments that had created the TF unanimously accepted its conclusion. The governor of Tennessee eventually vetoed the siting process, and the state sued in 1985 to stop the DOE from continuing its efforts. When Tennessee's legal block was removed in 1987, DOE presented its MRS recommendations to Congress. Two brief periods of activity among the TF and local political leaders included testimony in 1987 before two Congressional committees and before the MRS Study Commission in 1989. This testimony sought those additional conditions for siting the DOE had not given in its negotiations with the TF. The purpose of this paper is to evaluate key points and lessons learned from this path-breaking effort by a local unpaid TF of citizens and elected officials through its entire life until disbanding in 1989

A Prototype SSVEP Based Real Time BCI Gaming System.

Science.gov (United States)

Martišius, Ignas; Damaševičius, Robertas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

A Prototype SSVEP Based Real Time BCI Gaming System

Directory of Open Access Journals (Sweden)

Ignas Martišius

2016-01-01

Full Text Available Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

Training set optimization and classifier performance in a top-down diabetic retinopathy screening system

Science.gov (United States)

Wigdahl, J.; Agurto, C.; Murray, V.; Barriga, S.; Soliz, P.

2013-03-01

Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.

Energy-Efficient Neuromorphic Classifiers.

Science.gov (United States)

Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano

2016-10-01

Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

Studying neonatal bilirubin encephalopathy with conventional MRI, MRS, and DWI

International Nuclear Information System (INIS)

Wang, Xiaoyi; Wu, Wulin; Chineah, Ashley; Liu, Fan; Liao, Weihua; Hou, Bob L.; Zhang, Ping

2008-01-01

The purpose of this study was to evaluate the diagnostic value of conventional magnetic resonance imaging (MRI), proton magnetic resonance spectroscopy ( 1 H-MRS), and diffusion-weighted imaging (DWI) for neonatal bilirubin encephalopathy. We collected conventional MRI in 24 neonates with neonatal bilirubin encephalopathy. We performed 1 H-MRS and DWI sequences to nine of the 24 patients and seven age-matched healthy control subjects. Multiple-voxel 1 H-MRS data were acquired using PRESS pulse sequence with TE=135 ms and TR=1500 ms. The spectroscopic regions of interest were the bilateral basal ganglia and thalamus with a 1.0 mL spatial resolution. The data from DWI were collected by using a single shot-spin echo-echo planar imaging sequence with TR/TE: 2900/98, and imaging regions were also focused on the bilateral basal ganglia and thalamus. Nineteen of the 24 patients had abnormal T 1 -weighted image hyperintensity in the globus pallidus, but these lesions appeared as normal T 2 -weighted image intensity in the same region. Ten of the 24 patients had T 1 -weighted image high signal intensity in the subthalamic nucleus and appeared as normal intensity in the region for the T 2 -weighted images. The peak area ratios of NAA/Cho and NAA/Cr were significantly decreased (t-test, P 1 H-MRS are important complementary tools in the diagnosis of neonatal bilirubin encephalopathy. The study provides important information for applying these MR modalities to evaluate neonates with bilirubin encephalopathy. (orig.)

Classifying Sluice Occurrences in Dialogue

DEFF Research Database (Denmark)

Baird, Austin; Hamza, Anissa; Hardt, Daniel

2018-01-01

perform manual annotation with acceptable inter-coder agreement. We build classifier models with Decision Trees and Naive Bayes, with accuracy of 67%. We deploy a classifier to automatically classify sluice occurrences in OpenSubtitles, resulting in a corpus with 1.7 million occurrences. This will support....... Despite this, the corpus can be of great use in research on sluicing and development of systems, and we are making the corpus freely available on request. Furthermore, we are in the process of improving the accuracy of sluice identification and annotation for the purpose of created a subsequent version...

Learning classifier systems with memory condition to solve non-Markov problems

OpenAIRE

Zang, Zhaoxiang; Li, Dehua; Wang, Junying

2012-01-01

In the family of Learning Classifier Systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, where the optimal action is determined solely by the state of current sensory input. In practice, most environments are partially observable environments on agent's sensation, which are also known as non-Markov environments. Within these environments, XCS either fail...

Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

Directory of Open Access Journals (Sweden)

Chia-Hung Lin

2010-01-01

Full Text Available This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD from a two-dimensional (2D image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Comparisons of 13NH3, 18FDG PET and MRS in the presurgical evaluation of intractable epilepsy

International Nuclear Information System (INIS)

Cai Li; Gao Shuo; Li Dacheng; Li Zugui

2004-01-01

Purpose: Surgery offers a high chance of seizure-free outcome in patients with intractable epilepsy. Other than EEG, several functional and morphologic imaging Methods are used to define the spatial seizure origin. Blood flow perfusion and metabolic abnormalities in those patients are well described respectively. Proton MR spectroscopy (MRS) is still in the early stages in the evaluation of epilepsy. Comparisons with 13NH3 perfusion, 18FDG metabolic PET imaging and MRS in the same patients have rarely been documented. The present study was undertaken to compare the merits of 13NH3 PET, 18FDG PET, magnetic resonance imaging (MRI) and MRS for the lateralization of seizure foci. Methods: Preoperative long-term-EEG, Video-EEG, 13NH3 perfusion PET, 18FDG metabolic PET, MRI, MRS and neuropsychological assessment were performed in 15 patients with intractable epilepsy within 2 weeks(mean age=24.8 years, range 4 to 44 years; mean epilepsy duration=11 years, range 2 to 36 years), who received electrocorticography (ECoG). Antiepileptic drug (AED) was stopped taking at least 2 days before PET scanning. 13NH3 and FDG PET was performed in one day and analyzed with a region of interest template. An absolute asymmetry index, |AI|, greater than 0.15 was considered abnormal. 13 subjects were underwent MRS obtained from the hippocampus bilaterally, who had a presumptive temporal seizure focus based on seizure semiology, video-EEG and MRI. Metabolite ratio of NAA/Cho+Cr was calculated from the relative peak height measurements. An NAA/Cho+Cr ratio of 0.72 or less was regarded as abnormal. All the examination Results were compared with EcoG to evaluate their values of seizure foci lateraliaztion. Results: 1. The results were divided into ictal (n=4) and interictal (n= 11) groups. In the ictal group, the sensitivity of 13NH3 PET and 18FDG PET were both 100%(4/4), and 13NH3 PET showed bilateral hippocampus hyperfusion foci in one case. In the interictal group, 13NH3 PET correctly

SVM Classifiers: The Objects Identification on the Base of Their Hyperspectral Features

Directory of Open Access Journals (Sweden)

Demidova Liliya

2017-01-01

Full Text Available The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

New approach to information fusion for Lipschitz classifiers ensembles: Application in multi-channel C-OTDR-monitoring systems

Energy Technology Data Exchange (ETDEWEB)

Timofeev, Andrey V.; Egorov, Dmitry V. [LPP “EqualiZoom”, Astana, 010000 (Kazakhstan)

2016-06-08

This paper presents new results concerning selection of an optimal information fusion formula for an ensemble of Lipschitz classifiers. The goal of information fusion is to create an integral classificatory which could provide better generalization ability of the ensemble while achieving a practically acceptable level of effectiveness. The problem of information fusion is very relevant for data processing in multi-channel C-OTDR-monitoring systems. In this case we have to effectively classify targeted events which appear in the vicinity of the monitored object. Solution of this problem is based on usage of an ensemble of Lipschitz classifiers each of which corresponds to a respective channel. We suggest a brand new method for information fusion in case of ensemble of Lipschitz classifiers. This method is called “The Weighing of Inversely as Lipschitz Constants” (WILC). Results of WILC-method practical usage in multichannel C-OTDR monitoring systems are presented.

MRI and MRS on preserved samples as a tool in fish ecology.

Science.gov (United States)

Bock, Christian; Wermter, Felizitas C; Mintenbeck, Katja

2017-05-01

Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) gain increasing attention and importance as a tool in marine ecology. So far, studies were largely limited to morphological studies, e.g. for the creation of digital libraries. Here, the utility of MRI and MRS for ecologists is tested and exemplified using formalin preserved samples of the Antarctic silverfish, Pleuragramma antarctica. As this species lacks a swim bladder, buoyancy is attained by the deposition of large amounts of lipids that are mainly stored in subcutaneous and intermuscular lipid sacs. In this study MRI and MRS are not only used to study internal morphology, but additionally to investigate functional morphology and to measure parameters of high ecological interest. The data are compared with literature data obtained by means of traditional ecological methods. The results from this study show that MR scans are not only an alternative to histological sections (as shown before), but even allow the visualization of particular features in delicate soft tissues, such as Pleuragramma's lipid sacs. 3D rendering techniques proved to be a useful tool to study organ volumes and lipid content, which usually requires laborious chemical lipid extraction and analysis. Moreover, the application of MRS even allows for an analysis of lipids and fatty acids within lipid sacs, which wouldn't be possible using destructive methods. MRI and MRS, in particular when used in combination, have the capacity to provide useful data on parameters of high ecological relevance and thus have proven to be a highly valuable addition, if not alternative, to the classical methods. Copyright © 2016 Elsevier Inc. All rights reserved.

Tolerance to missing data using a likelihood ratio based classifier for computer-aided classification of breast cancer

International Nuclear Information System (INIS)

Bilska-Wolak, Anna O; Floyd, Carey E Jr

2004-01-01

While mammography is a highly sensitive method for detecting breast tumours, its ability to differentiate between malignant and benign lesions is low, which may result in as many as 70% of unnecessary biopsies. The purpose of this study was to develop a highly specific computer-aided diagnosis algorithm to improve classification of mammographic masses. A classifier based on the likelihood ratio was developed to accommodate cases with missing data. Data for development included 671 biopsy cases (245 malignant), with biopsy-proved outcome. Sixteen features based on the BI-RADS TM lexicon and patient history had been recorded for the cases, with 1.3 ± 1.1 missing feature values per case. Classifier evaluation methods included receiver operating characteristic and leave-one-out bootstrap sampling. The classifier achieved 32% specificity at 100% sensitivity on the 671 cases with 16 features that had missing values. Utilizing just the seven features present for all cases resulted in decreased performance at 100% sensitivity with average 19% specificity. No cases and no feature data were omitted during classifier development, showing that it is more beneficial to utilize cases with missing values than to discard incomplete cases that cannot be handled by many algorithms. Classification of mammographic masses was commendable at high sensitivity levels, indicating that benign cases could be potentially spared from biopsy

Class-specific Error Bounds for Ensemble Classifiers

Energy Technology Data Exchange (ETDEWEB)

Prenger, R; Lemmond, T; Varshney, K; Chen, B; Hanley, W

2009-10-06

The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missed detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.

Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support

International Nuclear Information System (INIS)

Mazurowski, Maciej A; Tourassi, Georgia D; Malof, Jordan M

2011-01-01

When constructing a pattern classifier, it is important to make best use of the instances (a.k.a. cases, examples, patterns or prototypes) available for its development. In this paper we present an extensive comparative analysis of algorithms that, given a pool of previously acquired instances, attempt to select those that will be the most effective to construct an instance-based classifier in terms of classification performance, time efficiency and storage requirements. We evaluate seven previously proposed instance selection algorithms and compare their performance to simple random selection of instances. We perform the evaluation using k-nearest neighbor classifier and three classification problems: one with simulated Gaussian data and two based on clinical databases for breast cancer detection and diagnosis, respectively. Finally, we evaluate the impact of the number of instances available for selection on the performance of the selection algorithms and conduct initial analysis of the selected instances. The experiments show that for all investigated classification problems, it was possible to reduce the size of the original development dataset to less than 3% of its initial size while maintaining or improving the classification performance. Random mutation hill climbing emerges as the superior selection algorithm. Furthermore, we show that some previously proposed algorithms perform worse than random selection. Regarding the impact of the number of instances available for the classifier development on the performance of the selection algorithms, we confirm that the selection algorithms are generally more effective as the pool of available instances increases. In conclusion, instance selection is generally beneficial for instance-based classifiers as it can improve their performance, reduce their storage requirements and improve their response time. However, choosing the right selection algorithm is crucial.

RUGs and "Medi-Cal" systems for classifying nursing home patients.

Science.gov (United States)

Grimaldi, P L

1985-12-01

Medicare and most state Medicaid programs currently use indirect case-mix measures to determine reimbursement for nursing home care. In the future, however, they probably will incorporate more direct case-mix measures into their payment systems. Care must be exercised in designing a case-based prospective payment system to ensure that its financial incentives motivate providers to expedite recovery, prevent deterioration, and admit heavy-care patients. For example, although use of a services-rendered approach helps guarantee that care will be provided when needed, it also offers providers an incentive to furnish a service regardless of whether it is in the patient's best interest. Consideration must be given to the frequency with which patients are reassessed. The implications of the timing of reassessments for quality of care also must be studied. Ideally, quality would be measured on an outcome basis--that is, payment would depend on whether targeted goals for individual patients are reached--rather than on structural or process measures alone. Two recent classification systems--Resource Utilization Groups and Medi-Cal groups--may serve as models for case-based prospective payment systems. Each method classifies patients into distinct, meaningful categories based on activities of daily living and services received.

Monitored Retrievable Storage (MRS) facility project status

International Nuclear Information System (INIS)

Milner, R.A.; Trebules, V.W.; Blandford, J.B.

1994-01-01

1993 has been yet another year of major change in the Monitored Retrievable Storage (MRS) project. The change in administration has brought a new Secretary of Energy to the Department. Secretary O'Leary has brought a strong leadership background and fresh ideas to address the Department's many complex challenges, including the Civilian Radioactive Waste Management System (CRWMS). Dr. Daniel Dreyfus was named Director of the Office of Civilian Radioactive Waste Management. Mr. Richard Stallings has been named, as the new, Nuclear Waste Negotiator under the Nuclear Waste Policy Act, Amendments of 1987. The overall mission of the Office of Civilian Radioactive Waste Management (OCRWM) has not changed. OCRWM is tasked with finding technically sound, environmentally responsible and economically viable solutions to spent nuclear fuel and high-level radioactive waste storage and disposal

Magnetic resonance spectroscopy (MRS) of vertebral column – an additional tool for evaluation of aggressiveness of vertebral haemangioma like lesion

International Nuclear Information System (INIS)

Jeromel, Miran; Podobnik, Janez

2014-01-01

Most vertebral haemangioma are asymptomatic and discovered incidentally. Sometimes the symptomatic lesions present with radiological signs of aggressiveness and their appearance resemble other aggressive lesions (e.g. solitary plasmacytoma). We present a patient with large symptomatic aggressive haemangioma like lesion in 12 th thoracic vertebra in which a magnetic resonance spectroscopy (MRS) was used to analyse fat content within the lesion. The lesion in affected vertebrae showed low fat content with 33% of fat fraction (%FF). The fat content in non-affected (1 st lumbar) vertebra was as expected for patient’s age (68%). Based on MRS data, the lesion was characterized as an aggressive haemangioma. The diagnosis was confirmed with biopsy, performed during the treatment – percutaneous vertebroplasty. The presented case shows that MRS can be used as an additional tool for evaluation of aggressiveness of vertebral haemangioma like lesions

Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform

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Syed Tahir Hussain Rizvi

2017-10-01

Full Text Available The realization of a deep neural architecture on a mobile platform is challenging, but can open up a number of possibilities for visual analysis applications. A neural network can be realized on a mobile platform by exploiting the computational power of the embedded GPU and simplifying the flow of a neural architecture trained on the desktop workstation or a GPU server. This paper presents an embedded platform-based Italian license plate detection and recognition system using deep neural classifiers. In this work, trained parameters of a highly precise automatic license plate recognition (ALPR system are imported and used to replicate the same neural classifiers on a Nvidia Shield K1 tablet. A CUDA-based framework is used to realize these neural networks. The flow of the trained architecture is simplified to perform the license plate recognition in real-time. Results show that the tasks of plate and character detection and localization can be performed in real-time on a mobile platform by simplifying the flow of the trained architecture. However, the accuracy of the simplified architecture would be decreased accordingly.

Using Neural Networks to Classify Digitized Images of Galaxies

Science.gov (United States)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

Integral Monitored Retrievable Storage (MRS) Facility conceptual design report

International Nuclear Information System (INIS)

1985-09-01

This document, Volume 5 Book 7, contains cost estimate information for a monitored retrievable storage (MRS) facility. Cost estimates are for onsite improvements, waste storage, and offsite improvements for the Clinch River Site

Correlation between MRS and serum PSA in the diagnosis of local recurrence after radical prostatectomy

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Ghafuri M

2012-08-01

Full Text Available Background: Multifocality, multicentricity and extension beyond the prostate capsule are all characteristics of prostatic adenocarcinoma that may escape diagnosis by conventional CT scanning or MRI. This study was designed to assess the diagnostic value of magnetic resonance spectroscopy (MRS in prostatic carcinoma and its compatibility with prostatic specific antigen (PSA as the conventional method.Methods: In this cross-sectional study, we recruited 139 patients with previous radical prostatectomy referring to Radiology department of Hazrate-e-Rasul Hospital during the first half of 2011 for the evaluation of local recurrence. Traditionally, local recurrence is defined as serum PSA concentration >0.2 ng/dl. We used 1.5-tesla Siemens Avanto MRI unit with endorectal coil and measured creatine, choline and citrate levels before calculating choline-creatine/citrate ratio. Correlation between MRS findings with PSA concentration was evaluated in regards to the multiple levels of the previously mentioned ratio.Results: Local recurrence was found in 107 (77% patients based on PSA levels. The mean values for serum PSA levels and creatine-choline/citrate ratio were significantly different in patients with and without local recurrence. Creatine-choline/citrate ratios greater than 50, 100 and 150 (as different cut-off points of recurrence were respectively seen in 104, 102 and 97 patients and agreement ratio between MRS and PSA in these levels were 94.1%, 94.4% and 85.1%, respectively. Correlation coefficient between these two methods was 0.481.Conclusion: MRS is a valuable tool for evaluating recurrence inpatients with prostate cancer treated by radical prostatectomy and it is in good agreement with serum PSA levels.

Using decision-tree classifier systems to extract knowledge from databases

Science.gov (United States)

St.clair, D. C.; Sabharwal, C. L.; Hacke, Keith; Bond, W. E.

1990-01-01

One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described.

Three data partitioning strategies for building local classifiers (Chapter 14)

NARCIS (Netherlands)

Zliobaite, I.; Okun, O.; Valentini, G.; Re, M.

2011-01-01

Divide-and-conquer approach has been recognized in multiple classifier systems aiming to utilize local expertise of individual classifiers. In this study we experimentally investigate three strategies for building local classifiers that are based on different routines of sampling data for training.

Development of FARICH detector for particle identification system at accelerators

Science.gov (United States)

Finogeev, D. A.; Kurepin, A. B.; Razin, V. I.; Reshetin, A. I.; Usenko, E. A.; Barnyakov, A. Yu.; Barnyakov, M. Yu.; Bobrovnikov, V. S.; Buzykaev, A. R.; Kasyanenko, P. V.; Kononov, S. A.; Kravchenko, E. A.; Kuyanov, I. A.; Onuchin, A. P.; Ovtin, I. V.; Podgornov, N. A.; Talyshev, A. A.; Danilyuk, A. F.

2018-01-01

Aerogel has been successfully used as a radiator in Cherenkov detectors. In 2004, a multilayer aerogel providing Cherenkov ring focusing was proposed and produced. FARICH (Focusing Aerogel Rich Imaging CHerenkov) detectors such as ARICH for Belle-II (KEK, Japan), Forward RICH for PANDA detector (FAIR, Germany), and FARICH for the Super Charm-Tau factory project (BINP, Novosibirsk) have been developed based on this aerogel. Prototypes of FARICH detector based on MRS APD and Philips DPC photosensors were developed and tested in the framework of this project. An angular resolution for Cherenkov rings of 3.6 mrad was achieved.

Current Directional Protection of Series Compensated Line Using Intelligent Classifier

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M. Mollanezhad Heydarabadi

2016-12-01

Full Text Available Current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. Application of the intelligent system for fault direction classification has been suggested in this paper. A new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. The proposed classifier uses only half cycle of pre-fault and post fault current samples at relay location to feed the classifier. A lot of forward and backward fault simulations under different system conditions upon a transmission line with a fixed series capacitor are carried out using PSCAD/EMTDC software. The applicability of decision tree (DT, probabilistic neural network (PNN and support vector machine (SVM are investigated using simulated data under different system conditions. The performance comparison of the classifiers indicates that the SVM is a best suitable classifier for fault direction discriminating. The backward faults can be accurately distinguished from forward faults even under current inversion without require to detect of the current inversion condition.

Rebuilding the injured brain: use of MRS in clinical regenerative medicine

Science.gov (United States)

Zare, Alina; Weiss, Michael; Gader, Paul

2011-03-01

Hypoxic-Ischemic Encephalopathy (HIE) is the brain manifestation of systemic asphyxia that occurs in 20 out of 1000 births. HIE triggers an immediate neuronal and glial injury leading to necrosis secondary to cellular edema and lysis. Because of this destructive neuronal injury, up to 25% of neonates exhibit severe permanent neuropsychological handicaps in the form of cerebral palsy, with or without associated mental retardation, learning disabilities, or epilepsy. Due to the devastating consequences of HIE, much research has focused on interrupting the cascade of events triggered by HIE. To date, none of these therapies, with the exception of hypothermia, have been successful in the clinical environment. Even in the case of hypothermia, only neonates with mild to moderate HIE respond to therapy. Stem cell therapy offers an attractive potential treatment for HIE. The ability to replace necrotic cells with functional cells could limit the degree of long-term neurological deficits. The neonatal brain offers a unique milieu for stem cell therapy due to its overall plasticity and the continued division of cells in the sub-ventricular zones. New powerful imaging tools allow researchers to track stem cells in vivo post-transplant, as shown in Figure 1. However, neuroimaging still leaves numerous questions unresolved: How can we identify stem cells without using tracking agents, what cells types are destroyed in the brain post injury? What is the final phenotypic fate of transplanted cells? Are the transplanted cells still viable? Do the transplanted cells spare endogenous neuronal tissue? We hypothesize that magnetic resonance spectroscopy (MRS), a broadly used clinical technique that can be performed at the time of a standard MRI scan, can provide answers to these questions when coupled with advanced computational approaches. MRS is widely available clinically, and is a relative measure of different metabolites within the sampled area. These measures are presented as a

MRS2016: Rigid Moon Rotation Series in the Relativistic Approximation

Science.gov (United States)

Pashkevich, V. V.

2017-03-01

The rigid Moon rotation problem is studied for the relativistic (kinematical) case, in which the geodetic perturbations in the Moon rotation are taken into account. As the result of this research the high-precision Moon Rotation Series MRS2016 in the relativistic approximation was constructed for the first time and the discrepancies between the high-precision numerical and the semi-analytical solutions of the rigid Moon rotation were investigated with respect to the fixed ecliptic of epoch J2000, by the numerical and analytical methods. The residuals between the numerical solution and MRS2016 in the perturbing terms of the physical librations do not exceed 80 mas and 10 arc seconds over 2000 and 6000 years, respectively.

Intelligent Recognition of Lung Nodule Combining Rule-based and C-SVM Classifiers

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Bin Li

2012-02-01

Full Text Available Computer-aided detection(CAD system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and cost-sensitive support vector machine(C-SVM classifiers is proposed for detecting both solid nodules and ground-glass opacity(GGO nodules(part solid and nonsolid. This method consists of several steps. Firstly, segmentation of regions of interest(ROIs, including pulmonary parenchyma and lung nodule candidates, is a difficult task. On one side, the presence of noise lowers the visibility of low-contrast objects. On the other side, different types of nodules, including small nodules, nodules connecting to vasculature or other structures, part-solid or nonsolid nodules, are complex, noisy, weak edge or difficult to define the boundary. In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentation method is proposed, they are: the lung parenchyma is extracted based on threshold and morphologic segmentation method; the image denoising and enhancing is realized by nonlinear anisotropic diffusion filtering(NADF method; candidate pulmonary nodules are segmented by the improved C-V level set method, in which the segmentation result of EM-based fuzzy threshold method is used as the initial contour of active contour model and a constrained energy term is added into the PDE of level set function. Then, lung nodules are classified by using the intelligent classifiers combining rules and C-SVM. Rule-based classification is first used to remove easily dismissible nonnodule objects, then C-SVM classification are used to further classify nodule candidates and reduce the number of false positive(FP objects. In order to increase the efficiency of SVM, an improved training method is used to train SVM, which uses the grid search method to search the optimal

Intelligent Recognition of Lung Nodule Combining Rule-based and C-SVM Classifiers

Directory of Open Access Journals (Sweden)

Bin Li

2011-10-01

Full Text Available Computer-aided detection(CAD system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and costsensitive support vector machine(C-SVM classifiers is proposed for detecting both solid nodules and ground-glass opacity(GGO nodules(part solid and nonsolid. This method consists of several steps. Firstly, segmentation of regions of interest(ROIs, including pulmonary parenchyma and lung nodule candidates, is a difficult task. On one side, the presence of noise lowers the visibility of low-contrast objects. On the other side, different types of nodules, including small nodules, nodules connecting to vasculature or other structures, part-solid or nonsolid nodules, are complex, noisy, weak edge or difficult to define the boundary. In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentation method is proposed, they are: the lung parenchyma is extracted based on threshold and morphologic segmentation method; the image denoising and enhancing is realized by nonlinear anisotropic diffusion filtering(NADF method;candidate pulmonary nodules are segmented by the improved C-V level set method, in which the segmentation result of EM-based fuzzy threshold method is used as the initial contour of active contour model and a constrained energy term is added into the PDE of level set function. Then, lung nodules are classified by using the intelligent classifiers combining rules and C-SVM. Rule-based classification is first used to remove easily dismissible nonnodule objects, then C-SVM classification are used to further classify nodule candidates and reduce the number of false positive(FP objects. In order to increase the efficiency of SVM, an improved training method is used to train SVM, which uses the grid search method to search the optimal parameters

Magnetic Resonance Imaging (MRI and Spectroscopy (MRS in Breast Cancer

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Uma Sharma

2008-01-01

Full Text Available Breast cancer is a major health problem in women and early detection is of prime importance. Breast magnetic resonance imaging (MRI provides both physical and physiologic tissue features that are useful in discriminating malignant from benign lesions. Contrast enhanced MRI is valuable for diagnosis of small tumors in dense breast and the structural and kinetic parameters improved the specificity of diagnosing benign from malignant lesions. It is a complimentary modality for preoperative staging, to follow response to therapy, to detect recurrences and for screening high risk women. Diffusion, perfusion and MR elastography have been applied to breast lesion characterization and show promise.In-vivo MR spectroscopy (MRS is a valuable method to obtain the biochemical status of normal and diseased tissues. Malignant tissues contain high concentration of choline containing compounds that can be used as a biochemical marker. MRS helps to increase the specificity of MRI in lesions larger than 1cm and to monitor the tumor response. Various MR techniques show promise primarily as adjunct to the existing standard detection techniques, and its acceptability as a screening method will increase if specificity can be improved. This review presents the progress made in different MRI and MRS techniques in breast cancer management.

Magnetic Resonance Imaging (MRI and Spectroscopy (MRS in Breast Cancer

Directory of Open Access Journals (Sweden)

Uma Sharma

2008-01-01

Full Text Available Breast cancer is a major health problem in women and early detection is of prime importance. Breast magnetic resonance imaging (MRI provides both physical and physiologic tissue features that are useful in discriminating malignant from benign lesions. Contrast enhanced MRI is valuable for diagnosis of small tumors in dense breast and the structural and kinetic parameters improved the specificity of diagnosing benign from malignant lesions. It is a complimentary modality for preoperative staging, to follow response to therapy, to detect recurrences and for screening high risk women. Diffusion, perfusion and MR elastography have been applied to breast lesion characterization and show promise. In-vivo MR spectroscopy (MRS is a valuable method to obtain the biochemical status of normal and diseased tissues. Malignant tissues contain high concentration of choline containing compounds that can be used as a biochemical marker. MRS helps to increase the specificity of MRI in lesions larger than 1cm and to monitor the tumor response. Various MR techniques show promise primarily as adjunct to the existing standard detection techniques, and its acceptability as a screening method will increase if specificity can be improved. This review presents the progress made in different MRI and MRS techniques in beast cancer management.

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

Science.gov (United States)

Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin

2017-01-01

Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

In vivo hepatic lipid quantification using MRS at 7 Tesla in a mouse model of glycogen storage disease type 1a.

Science.gov (United States)

Ramamonjisoa, Nirilanto; Ratiney, Helene; Mutel, Elodie; Guillou, Herve; Mithieux, Gilles; Pilleul, Frank; Rajas, Fabienne; Beuf, Olivier; Cavassila, Sophie

2013-07-01

The assessment of liver lipid content and composition is needed in preclinical research to investigate steatosis and steatosis-related disorders. The purpose of this study was to quantify in vivo hepatic fatty acid content and composition using a method based on short echo time proton magnetic resonance spectroscopy (MRS) at 7 Tesla. A mouse model of glycogen storage disease type 1a with inducible liver-specific deletion of the glucose-6-phosphatase gene (L-G6pc(-/-)) mice and control mice were fed a standard diet or a high-fat/high-sucrose (HF/HS) diet for 9 months. In control mice, hepatic lipid content was found significantly higher with the HF/HS diet than with the standard diet. As expected, hepatic lipid content was already elevated in L-G6pc(-/-) mice fed a standard diet compared with control mice. L-G6pc(-/-) mice rapidly developed steatosis which was not modified by the HF/HS diet. On the standard diet, estimated amplitudes from olefinic protons were found significantly higher in L-G6pc(-/-) mice compared with that in control mice. L-G6pc(-/-) mice showed no noticeable polyunsaturation from diallylic protons. Total unsaturated fatty acid indexes measured by gas chromatography were in agreement with MRS measurements. These results showed the great potential of high magnetic field MRS to follow the diet impact and lipid alterations in mouse liver.

Correlation Dimension-Based Classifier

Czech Academy of Sciences Publication Activity Database

Jiřina, Marcel; Jiřina jr., M.

2014-01-01

Roč. 44, č. 12 (2014), s. 2253-2263 ISSN 2168-2267 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : classifier * multidimensional data * correlation dimension * scaling exponent * polynomial expansion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.469, year: 2014

A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation.

Science.gov (United States)

Tseng, Yi-Ju; Wu, Jung-Hsuan; Lin, Hui-Chi; Chen, Ming-Yuan; Ping, Xiao-Ou; Sun, Chun-Chuan; Shang, Rung-Ji; Sheng, Wang-Huei; Chen, Yee-Chun; Lai, Feipei; Chang, Shan-Chwen

2015-09-21

Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 Psystem performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system.

The development of component-based information systems

CERN Document Server

Cesare, Sergio de; Macredie, Robert

2015-01-01

This work provides a comprehensive overview of research and practical issues relating to component-based development information systems (CBIS). Spanning the organizational, developmental, and technical aspects of the subject, the original research included here provides fresh insights into successful CBIS technology and application. Part I covers component-based development methodologies and system architectures. Part II analyzes different aspects of managing component-based development. Part III investigates component-based development versus commercial off-the-shelf products (COTS), includi

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Directory of Open Access Journals (Sweden)

Ting Shu

2017-01-01

Full Text Available At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

The Truth and Reconciliation Commission and gender: The Testimony of Mrs Konile revisited

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Sandiswa L. Kobe

2017-12-01

Full Text Available This article draws on a well-known narration of the Gugulethu Seven incident from the Truth and Reconciliation Commission (TRC proceedings with specific reference to testimonies of the mothers of the Gugulethu Seven. The article focuses on Mrs Konile’s testimony as a case study: Testimony of a black woman whose son was murdered by the apartheid government’s security forces. During the TRC hearings, Mrs Konile ‘failed’ to effectively narrate her story, which resulted in her testimony being dismissed as being incoherent. This article examines the underlying attributes of Mrs Konile’s testimony and revisits why she was considered ‘incapable’ of articulating her experience in a convincing manner. The analysis aims to acknowledge, identify and give insights about this woman’s testimony from an African women theologian viewpoint (specifically with references to the Isixhosa religious cultural background.

Phosphorus MRS study in bone and soft-tissue tumors

International Nuclear Information System (INIS)

Du Xiangke; Jiang Baoguo

2000-01-01

Objective: To study the metabolite changes in bone and soft-tissue tumors using phosphorus MRS for better understanding of the phospholipid metabolite and energy metabolite of tumors, which will provide more information for clinical diagnosis and therapy. Methods: Phosphorus MRS and MRI were performed in 14 bone and soft-tissue tumor patients (benign 6, malignant 8) and 19 healthy volunteers at 2.0 T. The areas under the peak of various metabolite in spectra were measured. The ratios of the other metabolite related to β-ATP, ATP, and Pcr were calculated. Intracellular pH was calculated according to the chemical shift change of Pi relative to Pcr. Results: The ratio of PME/β-ATP, PME/ATP, Pcr/PME in both benign and malignant group, intracellular pH in malignant group and LEP/Pcr in benign group were higher than that of the normal group significantly (P < 0.01). the ratios of Pi/Pcr in benign and malignant group, PDE/ATP, PDE/β-ATP, LET/Pcr, Pi/β-ATP in malignant group and LET/β-ATP in benign group were significantly different from that of the normal group (P < 0.05). Between benign and malignant tumors group, the ratios of Pcr/PME and Intracellular pH were different significantly (P < 0.05). Conclusion: The in vivo phosphorus MRS can non-invasively find abnormal phospholipid metabolite, energy metabolite and pH changes in bone and soft tissue tumors

Efficient full decay inversion of MRS data with a stretched-exponential approximation of the ? distribution

Science.gov (United States)

Behroozmand, Ahmad A.; Auken, Esben; Fiandaca, Gianluca; Christiansen, Anders Vest; Christensen, Niels B.

2012-08-01

We present a new, efficient and accurate forward modelling and inversion scheme for magnetic resonance sounding (MRS) data. MRS, also called surface-nuclear magnetic resonance (surface-NMR), is the only non-invasive geophysical technique that directly detects free water in the subsurface. Based on the physical principle of NMR, protons of the water molecules in the subsurface are excited at a specific frequency, and the superposition of signals from all protons within the excited earth volume is measured to estimate the subsurface water content and other hydrological parameters. In this paper, a new inversion scheme is presented in which the entire data set is used, and multi-exponential behaviour of the NMR signal is approximated by the simple stretched-exponential approach. Compared to the mono-exponential interpretation of the decaying NMR signal, we introduce a single extra parameter, the stretching exponent, which helps describe the porosity in terms of a single relaxation time parameter, and helps to determine correct initial amplitude and relaxation time of the signal. Moreover, compared to a multi-exponential interpretation of the MRS data, the decay behaviour is approximated with considerably fewer parameters. The forward response is calculated in an efficient numerical manner in terms of magnetic field calculation, discretization and integration schemes, which allows fast computation while maintaining accuracy. A piecewise linear transmitter loop is considered for electromagnetic modelling of conductivities in the layered half-space providing electromagnetic modelling of arbitrary loop shapes. The decaying signal is integrated over time windows, called gates, which increases the signal-to-noise ratio, particularly at late times, and the data vector is described with a minimum number of samples, that is, gates. The accuracy of the forward response is investigated by comparing a MRS forward response with responses from three other approaches outlining

Resting functional imaging tools (MRS, SPECT, PET and PCT)

NARCIS (Netherlands)

van der Naalt, Joukje; Grafman, Jordan; Salazar, Andres M

2015-01-01

Functional imaging includes imaging techniques that provide information about the metabolic and hemodynamic status of the brain. Most commonly applied functional imaging techniques in patients with traumatic brain injury (TBI) include magnetic resonance spectroscopy (MRS), single photon emission

Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier

Science.gov (United States)

Rosdi, Bakhtiar Affendi; Jaafar, Haryati; Ramli, Dzati Athiar

2015-02-01

In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.

MR spectroscopy (MRS) and magnetisation transfer imaging (MTI), lesion load and clinical scores in early relapsing remitting multiple sclerosis: a combined cross-sectional and longitudinal study

International Nuclear Information System (INIS)

Bellmann-Strobl, J.; Paul, F.; Aktas, O.; Zipp, F.; Stiepani, H.; Bohner, G.; Klingebiel, R.; Wuerfel, J.; Warmuth, C.; Wandinger, K.P.

2009-01-01

The purpose of this study was to correlate magnetic resonance imaging (MRI)-based lesion load assessment with clinical disability in early relapsing remitting multiple sclerosis (RRMS). Seventeen untreated patients (ten women, seven men; mean age 33.0±7.9 years) with the initial diagnosis of RRMS were included for cross-sectional as well as longitudinal (24 months) clinical and MRI-based assessment in comparison with age-matched healthy controls. Conventional MR sequences, MR spectroscopy (MRS) and magnetisation transfer imaging (MTI) were performed at 1.5 T. Lesion number and volume, MRS and MTI measurements for lesions and normal appearing white matter (NAWM) were correlated to clinical scores [Expanded Disability Status Scale (EDSS), Multiple Sclerosis Functional Composite (MSFC)] for monitoring disease course after treatment initiation (interferon β-1a). MTI and MRS detected changes [magnetisation transfer ratio (MTR), N-acetylaspartate (NAA)/creatine ratio] in NAWM over time. EDSS and lesional MTR increases correlated throughout the disease course. Average MTR of NAWM raised during the study (p<0.05) and correlated to the MSFC score (r=0.476, p<0.001). At study termination, NAA/creatine ratio of NAWM correlated to the MSFC score (p<0.05). MTI and MRS were useful for initial disease assessment in NAWM. MTI and MRS correlated with clinical scores, indicating potential for monitoring the disease course and gaining new insights into treatment-related effects. (orig.)

MR spectroscopy (MRS) and magnetisation transfer imaging (MTI), lesion load and clinical scores in early relapsing remitting multiple sclerosis: a combined cross-sectional and longitudinal study

Energy Technology Data Exchange (ETDEWEB)

Bellmann-Strobl, J.; Paul, F.; Aktas, O.; Zipp, F. [Charite - University Medicine Berlin and Max Delbrueck Center for Molecular Medicine, Cecilie Vogt Clinic for Neurology, Berlin (Germany); Stiepani, H.; Bohner, G.; Klingebiel, R. [Charite - University Medicine Berlin, Department of Neuroradiology, Berlin (Germany); Wuerfel, J. [Charite - University Medicine Berlin and Max Delbrueck Center for Molecular Medicine, Cecilie Vogt Clinic for Neurology, Berlin (Germany); University Schleswig-Holstein, Institute of Neuroradiology, Campus Luebeck, Kiel (Germany); Warmuth, C. [Charite - University Medicine Berlin, Department of Radiology, Berlin (Germany); Wandinger, K.P. [Charite - University Medicine Berlin, Department of Neurology, Berlin (Germany)

2009-08-15

The purpose of this study was to correlate magnetic resonance imaging (MRI)-based lesion load assessment with clinical disability in early relapsing remitting multiple sclerosis (RRMS). Seventeen untreated patients (ten women, seven men; mean age 33.0{+-}7.9 years) with the initial diagnosis of RRMS were included for cross-sectional as well as longitudinal (24 months) clinical and MRI-based assessment in comparison with age-matched healthy controls. Conventional MR sequences, MR spectroscopy (MRS) and magnetisation transfer imaging (MTI) were performed at 1.5 T. Lesion number and volume, MRS and MTI measurements for lesions and normal appearing white matter (NAWM) were correlated to clinical scores [Expanded Disability Status Scale (EDSS), Multiple Sclerosis Functional Composite (MSFC)] for monitoring disease course after treatment initiation (interferon {beta}-1a). MTI and MRS detected changes [magnetisation transfer ratio (MTR), N-acetylaspartate (NAA)/creatine ratio] in NAWM over time. EDSS and lesional MTR increases correlated throughout the disease course. Average MTR of NAWM raised during the study (p<0.05) and correlated to the MSFC score (r=0.476, p<0.001). At study termination, NAA/creatine ratio of NAWM correlated to the MSFC score (p<0.05). MTI and MRS were useful for initial disease assessment in NAWM. MTI and MRS correlated with clinical scores, indicating potential for monitoring the disease course and gaining new insights into treatment-related effects. (orig.)

Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier.

Science.gov (United States)

Mao, Keming; Deng, Zhuofu

2016-01-01

This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP) is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.

Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier

Directory of Open Access Journals (Sweden)

Keming Mao

2016-01-01

Full Text Available This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.

Brain GABA levels across psychiatric disorders : A systematic literature review and meta-analysis of 1H-MRS studies

NARCIS (Netherlands)

Schür, Remmelt R.; Draisma, Luc W R; Wijnen, Jannie P.; Boks, Marco P.; Koevoets, Martijn G J C; Joëls, Marian; Klomp, Dennis W.; Kahn, René S.; Vinkers, Christiaan H.

2016-01-01

The inhibitory gamma-aminobutyric acid (GABA) system is involved in the etiology of most psychiatric disorders, including schizophrenia, autism spectrum disorder (ASD) and major depressive disorder (MDD). It is therefore not surprising that proton magnetic resonance spectroscopy (1H-MRS) is

Brain white matter 1 H MRS in Leber optic neuropathy mutation carriers

DEFF Research Database (Denmark)

Ostojic, Jelena; Jancic, Jasna; Kozic, Dusko

2009-01-01

OBJECTIVE: This study was conducted in order to test the hypothesis that proton MR spectroscopic (1H MRS) profile of Leber's hereditary optic neuropathy (LHON) mutation carriers group (including both symptomatic and asymptomatic) differs from group of healthy individuals and to determine metabolite...... or ratio that contributes most to differentiation. PATIENTS AND METHODS: We performed single voxel 1H MRS in normal appearing white matter of eighteen LHON mtDNA mutation carriers bearing one of three LHON mtDNA point mutations and in fifty control subjects. RESULTS: ANOVA showed significant difference...

The PDD-MRS : An instrument for identification of autism spectrum disorders in persons with mental retardation

NARCIS (Netherlands)

Kraijer, D; de Bildt, A

The Scale of Pervasive Developmental Disorder in Mentally Retarded Persons (PDD-MRS) is described. The PDD-MRS is a simple classification and screening instrument devised for identification of autistic disorders (of the entire spectrum) in persons with mental retardation from mild to profound

Automatic discrimination between safe and unsafe swallowing using a reputation-based classifier

Directory of Open Access Journals (Sweden)

Nikjoo Mohammad S

2011-11-01

Full Text Available Abstract Background Swallowing accelerometry has been suggested as a potential non-invasive tool for bedside dysphagia screening. Various vibratory signal features and complementary measurement modalities have been put forth in the literature for the potential discrimination between safe and unsafe swallowing. To date, automatic classification of swallowing accelerometry has exclusively involved a single-axis of vibration although a second axis is known to contain additional information about the nature of the swallow. Furthermore, the only published attempt at automatic classification in adult patients has been based on a small sample of swallowing vibrations. Methods In this paper, a large corpus of dual-axis accelerometric signals were collected from 30 older adults (aged 65.47 ± 13.4 years, 15 male referred to videofluoroscopic examination on the suspicion of dysphagia. We invoked a reputation-based classifier combination to automatically categorize the dual-axis accelerometric signals into safe and unsafe swallows, as labeled via videofluoroscopic review. From these participants, a total of 224 swallowing samples were obtained, 164 of which were labeled as unsafe swallows (swallows where the bolus entered the airway and 60 as safe swallows. Three separate support vector machine (SVM classifiers and eight different features were selected for classification. Results With selected time, frequency and information theoretic features, the reputation-based algorithm distinguished between safe and unsafe swallowing with promising accuracy (80.48 ± 5.0%, high sensitivity (97.1 ± 2% and modest specificity (64 ± 8.8%. Interpretation of the most discriminatory features revealed that in general, unsafe swallows had lower mean vibration amplitude and faster autocorrelation decay, suggestive of decreased hyoid excursion and compromised coordination, respectively. Further, owing to its performance-based weighting of component classifiers, the static

A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier.

Science.gov (United States)

Chen, Yi; Shao, Ying; Yan, Jie; Yuan, Ti-Fei; Qu, Yanwen; Lee, Elizabeth; Wang, Shuihua

2017-01-01

Alzheimer's disease patients are increasing rapidly every year. Scholars tend to use computer vision methods to develop automatic diagnosis system. (Background) In 2015, Gorji et al. proposed a novel method using pseudo Zernike moment. They tested four classifiers: learning vector quantization neural network, pattern recognition neural network trained by Levenberg-Marquardt, by resilient backpropagation, and by scaled conjugate gradient. This study presents an improved method by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Therefore, it can be used to detect Alzheimer's disease. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Time-based air traffic management using expert systems

Science.gov (United States)

Tobias, L.; Scoggins, J. L.

1986-01-01

A prototype expert system was developed for the time scheduling of aircraft into the terminal area. The three functions of the air traffic control schedule advisor are as follows: first, for each new arrival, it develops an admissible flight plan for that aircraft. Second, as the aircraft progresses through the terminal area, it monitors deviations from the flight plan and provides advisories to return the aircraft to its assigned schedule. Third, if major disruptions such as missed approaches occur, it develops a revised plan. The advisor is operational on a Symbolics 3600, and is programed in MRS (a logic programming language), Lisp, and FORTRAN.

A web-based neurological pain classifier tool utilizing Bayesian decision theory for pain classification in spinal cord injury patients

Science.gov (United States)

Verma, Sneha K.; Chun, Sophia; Liu, Brent J.

2014-03-01

Pain is a common complication after spinal cord injury with prevalence estimates ranging 77% to 81%, which highly affects a patient's lifestyle and well-being. In the current clinical setting paper-based forms are used to classify pain correctly, however, the accuracy of diagnoses and optimal management of pain largely depend on the expert reviewer, which in many cases is not possible because of very few experts in this field. The need for a clinical decision support system that can be used by expert and non-expert clinicians has been cited in literature, but such a system has not been developed. We have designed and developed a stand-alone tool for correctly classifying pain type in spinal cord injury (SCI) patients, using Bayesian decision theory. Various machine learning simulation methods are used to verify the algorithm using a pilot study data set, which consists of 48 patients data set. The data set consists of the paper-based forms, collected at Long Beach VA clinic with pain classification done by expert in the field. Using the WEKA as the machine learning tool we have tested on the 48 patient dataset that the hypothesis that attributes collected on the forms and the pain location marked by patients have very significant impact on the pain type classification. This tool will be integrated with an imaging informatics system to support a clinical study that will test the effectiveness of using Proton Beam radiotherapy for treating spinal cord injury (SCI) related neuropathic pain as an alternative to invasive surgical lesioning.

Evaluation of brain metabolism in autism. A 1H-MRS study

International Nuclear Information System (INIS)

Otsuka, Hideki; Harada, Masafumi; Mori, Kenji; Taoka, Yoshiaki; Nishitani, Hiromu

1998-01-01

We performed a 1 H-MRS study on 13 autistic patients (2-15 y.o., 10 males, 3 females) and 10 normal children (6-14 y.o., 4 males, 6 females). An MR spectra of the bilateral amygdaloid-hippocampal regions, which play a very important role in the limbic system, was obtained for each subject using STEAM sequence (TR=5000 ms, TE=18 ms). In addition to the evaluation of signal intensity ratios, the absolute concentrations of 3 major metabolites, N-acetyl-aspartate (NAA), creatine/phosphocreatine (Cr) and choline-containing substances (Cho), were quantified by means of an internal reference method using unsuppressed tissue water. The concentration of NAA was decreased in autistic patients, and the difference between patients and normal controls was significant (right sided NAA level: autistic group-8.6±2.1 mM, control group-11.0±1.1 mM; left sided NAA level: autistic group-8.9±1.8 mM, control group-10.5±1.4 mM). We speculate that the decrease in NAA levels in the autistic patients reflects neuronal hypofunction or immature neurons. 1 H-MRS may provide useful clinical information which is not readily obtainable with other imaging methods. (author)

Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis

International Nuclear Information System (INIS)

Sahiner, Berkman; Chan, Heang-Ping; Petrick, Nicholas; Helvie, Mark A.; Goodsitt, Mitchell M.

1998-01-01

A genetic algorithm (GA) based feature selection method was developed for the design of high-sensitivity classifiers, which were tailored to yield high sensitivity with high specificity. The fitness function of the GA was based on the receiver operating characteristic (ROC) partial area index, which is defined as the average specificity above a given sensitivity threshold. The designed GA evolved towards the selection of feature combinations which yielded high specificity in the high-sensitivity region of the ROC curve, regardless of the performance at low sensitivity. This is a desirable quality of a classifier used for breast lesion characterization, since the focus in breast lesion characterization is to diagnose correctly as many benign lesions as possible without missing malignancies. The high-sensitivity classifier, formulated as the Fisher's linear discriminant using GA-selected feature variables, was employed to classify 255 biopsy-proven mammographic masses as malignant or benign. The mammograms were digitized at a pixel size of 0.1mmx0.1mm, and regions of interest (ROIs) containing the biopsied masses were extracted by an experienced radiologist. A recently developed image transformation technique, referred to as the rubber-band straightening transform, was applied to the ROIs. Texture features extracted from the spatial grey-level dependence and run-length statistics matrices of the transformed ROIs were used to distinguish malignant and benign masses. The classification accuracy of the high-sensitivity classifier was compared with that of linear discriminant analysis with stepwise feature selection (LDA sfs ). With proper GA training, the ROC partial area of the high-sensitivity classifier above a true-positive fraction of 0.95 was significantly larger than that of LDA sfs , although the latter provided a higher total area (A z ) under the ROC curve. By setting an appropriate decision threshold, the high-sensitivity classifier and LDA sfs correctly

Human Activity Recognition by Combining a Small Number of Classifiers.

Science.gov (United States)

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

(1) H-MRS processing parameters affect metabolite quantification: The urgent need for uniform and transparent standardization

NARCIS (Netherlands)

Bhogal, A.A.; Schur, R.R.; Houtepen, L.C.; Bank, B.L. van de; Boer, V.O.; Marsman, A.; Barker, P.B.; Scheenen, T.W.J.; Wijnen, J.P.; Vinkers, C.H.; Klomp, D.W.J.

2017-01-01

Proton magnetic resonance spectroscopy ((1) H-MRS) can be used to quantify in vivo metabolite levels, such as lactate, gamma-aminobutyric acid (GABA) and glutamate (Glu). However, there are considerable analysis choices which can alter the accuracy or precision of (1) H-MRS metabolite

The Menopause Rating Scale (MRS scale: A methodological review

Directory of Open Access Journals (Sweden)

Strelow Frank

2004-09-01

Full Text Available Abstract Background This paper compiles data from different sources to get a first comprehensive picture of psychometric and other methodological characteristics of the Menopause Rating Scale (MRS scale. The scale was designed and standardized as a self-administered scale to (a to assess symptoms/complaints of aging women under different conditions, (b to evaluate the severity of symptoms over time, and (c to measure changes pre- and postmenopause replacement therapy. The scale became widespread used (available in 10 languages. Method A large multinational survey (9 countries in 4 continents from 2001/ 2002 is the basis for in depth analyses on reliability and validity of the MRS. Additional small convenience samples were used to get first impressions about test-retest reliability. The data were centrally analyzed. Data from a postmarketing HRT study were used to estimate discriminative validity. Results Reliability measures (consistency and test-retest stability were found to be good across countries, although the sample size for test-retest reliability was small. Validity: The internal structure of the MRS across countries was astonishingly similar to conclude that the scale really measures the same phenomenon in symptomatic women. The sub-scores and total score correlations were high (0.7–0.9 but lower among the sub-scales (0.5–0.7. This however suggests that the subscales are not fully independent. Norm values from different populations were presented showing that a direct comparison between Europe and North America is possible, but caution recommended with comparisons of data from Latin America and Indonesia. But this will not affect intra-individual comparisons within clinical trials. The comparison with the Kupperman Index showed sufficiently good correlations, illustrating an adept criterion-oriented validity. The same is true for the comparison with the generic quality-of-life scale SF-36 where also a sufficiently close association

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

Directory of Open Access Journals (Sweden)

M. Al-Rousan

2005-08-01

Full Text Available Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

Neuronal degeneration in the hippocampus and dorsolateral prefrontal cortex in depressive disorder Correlation between 1H-MRS and Minnesota Multiphasic Personality Inventory

Institute of Scientific and Technical Information of China (English)

Jun Xia; Minjie Yang; Yi Lei; Yicheng Zhou

2010-01-01

Previous studies using magnetic resonance imaging(MRI)and functional MRI to study depression have primarily focused on proton magnetic resonance spectroscopy(1H-MRS)appearance in various areas of the brain and volume measurements in the limbic system.However,results have not been consistent.To the best of our knowledge,very little is known about the relationship between 1H-MRS appearance and depression inventory.In the present study,the relationship between 1H-MRS appearance in depressive patients and Minnesota Multiphasic Personality Inventory-2 scale was analyzed.MRI and 1H-MRS exhibited widened sulci and cisterns,as well as an absence of abnormal signals in depressive patients.In addition,N-acetyl aspartate/total creatine ratios in bilateral hippocampi and dorsolateral prefrontal cortex were significantly less in depressive patients than in control subjects(P < 0.01).In contrast,choline-containing compounds/total creatine ratios in the dorsolateral prefrontal cortex were significantly greater in depressive patients than in control subjects(P < 0.01).These ratios significantly and positively correlated with patient total depression scores as assessed using the Minnesota Multiphasic Personality Inventory-2 scale(r=0.934 7,0.878 7,P < 0.01).These results suggested that 1H-MRS could be used to reveal a reduced number of neurons in the hippocampus and dorsolateral prefrontal cortex,as well as altered membrane phospholipid metabolism in the dorsolateral prefrontal cortex,in patients with depressive disorder.Abnormal mechanisms partially reflected severity of depressive disorder.

Conceptual modular description of the high-level waste management system for system studies model development

International Nuclear Information System (INIS)

McKee, R.W.; Young, J.R.; Konzek, G.J.

1992-08-01

This document presents modular descriptions of possible alternative components of the federal high-level radioactive waste management system and the procedures for combining these modules to obtain descriptions for alternative configurations of that system. The 20 separate system component modules presented here can be combined to obtain a description of any of the 17 alternative system configurations (i.e., scenarios) that were evaluated in the MRS Systems Studies program (DOE 1989a). First-approximation descriptions of other yet-undefined system configurations could also be developed for system study purposes from this database. The descriptions include, in a modular format, both functional descriptions of the processes in the waste management system, plus physical descriptions of the equipment and facilities necessary for performance of those functions

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

Energy Technology Data Exchange (ETDEWEB)

Baraldi, Piero, E-mail: piero.baraldi@polimi.i [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Razavi-Far, Roozbeh [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Zio, Enrico [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Ecole Centrale Paris-Supelec, Paris (France)

2011-04-15

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

International Nuclear Information System (INIS)

Baraldi, Piero; Razavi-Far, Roozbeh; Zio, Enrico

2011-01-01

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Oblique decision trees using embedded support vector machines in classifier ensembles

NARCIS (Netherlands)

Menkovski, V.; Christou, I.; Efremidis, S.

2008-01-01

Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes oblique decision tree technology based on support vector machines for the construction of oblique (non-axis parallel)

Double Ramp Loss Based Reject Option Classifier

Science.gov (United States)

2015-05-22

of convex (DC) functions. To minimize it, we use DC programming approach [1]. The proposed method has following advantages: (1) the proposed loss LDR ...space constraints. We see that LDR does not put any restriction on ρ for it to be an upper bound of L0−d−1. 2.2 Risk Formulation Using LDR Let S = {(xn...classifier learnt using LDR based approach (C = 100, μ = 1, d = .2). Filled circles and triangles represent the support vectors. 4 Experimental Results We show

Monitored Retrievable Storage conceptual system studies: closed-cycle vault

International Nuclear Information System (INIS)

Washington, J.A.; Ganley, J.T.

1984-02-01

The Nuclear Waste Policy Act of 1982 requires the DOE to submit a proposal to Congress by June 1985 for the construction of one or more Monitored Retrieval Storage (MRS) facilities. In response, the DOE initiated studies to develop system descriptions and cost estimates for preconceptual designs of storage concepts suitable for use at MRS facilities. This report provides a system description and cost estimates for a Closed-Cycle Vault (CCV) MRS facility. The facility description is divided into four parts: (1) the R and H area, (2) the interface facility, (3) the on-site transport system, and (4) the storage system. The MRS facility has been designed to meet handling rates of 1800 and 3000 MTU/yr. The corresponding peak inventories are 15,000 and 72,000 MTU. Three types of cases were considered, based on the material to be stored: (1) Spent fuel only; (2) HLW and TRU waste; and (3) HLW only. For each of these three types, a cost estimate was done for a 15,000 and a 72,000 MTU facility, resulting in six different cost estimates. Section 4 presents the cost analysis of the CCV MRS system. Tables 4-2 through 4-7 give the construction or capital costs for the six cases. Tables 4-8 through 4-13 show the total discounted life-cycle costs for each of the six cases. These life-cycle costs include operating and decommissioning costs. These tables also show the time distribution of the capital costs. Table 2-1 summarizes the capital, operating, and discounted costs for the six cases studied. 2 references, 15 figures, 18 tables

1H-MRS processing parameters affect metabolite quantification : The urgent need for uniform and transparent standardization

NARCIS (Netherlands)

Bhogal, Alex A.; Schür, Remmelt; Houtepen, Lotte C.; van de Bank, B.L.; Boer, Vincent O.; Marsman, Anouk; Barker, Peter B.; Scheenen, Tom W. J.; Wijnen, Jannie P.; Vinkers, Christiaan H.; Klomp, Dennis W.J.

2017-01-01

Proton magnetic resonance spectroscopy (1H-MRS) can be used to quantify in vivo metabolite levels, such as lactate, γ-aminobutyric acid (GABA) and glutamate (Glu). However, there are considerable analysis choices which can alter the accuracy or precision of 1H-MRS metabolite quantification. It is

Hyperspectral image classifier based on beach spectral feature

International Nuclear Information System (INIS)

Liang, Zhang; Lianru, Gao; Bing, Zhang

2014-01-01

The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches

Detection of Fundus Lesions Using Classifier Selection

Science.gov (United States)

Nagayoshi, Hiroto; Hiramatsu, Yoshitaka; Sako, Hiroshi; Himaga, Mitsutoshi; Kato, Satoshi

A system for detecting fundus lesions caused by diabetic retinopathy from fundus images is being developed. The system can screen the images in advance in order to reduce the inspection workload on doctors. One of the difficulties that must be addressed in completing this system is how to remove false positives (which tend to arise near blood vessels) without decreasing the detection rate of lesions in other areas. To overcome this difficulty, we developed classifier selection according to the position of a candidate lesion, and we introduced new features that can distinguish true lesions from false positives. A system incorporating classifier selection and these new features was tested in experiments using 55 fundus images with some lesions and 223 images without lesions. The results of the experiments confirm the effectiveness of the proposed system, namely, degrees of sensitivity and specificity of 98% and 81%, respectively.

[Application of classified protection of information security in the information system of air pollution and health impact monitoring].

Science.gov (United States)

Hao, Shuxin; Lü, Yiran; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To study the application of classified protection of information security in the information system of air pollution and health impact monitoring, so as to solve the possible safety risk of the information system. According to the relevant national standards and requirements for the information system security classified protection, and the professional characteristics of the information system, to design and implement the security architecture of information system, also to determine the protection level of information system. Basic security measures for the information system were developed in the technical safety and management safety aspects according to the protection levels, which effectively prevented the security risk of the information system. The information system established relatively complete information security protection measures, to enhanced the security of professional information and system service, and to ensure the safety of air pollution and health impact monitoring project carried out smoothly.

How the signal‐to‐noise ratio influences hyperpolarized 13C dynamic MRS data fitting and parameter estimation

DEFF Research Database (Denmark)

Santarelli, Maria Filomena; Positano, Vincenzo; Giovannetti, Giulio

2012-01-01

signals with low signal‐to‐noise ratio (SNR). The relationship between SNR and the precision of quantitative analysis for the evaluation of the in vivo kinetic behavior of metabolites is unknown. In this article, this topic is addressed by Monte Carlo simulations, covering the problem of MRS signal model......MRS of hyperpolarized 13C‐labeled compounds represents a promising technique for in vivo metabolic studies. However, robust quantification and metabolic modeling are still important areas of investigation. In particular, time and spatial resolution constraints may lead to the analysis of MRS...

Systems Engineering Management Plan

International Nuclear Information System (INIS)

1994-01-01

The purpose of this Monitored Retrievable Storage (MRS) Project Systems Engineering Management Plan (SEMP) is to define and establish the MRS Project Systems Engineering process that implements the approved policy and requirements of the Office of Civilian Radioactive Waste Management (OCRWM) for the US Department of Energy (DOE). This plan is Volume 5 of the MRS Project Management Plan (PMP). This plan provides the framework for implementation of systems engineering on the MRS Project consistent with DOE Order 4700.1, the OCRWM Program Management System Manual (PMSM), and the OCRWM Systems Engineering Management Plan (SEMP)

In-Vivo Detection and Tracking of T Cells in Various Organs in a Melanoma Tumor Model by 19F-Fluorine MRS/MRI.

Directory of Open Access Journals (Sweden)

Christine Gonzales

Full Text Available 19F-MRI and 19F-MRS can identify specific cell types after in-vitro or in-vivo 19F-labeling. Knowledge on the potential to track in-vitro 19F-labeled immune cells in tumor models by 19F-MRI/MRS is scarce.To study 19F-based MR techniques for in-vivo tracking of adoptively transferred immune cells after in-vitro 19F-labeling, i.e. to detect and monitor their migration non-invasively in melanoma-bearing mice.Splenocytes (SP were labeled in-vitro with a perfluorocarbon (PFC and IV-injected into non-tumor bearing mice. In-vitro PFC-labeled ovalbumin (OVA-specific T cells from the T cell receptor-transgenic line OT-1, activated with anti-CD3 and anti-CD28 antibodies (Tact or OVA-peptide pulsed antigen presenting cells (TOVA-act, were injected into B16 OVA melanoma-bearing mice. The distribution of the 19F-labelled donor cells was determined in-vivo by 19F-MRI/MRS. In-vivo 19F-MRI/MRS results were confirmed by ex-vivo 19F-NMR and flow cytometry.SP, Tact, and TOVA-act were successfully PFC-labeled in-vitro yielding 3x1011-1.4x1012 19F-atoms/cell in the 3 groups. Adoptively transferred 19F-labeled SP, TOVA-act, and Tact were detected by coil-localized 19F-MRS in the chest, abdomen, and left flank in most animals (corresponding to lungs, livers, and spleens, respectively, with highest signal-to-noise for SP vs TOVA-act and Tact, p<0.009 for both. SP and Tact were successfully imaged by 19F-MRI (n = 3; liver. These in-vivo data were confirmed by ex-vivo high-resolution 19F-NMR-spectroscopy. By flow cytometric analysis, however, TOVA-act tended to be more abundant versus SP and Tact (liver: p = 0.1313; lungs: p = 0.1073; spleen: p = 0.109. Unlike 19F-MRI/MRS, flow cytometry also identified transferred immune cells (SP, Tact, and TOVA-act in the tumors.SP, Tact, and TOVA-act were successfully PFC-labeled in-vitro and detected in-vivo by non-invasive 19F-MRS/MRI in liver, lung, and spleen. The portion of 19F-labeled T cells in the adoptively transferred cell

The Resurgence of Ideology in Bernard Shaw’s Mrs Warren’s Profession (1893)

OpenAIRE

Guy, Stéphane

2017-01-01

In the last of his Plays Unpleasant, Shaw delves into the economic roots of prostitution, deconstructing the woman-with-a-past sham and its underlying conservative ideology. The socialist playwright and theorist seeks to lay the blame on the capitalist system and on a middle-class public all too eager to ascribe prostitution to merely individual villainy. Echoing the standpoint of Victorian social reformers, Mrs Warren’s Profession explodes the well-made play pattern to shatter commonplaces a...

Mrs Hitler and her doctor.

Science.gov (United States)

Macleod, Sandy

2005-12-01

The doctor who attended the mother of Adolf Hitler in her terminal illness has been blamed as a cause of the Holocaust. The medical details recorded of this professional relationship are presented and discussed. Dr Bloch's medical care of Mrs Hitler was consistent with the prevailing medical practice of the management of fungating breast carcinoma. Indeed, the general practitioner's care and attention of the family appear to have been astute and supportive. There is nothing to suggest that Dr Bloch's medical care was other than competent. Doctors who have the (mis)fortune to professionally attend major figures of history may be unfairly viewed, despite their appropriate and adequate care.

Glutamatergic system dysfunction in schizophrenia. A proton magnetic resonance spectroscopy (1H MRS) study

International Nuclear Information System (INIS)

Szulc, A.; Galinska, B.; Czernikiewicz, A.; Tarasow, E.; Kubas, B.; Dzienis, W.; Walecki, J.

2004-01-01

The present study was performed to determine whether there are any differences in metabolite levels as measured by 1 H MRS between chronic and first-episode schizophrenic patients. 17 patients with the diagnosis of chronic schizophrenia and 31 patients with first-episode schizophrenia (ICD-10) were included into the study. The patients were assessed by means of PANSS, CGI and Calgary scales.We also examined 13 healthy persons as control group. MRI and MRS procedures: Proton resonance spectroscopy was performed on a 1,5 MR scanner, PRESS sequence, TR=1500 ms, TE=35 ms, number of repetition=192 and included suppression of water by MOIST sequence. Each volume element (voxel) had dimension of 2x2x2 cm and was localised in the left frontal lobe, in the left temporal lobe and in left thalamus. Complex containing glutamine (Gln), glutamate (Glu) and gamma-aminobutyric acid (GABA) was measured. Ratios of metabolite to creatine and unsuppressed water signal were analysed. We didn't find any significant differences in Glx levels between chronic and first-episode patients and between chronic patients and controls in all studied regions.In the left temporal lobe Glx/Cr ratio was significantly higher in first-episode patients in comparison to controls.We observed significant positive correlation between Glx/Cr level in the left temporal lobe and CGI and PANSS-Negative scores, and negative correlation between Glx/H 2 0 level in the left temporal lobe and PANSS-Positive score. Increased Glx level in the left temporal lobe in first-episode patients suggest that altered glutamatergic activity in this region is present at the onset of disease and doesn't progress over time. (author)

Development of a Web-based financial application System

Science.gov (United States)

Hasan, M. R.; Ibrahimy, M. I.; Motakabber, S. M. A.; Ferdaus, M. M.; Khan, M. N. H.; Mostafa, M. G.

2013-12-01

The paper describes a technique to develop a web based financial system, following latest technology and business needs. In the development of web based application, the user friendliness and technology both are very important. It is used ASP .NET MVC 4 platform and SQL 2008 server for development of web based financial system. It shows the technique for the entry system and report monitoring of the application is user friendly. This paper also highlights the critical situations of development, which will help to develop the quality product.

FERAL : Network-based classifier with application to breast cancer outcome prediction

NARCIS (Netherlands)

Allahyar, A.; De Ridder, J.

2015-01-01

Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

Science.gov (United States)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

31P-MRS study for the assessment of tumor response after radiotherapy and/or hyperthermia

International Nuclear Information System (INIS)

Kimura, Hirohiko; Itho, Satoshi; Nakatsugawa, Sigekazu; Maeda, Masayuki; Iwasaki, Toshiko; Yamamoto, Kazutaka; Ishii, Yasushi

1992-01-01

The metabolic changes of human lung cancer implanted in nude mice were studied by the use of in vivo 31 P nuclear magnetic resonance spectroscopy ( 31 P-MRS) after radiotherapy, hyperthermia or the combined therapy of radiation and hyperthermia. 31 P-MRS of the tumors showed increased Pi/β-NTP ratio and acidic pH value on 1 day after hyperthermia, that indicated metabolic decline caused by hyperthermia. On the other hand, lower Pi/β-NTP ratios during 3 to 10 days after irradiation suggested metabolic activation of the tumors. In the tumors treated with the combined therapy, 31 P-MRS revealed increase of Pi/β-NTP ratio within 1 day and its decrease subsequent 6 to 10 days after treatment, that indicated additive bi-phasic changes induced by radiation and hyperthermia, respectively. Since Pi/β-NTP ratio had significant correlation to the tumor blood perfusion measured by hydrogen gas clearance studies, these bi-phasic changes were considered to correspond to two different physiological states, namely, ischemic and reperfused states. 31 P-MRS obtained from tumors could be useful to asses the physiological consequence following radiation, hyperthermia or the combined therapy. (author)

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 application of MRI and MRS in psychiatry and performance evaluation of magnetic field homogeneity in MRI

Science.gov (United States)

Chen, Hua Hsuan

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) is a safe non-invasive tool to study the physiological mechanisms of the human brain. MRS has the capability to provide the information regarding neurochemicals in brains of patients with neuropsychiatric disorders. Therefore, to produce measurable and interpretable information in MRI and MRS, a quality control (QC) program is required. Magnetic field homogeneity (MFH) is an important factor for QC when the volume sizes and neurochemical levels are quantified. Poor main (B0) MFH leads to artifacts, signal losses and broadened line widths. The American College of Radiology's (ACR) MRI QC manual mandates annual checks of MFH, suggesting tests using spectral line widths (FWHM) and phase-difference (Deltaϕ) maps. A new method, dubbed the bandwidth-difference (DeltaBW) method, is proposed along with a prototype phantom for determining MFH. The DeltaBW method is compared with standard methods and has also been tested in different model MRI systems from various manufacturers. Direct comparisons of the data obtained using the DeltaBW method demonstrated good agreement with data obtained using the linewidth method and the frequency map data provided by one MRI system manufacturer. As a result, the DeltaBW method produces measurements of MFH at various Diameter Sphere Volume (DSV) values that can be obtained from a single set of phantom images. The conclusion of the study is that the accuracy of DeltaBW B0 homogeneity measurements of MFH is comparable to the other methods tested while the ease of measurement in practical clinical setting is considerably improved.

Observation and control of hepatic specimens with MRI and MRS

International Nuclear Information System (INIS)

Ludescher, B.; Wietek, B.; Machann, J.; Graf, H.; Schick, F.; Subke, J.; Claussen, C.D.

2004-01-01

Purpose: The purpose of this study was to observe the process of fixation in liver specimens non-invasively by means of magnetic resonance. The fixation process of several formaldehyde-containing solutions was monitored with MRI and MRS at two different temperatures. Materials and Methods: Liver specimens were conserved in aqueous fixative solutions containing formaldehyde concentrations of 0.7, 1.8, 4 and 7.2% and at different temperatures of 5 C and 20 C. MRI was performed with T1-, T2- and PD-weighted TSE sequences, a 2D FLASH-sequence with and without magnetization transfer, and a FISP 3D-sequence on a clinical 1.5 Tesla MR whole-body unit, and MRS with 1 H-spectroscopic methods (STEAM-sequence) on a 3 Tesla MR whole-body unit. Results: The diffusion of formaldehyde into the tissue was best identified on PD- and T1-weighted images as a band under the liver surface with increasing thickness, penetrating especially fast during the first three days. Spectroscopic measurements revealed the rising formaldehyde concentration in the inner part of the organs. Temperature had no significant influence on the velocity of immersing, but cooling conditions produced less gas-filled caverns due to reduced undesired decomposition processes. Conclusion: The spatial and temporal process of ongoing fixation in the liver can be monitored by MRI. MRS confirms a rising concentration of formaldehyde during ongoing fixation. (orig.) [de

Recognition of pornographic web pages by classifying texts and images.

Science.gov (United States)

Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve

2007-06-01

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

Development of Patient Status-Based Dynamic Access System for Medical Information Systems

Directory of Open Access Journals (Sweden)

Chang Won Jeong

2015-06-01

Full Text Available Recently, the hospital information system environment using IT communication technology and utilization of medical information has been increasing. In the medical field, the medical information system only supports the transfer of patient information to medical staff through an electronic health record, without information about patient status. Hence, it needs a method of real-time monitoring for the patient. Also, in this environment, a secure method in approaching healthcare through various smart devices is required. Therefore, in this paper, in order to classify the status of the patients, we propose a dynamic approach of the medical information system in a hospital information environment using the dynamic access control method. Also, we applied the symmetric method of AES (Advanced Encryption Standard. This was the best encryption algorithm for sending and receiving biological information. We can define usefulness as the dynamic access application service based on the final result of the proposed system. The proposed system is expected to provide a new solution for a convenient medical information system.

The Role of PDH Inhibition in the Development of Hypertrophy in the Hyperthyroid Rat Heart: A Combined MRI and Hyperpolarized MRS Study

Science.gov (United States)

Atherton, Helen J.; Dodd, Michael S.; Heather, Lisa C.; Schroeder, Marie A.; Griffin, Julian L.; Radda, George K.; Clarke, Kieran; Tyler, Damian J.

2015-01-01

Background Hyperthyroidism increases heart rate, contractility and cardiac output, as well as metabolic rate. It is also accompanied by alterations in the regulation of cardiac substrate utilisation. Specifically, hyperthyroidism increases the ex vivo activity of pyruvate dehydrogenase kinase (PDK), thereby inhibiting glucose oxidation via pyruvate dehydrogenase (PDH). Cardiac hypertrophy is another effect of hyperthyroidism, with an increase in the abundance of mitochondria. Although the hypertrophy is initially beneficial, it can eventually lead to heart failure. The aim of this study was to use hyperpolarized magnetic resonance spectroscopy (MRS) to investigate the rate and regulation of in vivo pyruvate dehydrogenase (PDH) flux in the hyperthyroid heart, and to establish whether modulation of flux through PDH would alter cardiac hypertrophy. Methods & Results Hyperthyroidism was induced in 18 male Wistar rats with 7 daily intraperitoneal injections of freshly prepared triiodothyronine (T3; 0.2 mg/kg/day). In vivo PDH flux, assessed using hyperpolarized MRS, was reduced by 59% in hyperthyroid animals (0.0022 ± 0.0002 s−1 vs 0.0055 ± 0.0005 s−1, P = 0.0003) and this reduction was completely reversed by both acute and chronic delivery of the PDK inhibitor, dichloroacetic acid (DCA). Hyperpolarized [2-13C]pyruvate was also used to evaluate Krebs cycle metabolism and demonstrated a unique marker of anaplerosis, the level of which was significantly increased in the hyperthyroid heart. Cine MRI showed that chronic DCA treatment significantly reduced the hypertrophy observed in hyperthyroid animals (100 ± 20 mg vs 200 ± 30 mg; P = 0.04) despite no change to the increase observed in cardiac output. Conclusions This work has demonstrated that inhibition of glucose oxidation in the hyperthyroid heart in vivo is PDK mediated. Relieving this inhibition can increase the metabolic flexibility of the hyperthyroid heart and reduce the level of hypertrophy that develops

Discovering mammography-based machine learning classifiers for breast cancer diagnosis.

Science.gov (United States)

Ramos-Pollán, Raúl; Guevara-López, Miguel Angel; Suárez-Ortega, Cesar; Díaz-Herrero, Guillermo; Franco-Valiente, Jose Miguel; Rubio-Del-Solar, Manuel; González-de-Posada, Naimy; Vaz, Mario Augusto Pires; Loureiro, Joana; Ramos, Isabel

2012-08-01

This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details. The method can be used under different data acquisition circumstances and exploits computer clusters to select well performing MLC configurations. We evaluated 286 cases extracted from the repository owned by HSJ-FMUP, where specialized radiologists segmented regions on CC and/or MLO images (biopsies provided the golden standard). Around 20,000 MLC configurations were evaluated, obtaining classifiers achieving an area under the ROC curve of 0.996 when combining features vectors extracted from CC and MLO views of the same case.

Hippocampal volume MRI and 1H-MRS study in chronic alcohol dependent patients

International Nuclear Information System (INIS)

Miao Huanmin; Chen Jun; Zha Yunfei; Zhang Yu; Liu Changsheng; Pan Ewu

2010-01-01

Objective: To observe the changes of the bilateral hippocampal volume (BHV) and 1 H- MRS appearance of chronic alcohol dependent (CAD) patients and to provide quantitative information for the clinical diagnosis of CAD. Methods: The conventional MR imaging including three-dimensional fast spoiled gradient recalled echo (3D-FSPGR) and 1 H-MRS were performed on 16 patients with CAD (CAD group) and 18 cases of volunteer (control group). The BHV were measured in both groups and the standardized BHV in CAD group and control group were compared. 1 H-MRS metabolites including N-acetylaspartate (NAA), Choline compounds (Cho), Creatine (Cr), and myoinositol (mI) of the bilateral cephalic hippocampus were acquired. The ratios of Cho/Ci, Cho/NAA, NAA/Cr and mI/Ci within the bilateral cephalic hippocampus of the two groups were compared. The t test was used to compare the BHV and the ratios of 1 H-MRS in the bilateral cephalic hippocampus between the two groups. Results: In CAD group, the left and the right hippocampal volume were 1.881±0.292, 2.139±0.328 respectively while they were 2.106±0.245 and 2.267±0.271 respectively in the control group. The BHV had no significant difference between the left and the right in either the CAD group or the control group (t=0.232, 0.147 respectively, P>0.05). The BHV had no significant difference between the CAD group and control group (t=0.424, 0.131 respectively, P>0.05). The Cho/Cr and NAA/Cr in the right cephalic hippocampus of the CAD group were 1.225±0.210 and 1.145±0.034 respectively, while they were 1.429±0.286, 1.612±0.444 respectively in the control group (t=0.321, 0.408, P 1 H-MRS could potentially provide early diagnostic evidence for CAD patients before the onset of cerebral morphological changes. (authors)

A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

Science.gov (United States)

Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

2017-08-01

Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

Brain GABA levels across psychiatric disorders: A systematic literature review and meta-analysis of (1) H-MRS studies.

Science.gov (United States)

Schür, Remmelt R; Draisma, Luc W R; Wijnen, Jannie P; Boks, Marco P; Koevoets, Martijn G J C; Joëls, Marian; Klomp, Dennis W; Kahn, René S; Vinkers, Christiaan H

2016-09-01

The inhibitory gamma-aminobutyric acid (GABA) system is involved in the etiology of most psychiatric disorders, including schizophrenia, autism spectrum disorder (ASD) and major depressive disorder (MDD). It is therefore not surprising that proton magnetic resonance spectroscopy ((1) H-MRS) is increasingly used to investigate in vivo brain GABA levels. However, integration of the evidence for altered in vivo GABA levels across psychiatric disorders is lacking. We therefore systematically searched the clinical (1) H-MRS literature and performed a meta-analysis. A total of 40 studies (N = 1,591) in seven different psychiatric disorders were included in the meta-analysis: MDD (N = 437), schizophrenia (N = 517), ASD (N = 150), bipolar disorder (N = 129), panic disorder (N = 81), posttraumatic stress disorder (PTSD) (N = 104), and attention deficit/hyperactivity disorder (ADHD) (N = 173). Brain GABA levels were lower in ASD (standardized mean difference [SMD] = -0.74, P = 0.001) and in depressed MDD patients (SMD = -0.52, P = 0.005), but not in remitted MDD patients (SMD = -0.24, P = 0.310) compared with controls. In schizophrenia this finding did not reach statistical significance (SMD = -0.23, P = 0.089). No significant differences in GABA levels were found in bipolar disorder, panic disorder, PTSD, and ADHD compared with controls. In conclusion, this meta-analysis provided evidence for lower brain GABA levels in ASD and in depressed (but not remitted) MDD patients compared with healthy controls. Findings in schizophrenia were more equivocal. Even though future (1) H-MRS studies could greatly benefit from a longitudinal design and consensus on the preferred analytical approach, it is apparent that (1) H-MRS studies have great potential in advancing our understanding of the role of the GABA system in the pathogenesis of psychiatric disorders. Hum Brain Mapp 37:3337-3352, 2016. © 2016 Wiley Periodicals

Evaluation of brain metabolism in autism. A {sup 1}H-MRS study

Energy Technology Data Exchange (ETDEWEB)

Otsuka, Hideki; Harada, Masafumi; Mori, Kenji; Taoka, Yoshiaki; Nishitani, Hiromu [Tokushima Univ. (Japan). School of Medicine

1998-01-01

We performed a {sup 1}H-MRS study on 13 autistic patients (2-15 y.o., 10 males, 3 females) and 10 normal children (6-14 y.o., 4 males, 6 females). An MR spectra of the bilateral amygdaloid-hippocampal regions, which play a very important role in the limbic system, was obtained for each subject using STEAM sequence (TR=5000 ms, TE=18 ms). In addition to the evaluation of signal intensity ratios, the absolute concentrations of 3 major metabolites, N-acetyl-aspartate (NAA), creatine/phosphocreatine (Cr) and choline-containing substances (Cho), were quantified by means of an internal reference method using unsuppressed tissue water. The concentration of NAA was decreased in autistic patients, and the difference between patients and normal controls was significant (right sided NAA level: autistic group-8.6{+-}2.1 mM, control group-11.0{+-}1.1 mM; left sided NAA level: autistic group-8.9{+-}1.8 mM, control group-10.5{+-}1.4 mM). We speculate that the decrease in NAA levels in the autistic patients reflects neuronal hypofunction or immature neurons. {sup 1}H-MRS may provide useful clinical information which is not readily obtainable with other imaging methods. (author)

Implementation of decommissioning criteria in the conceptual design of the MRS facility

International Nuclear Information System (INIS)

Gross, D.L.; Wilcox, A.D.; Huang, S.

1986-01-01

The US Department of Energy (DOE) selected the Ralph M. Parsons Company (RMP) to prepare the conceptual design of the Monitored Retrievable Storage (MRS) Facility. The purpose of this facility is to consolidate and temporarily store spent fuel from civilian nuclear power plants. In addition, it will overpack, handle, and store high-level radioactive waste from non-defense related sources. The Functional Design Criteria (FDC) prepared by Pacific Northwest Laboratories, as well as 10 CFR 72, requires the facility to be designed for decommissioning, with provisions to facilitate decontamination of structures and equipment to minimize the volume of radioactive wastes and contaminated equipment at the time of decommissioning. Many problems associated with decommissioning a nuclear facility have been identified in recent years and the design for the MRS Facility presents a unique opportunity for RMP to implement decommissioning criteria into the conceptual design of a major nuclear facility. The provisions made in the design to facilitate decommissioning include good housekeeping during operations, controlled personnel access, access for equipment removal, equipment design, installed radiation monitors, adequate work space, installed decontamination systems and areas, control of all effluents, and operational documentation. These topics will be the major points of discussion for this paper

Quantitative analysis of benign prostate hyperplasia with MRS in Chinese adult

International Nuclear Information System (INIS)

Li Feiyu; Wang Xiaoying; Huang Rong; Jiang Xuexiang; Ding Jianping; Zhou Liangping

2005-01-01

Objective: To evaluate the metabolic level of different types of benign prostate hyperplasia (BPH) with MRS in Chinese adult. Methods: Ten BPH patients verified by biopsy were divided into two types: glandular BPH and stromal BPH. 3DMRS were performed on the central zone to measure the ratio of (Cho + Cre) /Cit. Results: The mean ratio of (Cho + Cre)/Cit in central zone of glandular BPH was 0.55 ± 0.32, whereas that of stromal BPH was 0.87 ± 0.34. Statistically significant difference was detected between the two types of BPH (t=8.18, P<0.05). Conclusion: The metabolic levels of prostate with BPH could be measured with MRS quantitatively, and metabolic difference could be detected between glandular BPH and stromal BPH. (authors)

An expert computer program for classifying stars on the MK spectral classification system

International Nuclear Information System (INIS)

Gray, R. O.; Corbally, C. J.

2014-01-01

This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

An expert computer program for classifying stars on the MK spectral classification system

Energy Technology Data Exchange (ETDEWEB)

Gray, R. O. [Department of Physics and Astronomy, Appalachian State University, Boone, NC 26808 (United States); Corbally, C. J. [Vatican Observatory Research Group, Tucson, AZ 85721-0065 (United States)

2014-04-01

This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

Helping a community control its future: Potential negotiating packages and benefits for an MRS host

International Nuclear Information System (INIS)

Helvey, E.; Kane, D.; Trebules, V.

1993-01-01

The voluntary sitting process for the Monitored Retrievable Storage (MRS) facility set forth in the Nuclear Waste Policy Amendments Act (NWPAA) of 1987 provides a potential host community a unique opportunity to improve its present situation and to gain greater control over its future. To take full advantage of that opportunity throughout the life of the facility, an interested host must bring two things to the negotiating table: (1) a clear understanding of the special benefits, concerns and impacts associated with siting a controversial facility along with a detailed plan for addressing the requirements and impacts of such a facility; and (2) a vision of what the community wants to be in the future and list of specific measures it might achieve through negotiations that would help it realize that future. This paper investigates potential negotiating options a host might develop that, while addressing the impacts arena, also set forth terms by which the host can use the MRS to gain greater control over its unique set of resources and needs. The first section of this paper highlights the major concerns that a community might raise when debating whether to host an MRS and lists generic mitigation techniques that address those concerns. The second section pulls those mitigation techniques together into negotiating packages to show how the same concerns can be addressed differently depending on the strengths, weaknesses, and priorities of two different hypothetical host communities

A scaling transformation for classifier output based on likelihood ratio: Applications to a CAD workstation for diagnosis of breast cancer

International Nuclear Information System (INIS)

Horsch, Karla; Pesce, Lorenzo L.; Giger, Maryellen L.; Metz, Charles E.; Jiang Yulei

2012-01-01

Purpose: The authors developed scaling methods that monotonically transform the output of one classifier to the ''scale'' of another. Such transformations affect the distribution of classifier output while leaving the ROC curve unchanged. In particular, they investigated transformations between radiologists and computer classifiers, with the goal of addressing the problem of comparing and interpreting case-specific values of output from two classifiers. Methods: Using both simulated and radiologists' rating data of breast imaging cases, the authors investigated a likelihood-ratio-scaling transformation, based on ''matching'' classifier likelihood ratios. For comparison, three other scaling transformations were investigated that were based on matching classifier true positive fraction, false positive fraction, or cumulative distribution function, respectively. The authors explored modifying the computer output to reflect the scale of the radiologist, as well as modifying the radiologist's ratings to reflect the scale of the computer. They also evaluated how dataset size affects the transformations. Results: When ROC curves of two classifiers differed substantially, the four transformations were found to be quite different. The likelihood-ratio scaling transformation was found to vary widely from radiologist to radiologist. Similar results were found for the other transformations. Our simulations explored the effect of database sizes on the accuracy of the estimation of our scaling transformations. Conclusions: The likelihood-ratio-scaling transformation that the authors have developed and evaluated was shown to be capable of transforming computer and radiologist outputs to a common scale reliably, thereby allowing the comparison of the computer and radiologist outputs on the basis of a clinically relevant statistic.

Parameterization of a fuzzy classifier for the diagnosis of an industrial process

International Nuclear Information System (INIS)

Toscano, R.; Lyonnet, P.

2002-01-01

The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method, which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization, which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a DC motor showing the utility of this method. However in many cases the total knowledge necessary to the synthesis of the fuzzy diagnosis system (FDS) is not, in general, directly available. It must be extracted from an often-considerable mass of information. For this reason, a general methodology for the design of a FDS is presented and illustrated on a non-linear plant

A comparison of liver fat content as determined by magnetic resonance imaging-proton density fat fraction and MRS versus liver histology in non-alcoholic fatty liver disease.

Science.gov (United States)

Idilman, Ilkay S; Keskin, Onur; Celik, Azim; Savas, Berna; Elhan, Atilla Halil; Idilman, Ramazan; Karcaaltincaba, Musturay

2016-03-01

Many imaging methods have been defined for quantification of hepatic steatosis in non-alcoholic fatty liver disease (NAFLD). However, studies comparing the efficiency of magnetic resonance imaging-proton density fat fraction (MRI-PDFF), magnetic resonance spectroscopy (MRS), and liver histology for quantification of liver fat content are limited. To compare the efficiency of MRI-PDFF and MRS in the quantification of liver fat content in individuals with NAFLD. A total of 19 NAFLD patients underwent MRI-PDFF, MRS, and liver biopsy for quantification of liver fat content. The MR examinations were performed on a 1.5 HDx MRI system. The MRI protocol included T1-independent volumetric multi-echo gradient-echo imaging with T2* correction and spectral fat modeling and MRS with STEAM technique. A close correlation was observed between liver MRI-PDFF- and histology- determined steatosis (r = 0.743, P liver MRS- and histology-determined steatosis (r = 0.712, P quantification of hepatic steatosis, a high correlation was observed between the two MRI methods (r = 0.986, P steatosis from mild/no hepatic steatosis (P = 0.007 and 0.013, respectively), with no superiority between them (AUCMRI-PDFF = 0.881 ± 0.0856 versus AUCMRS = 0.857 ± 0.0924, P = 0.461). Both MRI-PDFF and MRS can be used for accurate quantification of hepatic steatosis. © The Foundation Acta Radiologica 2015.

The Message Reporting System of the ATLAS DAQ System

CERN Document Server

Caprini, M; Kolos, S; 10th ICATPP Conference on Astroparticle, Particle, Space Physics, Detectors and Medical Physics Applications

2008-01-01

The Message Reporting System (MRS) in the ATLAS data acquisition system (DAQ) is one package of the Online Software which acts as a glue of various elements of DAQ, High Level Trigger (HLT) and Detector Control System (DCS). The aim of the MRS is to provide a facility which allows all software components in ATLAS to report messages to other components of the distributed DAQ system. The processes requiring a MRS are on one hand applications that report error conditions or information and on the other hand message processors that receive reported messages. A message reporting application can inject one or more messages into the MRS at any time. An application wishing to receive messages can subscribe to a message group according to defined criteria. The application receives messages that fulfill the subscription criteria when they are reported to MRS. The receiver message processing can consist of anything from simply logging the messages in a file/terminal to performing message analysis. The inter-process comm...

Least Square Support Vector Machine Classifier vs a Logistic Regression Classifier on the Recognition of Numeric Digits

Directory of Open Access Journals (Sweden)

Danilo A. López-Sarmiento

2013-11-01

Full Text Available In this paper is compared the performance of a multi-class least squares support vector machine (LSSVM mc versus a multi-class logistic regression classifier to problem of recognizing the numeric digits (0-9 handwritten. To develop the comparison was used a data set consisting of 5000 images of handwritten numeric digits (500 images for each number from 0-9, each image of 20 x 20 pixels. The inputs to each of the systems were vectors of 400 dimensions corresponding to each image (not done feature extraction. Both classifiers used OneVsAll strategy to enable multi-classification and a random cross-validation function for the process of minimizing the cost function. The metrics of comparison were precision and training time under the same computational conditions. Both techniques evaluated showed a precision above 95 %, with LS-SVM slightly more accurate. However the computational cost if we found a marked difference: LS-SVM training requires time 16.42 % less than that required by the logistic regression model based on the same low computational conditions.

Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method

Science.gov (United States)

Nurdiansyah, Yanuar; Bukhori, Saiful; Hidayat, Rahmad

2018-04-01

There are many ways of implementing the use of sentiments often found in documents; one of which is the sentiments found on the product or service reviews. It is so important to be able to process and extract textual data from the documents. Therefore, we propose a system that is able to classify sentiments from review documents into two classes: positive sentiment and negative sentiment. We use Naive Bayes Classifier method in this document classification system that we build. We choose Movienthusiast, a movie reviews in Bahasa Indonesia website as the source of our review documents. From there, we were able to collect 1201 movie reviews: 783 positive reviews and 418 negative reviews that we use as the dataset for this machine learning classifier. The classifying accuracy yields an average of 88.37% from five times of accuracy measuring attempts using aforementioned dataset.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

Science.gov (United States)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Neuropharmacological and neurobiological relevance of in vivo ¹H-MRS of GABA and glutamate for preclinical drug discovery in mental disorders.

Science.gov (United States)

Waschkies, Conny F; Bruns, Andreas; Müller, Stephan; Kapps, Martin; Borroni, Edilio; von Kienlin, Markus; Rudin, Markus; Künnecke, Basil

2014-09-01

Proton magnetic resonance spectroscopy ((1)H-magnetic resonance spectroscopy (MRS)) is a translational modality with great appeal for neuroscience since the two major excitatory and inhibitory neurotransmitters, glutamate, and GABA, can be noninvasively quantified in vivo and have served to explore disease state and effects of drug treatment. Yet, if (1)H-MRS shall serve for decision making in preclinical pharmaceutical drug discovery, it has to meet stringent requirements. In particular, (1)H-MRS needs to reliably report neurobiologically relevant but rather small changes in neurometabolite levels upon pharmacological interventions and to faithfully appraise target engagement in the associated molecular pathways at pharmacologically relevant doses. Here, we thoroughly addressed these matters with a three-pronged approach. Firstly, we determined the sensitivity and reproducibility of (1)H-MRS in rat at 9.4 Tesla for detecting changes in GABA and glutamate levels in the striatum and the prefrontal cortex, respectively. Secondly, we evaluated the neuropharmacological and neurobiological relevance of the MRS readouts by pharmacological interventions with five well-characterized drugs (vigabatrin, 3-mercaptopropionate, tiagabine, methionine sulfoximine, and riluzole), which target key nodes in GABAergic and glutamatergic neurotransmission. Finally, we corroborated the MRS findings with ex vivo biochemical analyses of drug exposure and neurometabolite concentrations. For all five interventions tested, (1)H-MRS provided distinct drug dose-effect relationships in GABA and glutamate over preclinically relevant dose ranges and changes as low as 6% in glutamate and 12% in GABA were reliably detected from 16 mm(3) volumes-of-interest. Taken together, these findings demonstrate the value and limitation of quantitative (1)H-MRS of glutamate and GABA for preclinical pharmaceutical research in mental disorders.

Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers.

Directory of Open Access Journals (Sweden)

Mansour Alsaleh

Full Text Available Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In this study, we first explore the effect of the page language on spam detection features and we demonstrate how the best set of detection features varies according to the page language. We also study the performance of Google Penguin, a newly developed anti-web spamming technique for their search engine. Using spam pages in Arabic as a case study, we show that unlike similar English pages, Google anti-spamming techniques are ineffective against a high proportion of Arabic spam pages. We then explore multiple detection features for spam pages to identify an appropriate set of features that yields a high detection accuracy compared with the integrated Google Penguin technique. In order to build and evaluate our classifier, as well as to help researchers to conduct consistent measurement studies, we collected and manually labeled a corpus of Arabic web pages, including both benign and spam pages. Furthermore, we developed a browser plug-in that utilizes our classifier to warn users about spam pages after clicking on a URL and by filtering out search engine results. Using Google Penguin as a benchmark, we provide an illustrative example to show that language-based web spam classifiers are more effective for capturing spam contents.

On-line detection of apnea/hypopnea events using SpO2 signal: a rule-based approach employing binary classifier models.

Science.gov (United States)

Koley, Bijoy Laxmi; Dey, Debangshu

2014-01-01

This paper presents an online method for automatic detection of apnea/hypopnea events, with the help of oxygen saturation (SpO2) signal, measured at fingertip by Bluetooth nocturnal pulse oximeter. Event detection is performed by identifying abnormal data segments from the recorded SpO2 signal, employing a binary classifier model based on a support vector machine (SVM). Thereafter the abnormal segment is further analyzed to detect different states within the segment, i.e., steady, desaturation, and resaturation, with the help of another SVM-based binary ensemble classifier model. Finally, a heuristically obtained rule-based system is used to identify the apnea/hypopnea events from the time-sequenced decisions of these classifier models. In the developmental phase, a set of 34 time domain-based features was extracted from the segmented SpO2 signal using an overlapped windowing technique. Later, an optimal set of features was selected on the basis of recursive feature elimination technique. A total of 34 subjects were included in the study. The results show average event detection accuracies of 96.7% and 93.8% for the offline and the online tests, respectively. The proposed system provides direct estimation of the apnea/hypopnea index with the help of a relatively inexpensive and widely available pulse oximeter. Moreover, the system can be monitored and accessed by physicians through LAN/WAN/Internet and can be extended to deploy in Bluetooth-enabled mobile phones.

Evaluation of Cancer Metabolomics Using ex vivo High Resolution Magic Angle Spinning (HRMAS Magnetic Resonance Spectroscopy (MRS

Directory of Open Access Journals (Sweden)

Taylor L. Fuss

2016-03-01

Full Text Available According to World Health Organization (WHO estimates, cancer is responsible for more deaths than all coronary heart disease or stroke worldwide, serving as a major public health threat around the world. High resolution magic angle spinning (HRMAS magnetic resonance spectroscopy (MRS has demonstrated its usefulness in the identification of cancer metabolic markers with the potential to improve diagnosis and prognosis for the oncology clinic, due partially to its ability to preserve tissue architecture for subsequent histological and molecular pathology analysis. Capable of the quantification of individual metabolites, ratios of metabolites, and entire metabolomic profiles, HRMAS MRS is one of the major techniques now used in cancer metabolomic research. This article reviews and discusses literature reports of HRMAS MRS studies of cancer metabolomics published between 2010 and 2015 according to anatomical origins, including brain, breast, prostate, lung, gastrointestinal, and neuroendocrine cancers. These studies focused on improving diagnosis and understanding patient prognostication, monitoring treatment effects, as well as correlating with the use of in vivo MRS in cancer clinics.

A supervised contextual classifier based on a region-growth algorithm

DEFF Research Database (Denmark)

Lira, Jorge; Maletti, Gabriela Mariel

2002-01-01

A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class. A su...

Classifier Fusion With Contextual Reliability Evaluation.

Science.gov (United States)

Liu, Zhunga; Pan, Quan; Dezert, Jean; Han, Jun-Wei; He, You

2018-05-01

Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with contextual reliability evaluation (CF-CRE) based on inner reliability and relative reliability concepts. The inner reliability, represented by a matrix, characterizes the probability of the object belonging to one class when it is classified to another class. The elements of this matrix are estimated from the -nearest neighbors of the object. A cautious discounting rule is developed under belief functions framework to revise the classification result according to the inner reliability. The relative reliability is evaluated based on a new incompatibility measure which allows to reduce the level of conflict between the classifiers by applying the classical evidence discounting rule to each classifier before their combination. The inner reliability and relative reliability capture different aspects of the classification reliability. The discounted classification results are combined with Dempster-Shafer's rule for the final class decision making support. The performance of CF-CRE have been evaluated and compared with those of main classical fusion methods using real data sets. The experimental results show that CF-CRE can produce substantially higher accuracy than other fusion methods in general. Moreover, CF-CRE is robust to the changes of the number of nearest neighbors chosen for estimating the reliability matrix, which is appealing for the applications.

Evaluating the Performance of Multiple Classifier Systems: A Matrix Algebra Representation of Boolean Fusion Rules

National Research Council Canada - National Science Library

Hill, Justin

2003-01-01

...., a logical OR, AND, or a majority vote of the classifiers in the system). An established method for evaluating a classifier is measuring some aspect of its Receiver Operating Characteristic (ROC...

Design of a Fuzzy Rule Base Expert System to Predict and Classify ...

African Journals Online (AJOL)

The main objective of design of a rule base expert system using fuzzy logic approach is to predict and forecast the risk level of cardiac patients to avoid sudden death. In this proposed system, uncertainty is captured using rule base and classification using fuzzy c-means clustering is discussed to overcome the risk level, ...

Real-Time EEG-Based Happiness Detection System

Directory of Open Access Journals (Sweden)

Noppadon Jatupaiboon

2013-01-01

Full Text Available We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8 gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma give a better result than low-frequency bands. Considering different time durations for emotion elicitation, that result from 30 seconds does not have significant difference compared with the result from 60 seconds. From all of these results, we implement real-time EEG-based happiness detection system using only one pair of channels. Furthermore, we develop games based on the happiness detection system to help user recognize and control the happiness.

A high resolution chromosome image processor for study purposes, NIRS-1000:CHROMO STUDY, and algorithm developing to classify radiation induced aberrations.

Science.gov (United States)

Yamamoto, M; Hayata, I; Furuta, S

1992-03-01

Since 1989 we have promoted a project to develop an automated scoring system of radiation induced chromosome aberrations. As a first step, a high resolution image processing system for study purposes, NIRS-1000:CHROMO STUDY, has been developed. It is composed of: (1) CHROMO MARKER whose main purpose is to mark on images to make image data base, (2) CHROMO ALGO whose purpose is algorithm development, and (3) METAPHASE RANKER whose purposes are metaphase finding and ranking with a high power objective lens. However, METAPHASE RANKER is presently under development. The system utilizes a high definition video system so as to realize the best spatial resolution that is achievable with an optical microscope using an objective lens (x 100, numerical aperture 1.4). The video camera has 1024 effective scan lines to realize 0.1 microns sampling on a specimen. The system resolution achieved on the hard copy is less than 0.3 microns on a specimen. A preliminary algorithm has been developed to classify the aberrations on the system using projection information of gray level. The preliminary test results on excellent 10 metaphases show that the correct classification ratio is 92.7%, that the detection rate of the aberrations is 83.3% and that the false positive rate is 6.1%.

Measurements of fuel and ablator ρR in Symmetry-Capsule implosions with the Magnetic Recoil neutron Spectrometer (MRS) on the National Ignition Facility

Energy Technology Data Exchange (ETDEWEB)

Gatu Johnson, M., E-mail: gatu@psfc.mit.edu; Frenje, J. A.; Li, C. K.; Séguin, F. H.; Petrasso, R. D. [Plasma Science and Fusion Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Bionta, R. M.; Casey, D. T.; Caggiano, J. A.; Hatarik, R.; Khater, H. Y.; Sayre, D. B. [Lawrence Livermore National Laboratory, Livermore, California 94550 (United States); Knauer, J. P.; Sangster, T. C. [Laboratory for Laser Energetics, University of Rochester, Rochester, New York 14623 (United States); Herrmann, H. W. [Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States); Kilkenny, J. D. [General Atomics, San Diego, California 92186 (United States)

2014-11-15

The Magnetic Recoil neutron Spectrometer (MRS) on the National Ignition Facility (NIF) measures the neutron spectrum in the energy range of 4–20 MeV. This paper describes MRS measurements of DT-fuel and CH-ablator ρR in DT gas-filled symmetry-capsule implosions at the NIF. DT-fuel ρR's of 80–140 mg/cm{sup 2} and CH-ablator ρR's of 400–680 mg/cm{sup 2} are inferred from MRS data. The measurements were facilitated by an improved correction of neutron-induced background in the low-energy part of the MRS spectrum. This work demonstrates the accurate utilization of the complete MRS-measured neutron spectrum for diagnosing NIF DT implosions.

Novel model of direct and indirect cost-benefit analysis of mechanical embolectomy over IV tPA for large vessel occlusions: a real-world dollar analysis based on improvements in mRS.

Science.gov (United States)

Mangla, Sundeep; O'Connell, Keara; Kumari, Divya; Shahrzad, Maryam

2016-01-20

Ischemic strokes result in significant healthcare expenditures (direct costs) and loss of quality-adjusted life years (QALYs) (indirect costs). Interventional therapy has demonstrated improved functional outcomes in patients with large vessel occlusions (LVOs), which are likely to reduce the economic burden of strokes. To develop a novel real-world dollar model to assess the direct and indirect cost-benefit of mechanical embolectomy compared with medical treatment with intravenous tissue plasminogen activator (IV tPA) based on shifts in modified Rankin scores (mRS). A cost model was developed including multiple parameters to account for both direct and indirect stroke costs. These were adjusted based upon functional outcome (mRS). The model compared IV tPA with mechanical embolectomy to assess the costs and benefits of both therapies. Direct stroke-related costs included hospitalization, inpatient and outpatient rehabilitation, home care, skilled nursing facilities, and long-term care facility costs. Indirect costs included years of life expectancy lost and lost QALYs. Values for the model cost parameters were derived from numerous resources and functional outcomes were derived from the MR CLEAN study as a reflective sample of LVOs. Direct and indirect costs and benefits for the two treatments were assessed using Microsoft Excel 2013. This cost-benefit model found a cost-benefit of mechanical embolectomy over IV tPA of $163 624.27 per patient and the cost benefit for 50 000 patients on an annual basis is $8 181 213 653.77. If applied widely within the USA, mechanical embolectomy will significantly reduce the direct and indirect financial burden of stroke ($8 billion/50 000 patients). Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Component-Based Approach in Learning Management System Development

Science.gov (United States)

Zaitseva, Larisa; Bule, Jekaterina; Makarov, Sergey

2013-01-01

The paper describes component-based approach (CBA) for learning management system development. Learning object as components of e-learning courses and their metadata is considered. The architecture of learning management system based on CBA being developed in Riga Technical University, namely its architecture, elements and possibilities are…

Postictal in situ MRS brain lactate in the rat kindling model.

Science.gov (United States)

Maton, B M; Najm, I M; Wang, Y; Lüders, H O; Ng, T C

1999-12-10

To determine the temporal and spatial extent of the lactate (Lact) changes as correlated with seizure characteristics and EEG changes in the rat kindling model. Prior studies using MRS have detected cerebral Lact postictally in animal models of seizures and in patients with intractable focal epilepsy. We performed MRS in sham control rats (n = 4) and in rats stimulated in the right hippocampus at two different stages of the kindling and at three time points after the seizures: 3 hours (n = 4 and 2). Lact/creatine (Cr) and N-acetylaspartate (NAA)/Cr ratios were measured in six contiguous voxels (three left, three right) covering the hippocampi, anterior and posterior regions, and compared with EEG and ictal behavior. Lact/Cr ratios were measured at a very low level in the sham control rats and in the >3-hour group. In the epilepsy.

MRS studies of muscle and heart in obesity and diabetes

NARCIS (Netherlands)

Prompers, J.J.; Nicolay, K.

2016-01-01

Type 2 diabetes (T2D) has reached epidemic proportions and is a major threat to global public health. In vivo magnetic resonance spectroscopy (MRS) allows the noninvasive study of tissuemetabolism and hasmade major contributions to our understanding of the etiology of insulin resistance and T2D.

Online LDA BASED brain-computer interface system to aid disabled people

Directory of Open Access Journals (Sweden)

Apdullah Yayık

2017-06-01

Full Text Available This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must focus on related object to convey desired needs. The system can convey desired needs correctly by detecting P300 wave in acquired 14-channel EEG signal and classifying using linear discriminant analysis classifier just in 15 seconds. Experiments have been carried out on 19 volunteers to validate developed BCI system. As a result, accuracy rate of 90.83% is achieved in online performance.

Automated time activity classification based on global positioning system (GPS) tracking data.

Science.gov (United States)

Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph

2011-11-14

Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.

Science.gov (United States)

Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2017-02-27

In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called 'shadow features' are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

Directory of Open Access Journals (Sweden)

Simon Fong

2017-02-01

Full Text Available In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

Neural network classifier of attacks in IP telephony

Science.gov (United States)

Safarik, Jakub; Voznak, Miroslav; Mehic, Miralem; Partila, Pavol; Mikulec, Martin

2014-05-01

Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

The reproducibility of 31-phosphorus MRS measures of muscle energetics at 3 Tesla in trained men.

Directory of Open Access Journals (Sweden)

Lindsay M Edwards

Full Text Available OBJECTIVE: Magnetic resonance spectroscopy (MRS provides an exceptional opportunity for the study of in vivo metabolism. MRS is widely used to measure phosphorus metabolites in trained muscle, although there are no published data regarding its reproducibility in this specialized cohort. Thus, the aim of this study was to assess the reproducibility of (31P-MRS in trained skeletal muscle. METHODS: We recruited fifteen trained men (VO(2peak = 4.7±0.8 L min(-1/58±8 mL kg(-1 min(-1 and performed duplicate MR experiments during plantar flexion exercise, three weeks apart. RESULTS: Measures of resting phosphorus metabolites were reproducible, with 1.7 mM the smallest detectable difference in phosphocreatine (PCr. Measures of metabolites during exercise were less reliable: exercising PCr had a coefficient of variation (CV of 27% during exercise, compared with 8% at rest. Estimates of mitochondrial function were variable, but experimentally useful. The CV of PCr(1/2t was 40%, yet much of this variance was inter-subject such that differences of <20% were detectable with n = 15, given a significance threshold of p<0.05. CONCLUSIONS: 31-phosphorus MRS provides reproducible and experimentally useful measures of phosphorus metabolites and mitochondrial function in trained human skeletal muscle.

Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

Directory of Open Access Journals (Sweden)

Eils Roland

2006-06-01

Full Text Available Abstract Background The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. Results A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. Conclusion This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Directory of Open Access Journals (Sweden)

Simon Fong

2013-01-01

Full Text Available Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

In vivo proton magnetic resonance spectroscopy (1H-MRS) evaluation of the metabolite concentration of optic radiation in primary open angle glaucoma

Energy Technology Data Exchange (ETDEWEB)

Sidek, Sabrilhakim [University of Malaya, Department of Biomedical Imaging, University Malaya Research Imaging Centre (UMRIC), Kuala Lumpur (Malaysia); Universiti Teknologi MARA, Medical Imaging Unit, Faculty of Medicine, Sg Buloh, Selangor (Malaysia); Ramli, Norlisah; Rahmat, Kartini; Kuo, Tan Li [University of Malaya, Department of Biomedical Imaging, University Malaya Research Imaging Centre (UMRIC), Kuala Lumpur (Malaysia); Ramli, Norlina Mohd; Abdulrahman, Fadzlina [University of Malaya, Department of Ophthalmology, Faculty of Medicine, Kuala Lumpur (Malaysia)

2016-12-15

To compare the metabolite concentration of optic radiation in glaucoma patients with that of healthy subjects using Proton Magnetic Resonance Spectroscopy (1H-MRS). 1H-MRS utilising the Single-Voxel Spectroscopy (SVS) technique was performed using a 3.0Tesla MRI on 45 optic radiations (15 from healthy subjects, 15 from mild glaucoma patients, and 15 from severe glaucoma patients). A standardised Volume of Interest (VOI) of 20 x 20 x 20 mm was placed in the region of optic radiation. Mild and severe glaucoma patients were categorised based on the Hodapp-Parrish-Anderson (HPA) classification. Mean and multiple group comparisons for metabolite concentration and metabolite concentration ratio between glaucoma grades and healthy subjects were obtained using one-way ANOVA. The metabolite concentration and metabolite concentration ratio between the optic radiations of glaucoma patients and healthy subjects did not demonstrate any significant difference (p > 0.05). Our findings show no significant alteration of metabolite concentration associated with neurodegeneration that could be measured by single-voxel 1H-MRS in optic radiation among glaucoma patients. (orig.)

Determination of cost effective waste management system receipt rates

International Nuclear Information System (INIS)

McKee, R.W.; Huber, H.D.

1991-01-01

A comprehensive logistics and cost analysis has been carried out to determine if there are potential benefits to the high-level waste management system for receipt rates other than the current 3,000 MTU/yr design-basis receipt rate. The scope of the analysis includes both a Repository-Only System and a Storage-Only or Basic MRS System. To allow for current uncertainties in facility startup scheduling, cases considering repository startup dates of 2010 and 2015 and MRS startup dates of 1998 and three years prior to the repository have been evaluated. Receipt rates ranging from 1,500 to 6,000 MTU/yr have been considered for both the MRS and the repository. Higher receipt rates appear to be economically justified for both the repository and an MRS. For a repository-only system, minimum costs are found at a repository receipt rate of 6,000 MTU/yr. When a storage-only MRS is included in the system, minimum system costs are also achieved at a repository receipt rate of 6,000 MTU/yr. However, the MRS receipt rate for minimum system costs depends on the MRS startup date and ranges from 3,500 to 6,000 MTU/yr. With a 1998 MRS and a 2010 repository, the added cost of providing the MRS is offset by at-reactor storage cost reductions and the total system cost of $10.0 billion is virtually the same as for the repository-only system

Iranian nuclear program: who decides in Tehran. Interview with Mrs Azadeh Kian-Thiebaut, April 9, 2008

International Nuclear Information System (INIS)

Hautecouverture, Benjamin

2008-01-01

Mrs Azadeh Kian-Thiebaut is Professor of Sociology at Paris VII University, and researcher at the CNRS Laboratory 'Iranian and Indian Worlds'. In this interview, Mrs Azadeh Kian-Thiebaut explains the mysteries of the Iranian government, the decision-making process and the possible lines of tension regarding the Iranian nuclear program in the context of parliamentary elections (March 14 - April 25, 2008), and about a year before the next presidential elections (June 2009)

An ensemble self-training protein interaction article classifier.

Science.gov (United States)

Chen, Yifei; Hou, Ping; Manderick, Bernard

2014-01-01

Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.

Upgrade of the Automatic Analysis System in the TJ-II Thomson Scattering Diagnostic: New Image Recognition Classifier and Fault Condition Detection

Energy Technology Data Exchange (ETDEWEB)

Makili, L.; Dormido-Canto, S. [UNED, Madrid (Spain); Vega, J.; Pastor, I.; Pereira, A.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M. [Association EuratomCIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Instituut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

2009-07-01

Full text of publication follows: An automatic image classification system has been in operation for years in the TJ-II Thomson diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut o density during ECH heating. Each kind of image implies the execution of different application software. Therefore, the classification system was developed to launch the corresponding software in an automatic way. The method to recognize the several classes was based on a learning system, in particular Support Vector Machines (SVM). Since the first implementation of the classifier, a relevant improvement has been accomplished in the diagnostic: a new notch filter is in operation, having a larger stray-light rejection at the ruby wavelength than the previous filter. On the other hand, its location in the optical system has been modified. As a consequence, the stray light pattern in the CCD image is located in a different position. In addition to these transformations, the power of neutral beams injected in the TJ-II plasma has been increased about a factor of 2. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. The creation of a new model (also based on SVM) under the present conditions has been necessary. Finally, specific error conditions in the data acquisition process can automatically be detected now. The recovering process can be automated, thereby avoiding the loss of data in ensuing discharges. (authors)

Resting functional imaging tools (MRS, SPECT, PET and PCT).

Science.gov (United States)

Van Der Naalt, J

2015-01-01

Functional imaging includes imaging techniques that provide information about the metabolic and hemodynamic status of the brain. Most commonly applied functional imaging techniques in patients with traumatic brain injury (TBI) include magnetic resonance spectroscopy (MRS), single photon emission computed tomography (SPECT), positron emission tomography (PET) and perfusion CT (PCT). These imaging modalities are used to determine the extent of injury, to provide information for the prediction of outcome, and to assess evidence of cerebral ischemia. In TBI, secondary brain damage mainly comprises ischemia and is present in more than 80% of fatal cases with traumatic brain injury (Graham et al., 1989; Bouma et al., 1991; Coles et al., 2004). In particular, while SPECT measures cerebral perfusion and MRS determines metabolism, PET is able to assess both perfusion and cerebral metabolism. This chapter will describe the application of these techniques in traumatic brain injury separately for the major groups of severity comprising the mild and moderate to severe group. The application in TBI and potential difficulties of each technique is described. The use of imaging techniques in children will be separately outlined. © 2015 Elsevier B.V. All rights reserved.

On Neglecting Chemical Exchange Effects When Correcting in Vivo 31P MRS Data for Partial Saturation

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Ouwerkerk, Ronald; Bottomley, Paul A.

2001-02-01

Signal acquisition in most MRS experiments requires a correction for partial saturation that is commonly based on a single exponential model for T1 that ignores effects of chemical exchange. We evaluated the errors in 31P MRS measurements introduced by this approximation in two-, three-, and four-site chemical exchange models under a range of flip-angles and pulse sequence repetition times (TR) that provide near-optimum signal-to-noise ratio (SNR). In two-site exchange, such as the creatine-kinase reaction involving phosphocreatine (PCr) and γ-ATP in human skeletal and cardiac muscle, errors in saturation factors were determined for the progressive saturation method and the dual-angle method of measuring T1. The analysis shows that these errors are negligible for the progressive saturation method if the observed T1 is derived from a three-parameter fit of the data. When T1 is measured with the dual-angle method, errors in saturation factors are less than 5% for all conceivable values of the chemical exchange rate and flip-angles that deliver useful SNR per unit time over the range T1/5 ≤ TR ≤ 2T1. Errors are also less than 5% for three- and four-site exchange when TR ≥ T1*/2, the so-called "intrinsic" T1's of the metabolites. The effect of changing metabolite concentrations and chemical exchange rates on observed T1's and saturation corrections was also examined with a three-site chemical exchange model involving ATP, PCr, and inorganic phosphate in skeletal muscle undergoing up to 95% PCr depletion. Although the observed T1's were dependent on metabolite concentrations, errors in saturation corrections for TR = 2 s could be kept within 5% for all exchanging metabolites using a simple interpolation of two dual-angle T1 measurements performed at the start and end of the experiment. Thus, the single-exponential model appears to be reasonably accurate for correcting 31P MRS data for partial saturation in the presence of chemical exchange. Even in systems where

In vivo 19F-MRS observation of 5-FU metabolism in fatty liver induced by choline-deficient diet

International Nuclear Information System (INIS)

Otsuka, Hideki; Harada, Masafumi; Nishitani, Hiromu; Koga, Keiko.

1996-01-01

Using 19 F-MRS, 5-FU metabolism was investigated in rat fatty liver. Fatty liver was induced by choline-deficient diet (CD diet). This study showed differences in 5-FU metabolism between normal and fatty liver. After laparotomy, a surface coil was placed directly on the liver surface. Spectra were continuously obtained after injection of 5-FU 100 mg/kg body weight via a catheter inserted into femoral vein. We made MRI and 1 H-MRS study to examine the lipid accumulation. Histological study was also performed using HE (hematoxylin-eosin) and oil red stain. The livers of rats fed a CD diet showed very high intensity on T 1 -WI. 1 H-MRS was very useful in deteminating the fat content because the fat ratio demonstrated by 1 H-MRS is well correlated to histological findings. In 19 F-MRS, we recognized the following four peaks: 5-FU, FBAL, Fnct (fluoronucleotide) and FUPA. The decrease of 5-FU was not very apparent, but compared to the normal liver, the formation of Fnct increased and the formation of FBAL was suppressed in fatty liver. The rats fed a CD diet for four weeks showed a higher Fnct peak and lower FBAL peak compared with the results of rats fed a CD diet for two weeks. In a CD diet group, liver cell degeneration and necrotic changes were observed histologically. It is reported that cell degeneration is followed by cell proliferation in fatty liver induced by a choline deficient diet, and the high Fnct peak found in our study may reflect this phenomenon. The high Fnct peak on 19 F-MRS may correspond to recovering reaction from liver injury like fatty liver. (author)

Two distinct tumor phenotypes isolated from glioblastomas show different MRS characteristics

Czech Academy of Sciences Publication Activity Database

Thorsen, F.; Jirák, D.; Wang, J.; Syková, Eva; Bjerkvig, R.; Enger, P.O.; van der Kogel, A.; Hájek, M.

2008-01-01

Roč. 21, č. 8 (2008), s. 830-838 ISSN 0952-3480 Grant - others:EU(NO) LSHC-CT-2004-504743 Institutional research plan: CEZ:AV0Z50390512 Keywords : Glioblastoma * Proton MRS * Creatine Subject RIV: FH - Neurology Impact factor: 4.329, year: 2008

Clinical research on alterations of brain MRI and 1H-MRS in chronic hepatic disease

International Nuclear Information System (INIS)

Long Liling; Li Xiangrong; Hong Zhongkui

2006-01-01

Objective: To study the abnormal findings and metabolic alterations of the brain in chronic hepatic disease with MRI and 1 H magnetic resonance spectroscopy ( 1 H-MRS) for better understanding the clinical significance of pallidal hyperintensity and the role in the diagnosis and treatment of hepatic encephalopathy (HE). Methods: Brain MRI and 1 H-MRS examination were performed in 50 patients with chronic hepatic disease and 20 healthy volunteers. The pallidus index (PI) was calculated and the height of resonance peaks of Glx was measured. The correlation between PI and Child/Pugh classification, and the association between blood ammonia and the spectroscopic alterations were studied. Pre-and post-therapeutic comparative study was also conducted in 5 cases with chronic HE. Results: PI was gradually increased from healthy volunteers to patients with chronic hepatitis and liver cirrhosis (1.01± 0.04, 1.06±0.09, and 1.18±0.09), and the differences in PI value among them were significant (F=22.294, P 1 -weighted MRI disappeared and the abnormal metabolic alterations returned to normal range 5 to 6 months after successful liver transplantation. However, the normalization of 1 H-MRS alterations preceded the disappearance of pallidal hyperintensities. Conclusion: PI can be an index of reference for liver dysfunction. Glx is more sensitive than blood ammonia in detecting the brain dysfunction. MRI and 1 H-MRS are reliable techniques in the diagnosis and evaluation of therapy for hepatic encephalopathy. (authors)

Development and analysis of a low-cost screening tool to identify and classify hearing loss in children: a proposal for developing countries

Directory of Open Access Journals (Sweden)

Alessandra Giannella Samelli

2011-01-01

Full Text Available OBJECTIVE: A lack of attention has been given to hearing health in primary care in developing countries. A strategy involving low-cost screening tools may fill the current gap in hearing health care provided to children. Therefore, it is necessary to establish and adopt lower-cost procedures that are accessible to underserved areas that lack other physical or human resources that would enable the identification of groups at risk for hearing loss. The aim of this study was to develop and analyze the efficacy of a low-cost screening tool to identify and classify hearing loss in children. METHODS: A total of 214 2-to-10 year-old children participated in this study. The study was conducted by providing a questionnaire to the parents and comparing the answers with the results of a complete audiological assessment. Receiver operating characteristic (ROC curves were constructed, and discriminant analysis techniques were used to classify each child based on the total score. RESULTS: We found conductive hearing loss in 39.3% of children, sensorineural hearing loss in 7.4% and normal hearing in 53.3%. The discriminant analysis technique provided the following classification rule for the total score on the questionnaire: 0 to 4 points - normal hearing; 5 to 7 points - conductive hearing loss; over 7 points - sensorineural hearing loss. CONCLUSION: Our results suggest that the questionnaire could be used as a screening tool to classify children with normal hearing or hearing loss and according to the type of hearing loss based on the total questionnaire score

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

Science.gov (United States)

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

Directory of Open Access Journals (Sweden)

Yi Zhang

2015-01-01

Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Preliminary assessment of radiological doses in alternative waste management systems without an MRS facility

International Nuclear Information System (INIS)

Schneider, K.J.; Pelto, P.J.; Daling, P.M.; Lavender, J.C.; Fecht, B.A.

1986-06-01

This report presents generic analyses of radiological dose impacts of nine hypothetical changes in the operation of a waste management system without a monitored retrievable storage (MRS) facility. The waste management activities examined in this study include those for handling commercial spent fuel at nuclear power reactors and at the surface facilities of a deep geologic repository, and the transportation of spent fuel by rail and truck between the reactors and the repository. In the reference study system, the radiological doses to the public and to the occupational workers are low, about 170 person-rem/1000 metric ton of uranium (MTU) handled with 70% of the fuel transported by rail and 30% by truck. The radiological doses to the public are almost entirely from transportation, whereas the doses to the occupational workers are highest at the reactors and the repository. Operating alternatives examined included using larger transportation casks, marshaling rail cars into multicar dedicated trains, consolidating spent fuel at the reactors, and wet or dry transfer options of spent fuel from dry storage casks. The largest contribution to radiological doses per unit of spent fuel for both the public and occupational workers would result from use of truck transportation casks, which are smaller than rail casks. Thus, reducing the number of shipments by increasing cask sizes and capacities (which also would reduce the number of casks to be handled at the terminals) would reduce the radiological doses in all cases. Consolidating spent fuel at the reactors would reduce the radiological doses to the public but would increase the doses to the occupational workers at the reactors

Development of methods for quantitative in vivo NMR and their application to the study of hepatic encephalopathy in the brain

International Nuclear Information System (INIS)

Graaf, A.A. de.

1989-01-01

The aim of the work presented in this thesis was to develop reliable methods for quantitative MRS that are medically relevant for the study of Hepatic Encephalopathy (HE) in rats. The required modifications of the initiation and control software of the 7 Tesla spectrometer system of the Spin Imaging group at the Technical University Delft (Netherlands), are described. Experimental methods for localized, water suppressed 1 H MRS with a surface coil, including Spectroscopic Imaging, were developed in order to solve the problems of irreproducibility and spectral overlap caused by water and lipid signals. A method for correction of line-shape distortions as a consequence of static magnetic field imperfections was developed and evaluated both theoretically and experimentally. An approach to solve the problems in the quantification of the 1 H MRS spectra, caused especially by spectral overlap, frequency dependent intensity distortions and intensity modulations in coupled spin systems, was developed and evaluated. The brain energy state during HE was investigated using 31 P MRS. The developed methods for quantitative 1 H MRS were applied to monitor the concentrations of severeal important brain amino acids and other metabolic compounds during the development of acute HE, and during the development of ammonia induced encephalopathy in two different animal models. (author). 201 refs.; 32 figs.; 28 schemes.; 11 tabs

High-resolution measurements of the DT neutron spectrum using new CD foils in the Magnetic Recoil neutron Spectrometer (MRS) on the National Ignition Facility

Energy Technology Data Exchange (ETDEWEB)

Gatu Johnson, M., E-mail: gatu@psfc.mit.edu; Frenje, J. A.; Li, C. K.; Petrasso, R. D.; Séguin, F. H. [Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Bionta, R. M.; Casey, D. T.; Eckart, M. J.; Grim, G. P.; Hartouni, E. P.; Hatarik, R.; Sayre, D. B.; Skulina, K.; Yeamans, C. B. [Lawrence Livermore National Laboratory, Livermore, California 94550 (United States); Farrell, M. P.; Hoppe, M.; Kilkenny, J. D.; Reynolds, H. G.; Schoff, M. E. [General Atomics, San Diego, California 92186 (United States)

2016-11-15

The Magnetic Recoil neutron Spectrometer (MRS) on the National Ignition Facility measures the DT neutron spectrum from cryogenically layered inertial confinement fusion implosions. Yield, areal density, apparent ion temperature, and directional fluid flow are inferred from the MRS data. This paper describes recent advances in MRS measurements of the primary peak using new, thinner, reduced-area deuterated plastic (CD) conversion foils. The new foils allow operation of MRS at yields 2 orders of magnitude higher than previously possible, at a resolution down to ∼200 keV FWHM.

Classifying smoking urges via machine learning.

Science.gov (United States)

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

Global brain metabolic quantification with whole-head proton MRS at 3 T.

Science.gov (United States)

Kirov, Ivan I; Wu, William E; Soher, Brian J; Davitz, Matthew S; Huang, Jeffrey H; Babb, James S; Lazar, Mariana; Fatterpekar, Girish; Gonen, Oded

2017-10-01

Total N-acetyl-aspartate + N-acetyl-aspartate-glutamate (NAA), total creatine (Cr) and total choline (Cho) proton MRS ( 1 H-MRS) signals are often used as surrogate markers in diffuse neurological pathologies, but spatial coverage of this methodology is limited to 1%-65% of the brain. Here we wish to demonstrate that non-localized, whole-head (WH) 1 H-MRS captures just the brain's contribution to the Cho and Cr signals, ignoring all other compartments. Towards this end, 27 young healthy adults (18 men, 9 women), 29.9 ± 8.5 years old, were recruited and underwent T 1 -weighted MRI for tissue segmentation, non-localizing, approximately 3 min WH 1 H-MRS (T E /T R /T I  = 5/10/940 ms) and 30 min 1 H-MR spectroscopic imaging (MRSI) (T E /T R  = 35/2100 ms) in a 360 cm 3 volume of interest (VOI) at the brain's center. The VOI absolute NAA, Cr and Cho concentrations, 7.7 ± 0.5, 5.5 ± 0.4 and 1.3 ± 0.2 mM, were all within 10% of the WH: 8.6 ± 1.1, 6.0 ± 1.0 and 1.3 ± 0.2 mM. The mean NAA/Cr and NAA/Cho ratios in the WH were only slightly higher than the "brain-only" VOI: 1.5 versus 1.4 (7%) and 6.6 versus 5.9 (11%); Cho/Cr were not different. The brain/WH volume ratio was 0.31 ± 0.03 (brain ≈ 30% of WH volume). Air-tissue susceptibility-driven local magnetic field changes going from the brain outwards showed sharp gradients of more than 100 Hz/cm (1 ppm/cm), explaining the skull's Cr and Cho signal losses through resonance shifts, line broadening and destructive interference. The similarity of non-localized WH and localized VOI NAA, Cr and Cho concentrations and their ratios suggests that their signals originate predominantly from the brain. Therefore, the fast, comprehensive WH- 1 H-MRS method may facilitate quantification of these metabolites, which are common surrogate markers in neurological disorders. Copyright © 2017 John Wiley & Sons, Ltd.

Classifier fusion for VoIP attacks classification

Science.gov (United States)

Safarik, Jakub; Rezac, Filip

2017-05-01

SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

Snoring classified: The Munich-Passau Snore Sound Corpus.

Science.gov (United States)

Janott, Christoph; Schmitt, Maximilian; Zhang, Yue; Qian, Kun; Pandit, Vedhas; Zhang, Zixing; Heiser, Clemens; Hohenhorst, Winfried; Herzog, Michael; Hemmert, Werner; Schuller, Björn

2018-03-01

Snoring can be excited in different locations within the upper airways during sleep. It was hypothesised that the excitation locations are correlated with distinct acoustic characteristics of the snoring noise. To verify this hypothesis, a database of snore sounds is developed, labelled with the location of sound excitation. Video and audio recordings taken during drug induced sleep endoscopy (DISE) examinations from three medical centres have been semi-automatically screened for snore events, which subsequently have been classified by ENT experts into four classes based on the VOTE classification. The resulting dataset containing 828 snore events from 219 subjects has been split into Train, Development, and Test sets. An SVM classifier has been trained using low level descriptors (LLDs) related to energy, spectral features, mel frequency cepstral coefficients (MFCC), formants, voicing, harmonic-to-noise ratio (HNR), spectral harmonicity, pitch, and microprosodic features. An unweighted average recall (UAR) of 55.8% could be achieved using the full set of LLDs including formants. Best performing subset is the MFCC-related set of LLDs. A strong difference in performance could be observed between the permutations of train, development, and test partition, which may be caused by the relatively low number of subjects included in the smaller classes of the strongly unbalanced data set. A database of snoring sounds is presented which are classified according to their sound excitation location based on objective criteria and verifiable video material. With the database, it could be demonstrated that machine classifiers can distinguish different excitation location of snoring sounds in the upper airway based on acoustic parameters. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development of FPGA-based safety-related I and C systems

Energy Technology Data Exchange (ETDEWEB)

Goto, Y.; Oda, N.; Miyazaki, T.; Hayashi, T.; Sato, T.; Igawa, S. [08, Shinsugita-cho, Isogo-ku, Yokohama 235-8523 (Japan); 1, Toshiba-cho, Fuchu, Tokyo 183-8511 (Japan)

2006-07-01

Toshiba has developed Non-rewritable (NRW) Field Programmable Gate Array (FPGA)-based safety-related Instrumentation and Control (I and C) system [1]. Considering application to safety-related systems, nonvolatile and non-rewritable FPGA which is impossible to be changed after once manufactured has been adopted in Toshiba FPGA-based system. FPGA is a device which consists only of defined digital circuit: hardware, which performs defined processing. FPGA-based system solves issues existing both in the conventional systems operated by analog circuits (analog-based system) and the systems operated by central processing unit (CPU-based system). The advantages of applying FPGA are to keep the long-life supply of products, improving testability (verification), and to reduce the drift which may occur in analog-based system. The system which Toshiba developed this time is Power Range Monitor (PRM). Toshiba is planning to expand application of FPGA-based technology by adopting this development method to the other safety-related systems from now on. (authors)

Nonlinear Knowledge in Kernel-Based Multiple Criteria Programming Classifier

Science.gov (United States)

Zhang, Dongling; Tian, Yingjie; Shi, Yong

Kernel-based Multiple Criteria Linear Programming (KMCLP) model is used as classification methods, which can learn from training examples. Whereas, in traditional machine learning area, data sets are classified only by prior knowledge. Some works combine the above two classification principle to overcome the defaults of each approach. In this paper, we propose a model to incorporate the nonlinear knowledge into KMCLP in order to solve the problem when input consists of not only training example, but also nonlinear prior knowledge. In dealing with real world case breast cancer diagnosis, the model shows its better performance than the model solely based on training data.

Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

Directory of Open Access Journals (Sweden)

Zhibin Xiao

2017-02-01

Full Text Available Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity.

Overview of the US program for developing a waste disposal system for spent nuclear fuel and high-level waste

International Nuclear Information System (INIS)

Kay, C.E.

1988-01-01

Safe disposal of spent nuclear fuel and radioactive high-level waste (HLW) has been a matter of national concern ever since the first US civilian nuclear reactor began generating electricity in 1957. Based on current projections of commercial generating capacity, by the turn of the century, there will be >40,000 tonne of spent fuel in the Untied States. In addition to commercial spent fuel, defense HLW is generated in the United States and currently stored at three US Department of Energy (DOE) sites: The Nuclear Waste Policy Amendments Act of 1987 provided for financial incentives to host a repository or a monitored retrievable storage (MRS) facility; mandated the areas in which DOE's siting efforts should concentrate (Yucca Mountain, Nevada); required termination of site-specific activities at other sites; required a resisting process for an MRS facility, which DOE had proposed as an integral part of the waste disposal system; terminated all activities for identifying candidates for a second repository; established an 11-member Nuclear Waste Technical Review Board; established a three-member MRS commission to be appointed by heads of the US Senate and House; directed the President to appoint a negotiator to seek a state or Indian tribe willing to host a repository or MRS facility at a suitable site and to negotiate terms and conditions under which the state or tribe would be willing to host such a facility; and amended, adjusted, or established other requirements contained in the 1982 law

Mrs. Aristotele’s teeth : How SOEP transformed life satisfaction research

NARCIS (Netherlands)

Headey, Bruce; Muffels, R.J.A.; Erlinghagen, M.; Hank, K.; Kreyenfeld, M.

2018-01-01

Aristotle thought that women were inferior to men, and cited the well-known »fact« that they have fewer teeth as evidence to support his belief. Bertrand Russell pointed out that all he had to do to check this »fact« was ask Mrs. Aristotle to open her mouth. SOEP has played the same role in research

Diagnostics of synchronous motor based on analysis of acoustic signals with application of MFCC and Nearest Mean classifier

OpenAIRE

Adam Głowacz; Witold Głowacz; Andrzej Głowacz

2010-01-01

The paper presents method of diagnostics of imminent failure conditions of synchronous motor. This method is based on a study ofacoustic signals generated by synchronous motor. Sound recognition system is based on algorithms of data processing, such as MFCC andNearest Mean classifier with cosine distance. Software to recognize the sounds of synchronous motor was implemented. The studies werecarried out for four imminent failure conditions of synchronous motor. The results confirm that the sys...

Development of FPGA-based safety-related instrumentation and control systems

Energy Technology Data Exchange (ETDEWEB)

Oda, N.; Tanaka, A.; Izumi, M.; Tarumi, T.; Sato, T. [Toshiba Corporation, Isogo Nuclear Engineering Center, Yokohama (Japan)

2004-07-01

Toshiba has developed systems which perform signal processing by field programmable gate arrays (FPGA) for safety-related instrumentation and control systems. FPGA is a device which consists only of defined digital circuit: hardware, which performs defined processing. FPGA-based system solves issues existing both in the conventional systems operated by analog circuits (analog-based system) and the systems operated by central processing units (CPU-based system). The advantages of applying FPGA are to keep the long-life supply of products, improving testability (verification), and to reduce the drift which may occur in analog-based system. Considering application to safety-related systems, nonvolatile and non rewritable FPGA which is impossible to be changed after once manufactured has been adopted in Toshiba FPGA-based system. The systems which Toshiba developed this time are Power range Monitor (PRM) and Trip Module (TM). These systems are compatible with the conventional analog-based systems and the CPU-based systems. Therefore, requested cost for upgrading will be minimized. Toshiba is planning to expand application of FPGA-based technology by adopting this development method to the other safety-related systems from now on. (authors)

In vivo quantitation of metabolite concentrations in the brain by means of proton MRS

DEFF Research Database (Denmark)

Henriksen, O

1995-01-01

MRS offers unique possibilities for non-invasive studies of biochemistry in the human brain in vivo. A growing body of evidence suggests that proton MRS may contribute to the clinical evaluation of a number of pathologies including ischaemia, tumours, epilepsy, metabolic and neuropaediatric...... (kg wet weight)-1 range between 8.2 and 17.2 (mean 10.2), 5.9 and 11.6 (mean 7.2), 1.1 and 2.0 (mean 1.5) and 3.9 and 8.1 (mean 6.1), respectively. So far only a limited number of clinical studies has been published including studies of acute stroke, multiple sclerosis and Alzheimer's disease...

Surface inspection system for industrial components based on shape from shading minimization approach

Science.gov (United States)

Kotan, Muhammed; Öz, Cemil

2017-12-01

An inspection system using estimated three-dimensional (3-D) surface characteristics information to detect and classify the faults to increase the quality control on the frequently used industrial components is proposed. Shape from shading (SFS) is one of the basic and classic 3-D shape recovery problems in computer vision. In our application, we developed a system using Frankot and Chellappa SFS method based on the minimization of the selected basis function. First, the specialized image acquisition system captured the images of the component. To eliminate noise, wavelet transform is applied to the taken images. Then, estimated gradients were used to obtain depth and surface profiles. Depth information was used to determine and classify the surface defects. Also, a comparison made with some linearization-based SFS algorithms was discussed. The developed system was applied to real products and the results indicated that using SFS approaches is useful and various types of defects can easily be detected in a short period of time.

Development of an ultrasonic weld inspection system based on image processing and neural networks

Science.gov (United States)

Roca Barceló, Fernando; Jaén del Hierro, Pedro; Ribes Llario, Fran; Real Herráiz, Julia

2018-04-01

Several types of discontinuities and defects may be present on a weld, thus leading to a considerable reduction of its resistance. Therefore, ensuring a high welding quality and reliability has become a matter of key importance for many construction and industrial activities. Among the non-destructive weld testing and inspection techniques, the time-of-flight diffraction (TOFD) arises as a very safe (no ionising radiation), precise, reliable and versatile practice. However, this technique presents a relevant drawback, associated to the appearance of speckle noise that should be addressed. In this regard, this paper presents a new, intelligent and automatic method for weld inspection and analysis, based on TOFD, image processing and neural networks. The developed system is capable of detecting weld defects and imperfections with accuracy, and classify them into different categories.

Reducing variability in the output of pattern classifiers using histogram shaping

International Nuclear Information System (INIS)

Gupta, Shalini; Kan, Chih-Wen; Markey, Mia K.

2010-01-01

Purpose: The authors present a novel technique based on histogram shaping to reduce the variability in the output and (sensitivity, specificity) pairs of pattern classifiers with identical ROC curves, but differently distributed outputs. Methods: The authors identify different sources of variability in the output of linear pattern classifiers with identical ROC curves, which also result in classifiers with differently distributed outputs. They theoretically develop a novel technique based on the matching of the histograms of these differently distributed pattern classifier outputs to reduce the variability in their (sensitivity, specificity) pairs at fixed decision thresholds, and to reduce the variability in their actual output values. They empirically demonstrate the efficacy of the proposed technique by means of analyses on the simulated data and real world mammography data. Results: For the simulated data, with three different known sources of variability, and for the real world mammography data with unknown sources of variability, the proposed classifier output calibration technique significantly reduced the variability in the classifiers' (sensitivity, specificity) pairs at fixed decision thresholds. Furthermore, for classifiers with monotonically or approximately monotonically related output variables, the histogram shaping technique also significantly reduced the variability in their actual output values. Conclusions: Classifier output calibration based on histogram shaping can be successfully employed to reduce the variability in the output values and (sensitivity, specificity) pairs of pattern classifiers with identical ROC curves, but differently distributed outputs.

Development of data acquisition software for VME based system

International Nuclear Information System (INIS)

Kumar, A.; Chatterjee, A.; Mahata, K.; Ramachandran, K.

2012-01-01

A data acquisition system for VME has been developed for use in accelerator based experiments. The development was motivated by the growing demand for higher throughput in view of the increasing size of experiments. VME based data acquisition system provides a powerful alternative to CAMAC standards on account of higher readout speeds (100 ns/word) resulting in reduced dead time. Further, high density VME modules are capable of providing up to 640 channels in a single VME crate with 21 slots. The software system LAMPS, earlier developed for CAMAC based system and used extensively in our laboratory and elsewhere has been modified for the present VME based system. The system makes use of the VME library to implement Chain Block Transfer Readout (CBLT) and gives the option of both Polling and Interrupt mode to acquire data. Practical throughput of ∼ 250 ns/word in zero suppressed mode has been achieved. (author)

A comparison of rule-based and machine learning approaches for classifying patient portal messages.

Science.gov (United States)

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

2017-09-01

Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard

A Naive-Bayes classifier for damage detection in engineering materials

Energy Technology Data Exchange (ETDEWEB)

Addin, O. [Laboratory of Intelligent Systems, Institute of Advanced Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor (Malaysia); Sapuan, S.M. [Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor (Malaysia)]. E-mail: sapuan@eng.upm.edu.my; Mahdi, E. [Department of Aerospace Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor (Malaysia); Othman, M. [Department of Communication Technology and Networks, Universiti Putra Malaysia, 43400 Serdang, Selangor (Malaysia)

2007-07-01

This paper is intended to introduce the Bayesian network in general and the Naive-Bayes classifier in particular as one of the most successful classification systems to simulate damage detection in engineering materials. A method for feature subset selection has also been introduced too. The method is based on mean and maximum values of the amplitudes of waves after dividing them into folds then grouping them by a clustering algorithm (e.g. k-means algorithm). The Naive-Bayes classifier and the feature sub-set selection method were analyzed and tested on two sets of data. The data sets were conducted based on artificial damages created in quasi isotopic laminated composites of the AS4/3501-6 graphite/epoxy system and ball bearing of the type 6204 with a steel cage. The Naive-Bayes classifier and the proposed feature subset selection algorithm have been shown as efficient techniques for damage detection in engineering materials.

Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

International Nuclear Information System (INIS)

Makili, L.; Vega, J.; Dormido-Canto, S.; Pastor, I.; Pereira, A.; Farias, G.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C.; Busch, P.

2010-01-01

An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

Energy Technology Data Exchange (ETDEWEB)

Makili, L. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Dormido-Canto, S., E-mail: sebas@dia.uned.e [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Pastor, I.; Pereira, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Farias, G. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Institut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

2010-07-15

An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

Designing a Web Spam Classifier Based on Feature Fusion in the Layered Multi-Population Genetic Programming Framework

Directory of Open Access Journals (Sweden)

Amir Hosein KEYHANIPOUR

2013-11-01

Full Text Available Nowadays, Web spam pages are a critical challenge for Web retrieval systems which have drastic influence on the performance of such systems. Although these systems try to combat the impact of spam pages on their final results list, spammers increasingly use more sophisticated techniques to increase the number of views for their intended pages in order to have more commercial success. This paper employs the recently proposed Layered Multi-population Genetic Programming model for Web spam detection task as well application of correlation coefficient analysis for feature space reduction. Based on our tentative results, the designed classifier, which is based on a combination of easy to compute features, has a very reasonable performance in comparison with similar methods.

MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

Science.gov (United States)

Chen, Lei; Kamel, Mohamed S.

2016-01-01

In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

Fingerprint prediction using classifier ensembles

CSIR Research Space (South Africa)

Molale, P

2011-11-01

Full Text Available ); logistic discrimination (LgD), k-nearest neighbour (k-NN), artificial neural network (ANN), association rules (AR) decision tree (DT), naive Bayes classifier (NBC) and the support vector machine (SVM). The performance of several multiple classifier systems...

Cerebral blood flow and metabolism of the patients with a carotid stenosis evaluated by SPECT and 1H-MRS

International Nuclear Information System (INIS)

Uno, Masaaki; Nishi, Kyouko; Shinno, Kiyohito; Nagahiro, Shinji; Ohtsuka, Hideki; Harada, Masafumi

1998-01-01

We examined cerebral blood flow (CBF) and metabolic states in patients with a severe carotid stenosis by SPECT and proton MRS ( 1 H-MRS). SPECT using 99m Tc-HMPAO and 1 H-MRS were performed in twenty five patients with over 70% carotid stenosis. Moreover, 10 patients were evaluated by these methods after carotid endarterectomy (CEA). N-acetyl-aspartate (NAA) on the ipsilateral side was reduced in 17 patients (Group A) while NAA of other 8 patients was not reduced (Group B). In Group A, although regional CBF was reduced in only 4 of 17 patients, 11 patients showed decline of cerebral vasoreactivity evaluated by acetazolamide injection. Concentration of NAA and regional CBF showed significant correlations. After CEA, ipsilateral NAA was increased significantly compared to preoperative NAA level. In 6 of 7 patients in Group A, cerebral vasoreactivity also improved postoperatively. These results indicated that ipsilatereal NAA metabolism and cerebral vasoreactivity can be improved after CEA. SPECT and 1 H-MRS were useful to evaluate CBF and metabolism in patients with carotid stenosis. (author)

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

OpenAIRE

Rosenberg, Ishai; Shabtai, Asaf; Rokach, Lior; Elovici, Yuval

2017-01-01

In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such...

Development of Spreadsheet-Based Integrated Transaction Processing Systems and Financial Reporting Systems

Science.gov (United States)

Ariana, I. M.; Bagiada, I. M.

2018-01-01

Development of spreadsheet-based integrated transaction processing systems and financial reporting systems is intended to optimize the capabilities of spreadsheet in accounting data processing. The purpose of this study are: 1) to describe the spreadsheet-based integrated transaction processing systems and financial reporting systems; 2) to test its technical and operational feasibility. This study type is research and development. The main steps of study are: 1) needs analysis (need assessment); 2) developing spreadsheet-based integrated transaction processing systems and financial reporting systems; and 3) testing the feasibility of spreadsheet-based integrated transaction processing systems and financial reporting systems. The technical feasibility include the ability of hardware and operating systems to respond the application of accounting, simplicity and ease of use. Operational feasibility include the ability of users using accounting applications, the ability of accounting applications to produce information, and control applications of the accounting applications. The instrument used to assess the technical and operational feasibility of the systems is the expert perception questionnaire. The instrument uses 4 Likert scale, from 1 (strongly disagree) to 4 (strongly agree). Data were analyzed using percentage analysis by comparing the number of answers within one (1) item by the number of ideal answer within one (1) item. Spreadsheet-based integrated transaction processing systems and financial reporting systems integrate sales, purchases, and cash transaction processing systems to produce financial reports (statement of profit or loss and other comprehensive income, statement of changes in equity, statement of financial position, and statement of cash flows) and other reports. Spreadsheet-based integrated transaction processing systems and financial reporting systems is feasible from the technical aspects (87.50%) and operational aspects (84.17%).

Occipital cortical proton MRS at 4 Tesla in human moderate MDMA polydrug users.

Science.gov (United States)

Cowan, Ronald L; Bolo, Nicolas R; Dietrich, Mary; Haga, Erica; Lukas, Scott E; Renshaw, Perry F

2007-08-15

The recreational drug MDMA (3,4, methylenedioxymethamphetamine; sold under the street name of Ecstasy) is toxic to serotonergic axons in some animal models of MDMA administration. In humans, MDMA use is associated with alterations in markers of brain function that are pronounced in occipital cortex. Among neuroimaging methods, magnetic resonance spectroscopy (MRS) studies of brain metabolites N-acetylaspartate (NAA) and myoinositol (MI) at a field strength of 1.5 Tesla (T) reveal inconsistent results in MDMA users. Because higher field strength proton MRS has theoretical advantages over lower field strengths, we used proton MRS at 4.0 T to study absolute concentrations of occipital cortical NAA and MI in a cohort of moderate MDMA users (n=9) versus non-MDMA using (n=7) controls. Mean NAA in non-MDMA users was 10.47 mM (+/-2.51), versus 9.83 mM (+/-1.94) in MDMA users. Mean MI in non-MDMA users was 7.43 mM (+/-.68), versus 6.57 mM (+/-1.59) in MDMA users. There were no statistical differences in absolute metabolite levels for NAA and MI in occipital cortex of MDMA users and controls. These findings are not supportive of MDMA-induced alterations in NAA or MI levels in this small sample of moderate MDMA users. Limitations to this study suggest caution in the interpretation of these results.

NWPA progress: At-reactor through MRS

International Nuclear Information System (INIS)

Klein, K.A.

1986-01-01

Sufficient pressures and consensus were generated to enact the Nuclear Waste Policy Act in January 1983. The Act appropriately focuses on deep geologic disposal, but it also addresses other waste management activities: utilities are assigned primary responsibility for dealing with at-reactor storage problems prior to availability of Federal waste management facilities. DOE is assigned responsibility to help utilities deal with these problems and to provide limited interim storage capacity for those utilities that cannot add sufficient capacity soon enough; DOE is assigned responsibility for transporting fuel from the utilities; and DOE is assigned responsibility for providing Congress a site-specific proposal for one more MRS facilities. Utilities, seeing this tangible progress, can begin confidently, and far less conservatively, for their storage needs

Proton MRS of the peritumoral brain.

Science.gov (United States)

Chernov, Mikhail F; Kubo, Osami; Hayashi, Motohiro; Izawa, Masahiro; Maruyama, Takashi; Usukura, Masao; Ono, Yuko; Hori, Tomokatsu; Takakura, Kintomo

2005-02-15

Long-echo (TR: 2000 ms, TE: 136 ms) proton MRS of the cerebral tissue in the vicinity to intracranial lesion was done in 15 patients, mainly with parenchymal brain tumors. Significant decrease of N-acetylaspartate (NAA) (Plactate (Plactate in the lesion (Plactate in the lesion compared to perilesional brain (Plactate in the lesion is associated with lower relative NAA content in the perilesional brain tissue, independently on the presence or absence of any other factor, including brain edema (Plactate diffused from the tumor, or other metabolites secreted by lactate-producing neoplasm, should be considered as important contributors to the neuronal dysfunction in the surrounding brain. Decrease of NAA in the vicinity to intracranial lesions may reflect neuronal alteration responsible for associated epilepsy.

Classified one-step high-radix signed-digit arithmetic units

Science.gov (United States)

Cherri, Abdallah K.

1998-08-01

High-radix number systems enable higher information storage density, less complexity, fewer system components, and fewer cascaded gates and operations. A simple one-step fully parallel high-radix signed-digit arithmetic is proposed for parallel optical computing based on new joint spatial encodings. This reduces hardware requirements and improves throughput by reducing the space-bandwidth produce needed. The high-radix signed-digit arithmetic operations are based on classifying the neighboring input digit pairs into various groups to reduce the computation rules. A new joint spatial encoding technique is developed to present both the operands and the computation rules. This technique increases the spatial bandwidth product of the spatial light modulators of the system. An optical implementation of the proposed high-radix signed-digit arithmetic operations is also presented. It is shown that our one-step trinary signed-digit and quaternary signed-digit arithmetic units are much simpler and better than all previously reported high-radix signed-digit techniques.

Preliminary characterization of risks in the nuclear waste management system based on information in the literature

International Nuclear Information System (INIS)

Daling, P.M.; Rhoads, R.E.; Van Luick, A.E.; Fecht, B.A.; Nilson, S.A.; Sevigny, N.L.; Armstrong, G.R.; Hill, D.H.; Rowe, M.; Stern, E.

1992-01-01

This document presents preliminary information on the radiological and nonradiological risks in the nuclear waste management system. The objective of the study was to (1) review the literature containing information on risks in the nuclear waste management system and (2) use this information to develop preliminary estimates of the potential magnitude of these risks. Information was collected on a broad range of risk categories to assist the US Department of Energy (DOE) in communicating information about the risks in the waste management systems. The study examined all of the portions of the nuclear waste management system currently expected to be developed by the DOE. The scope of this document includes the potential repository, the integral MRS facility, and the transportation system that supports the potential repository and the MRS facility. Relevant literature was reviewed for several potential repository sites and geologic media. A wide range of ''risk categories'' are addressed in this report: (1) public and occupational risks from accidents that could release radiological materials, (2) public and occupational radiation exposure resulting from routine operations, (3) public and occupational risks from accidents involving hazards other than radioactive materials, and (4) public and occupational risks from exposure to nonradioactive hazardous materials during routine operations. The report is intended to provide a broad spectrum of risk-related information about the waste management system. This information is intended to be helpful for planning future studies

Pregenual Anterior Cingulate Dysfunction Associated with Depression in OCD: An Integrated Multimodal fMRI/1H MRS Study.

Science.gov (United States)

Tadayonnejad, Reza; Deshpande, Rangaprakash; Ajilore, Olusola; Moody, Teena; Morfini, Francesca; Ly, Ronald; O'Neill, Joseph; Feusner, Jamie D

2018-04-01

Depression is a commonly occurring symptom in obsessive-compulsive disorder (OCD), and is associated with worse functional impairment, poorer quality of life, and poorer treatment response. Understanding the underlying neurochemical and connectivity-based brain mechanisms of this important symptom domain in OCD is necessary for development of novel, more globally effective treatments. To investigate biopsychological mechanisms of comorbid depression in OCD, we examined effective connectivity and neurochemical signatures in the pregenual anterior cingulate cortex (pACC), a structure known to be involved in both OCD and depression. Resting-state functional magnetic resonance imaging (fMRI) and 1 H magnetic resonance spectroscopy (MRS) data were obtained from participants with OCD (n=49) and healthy individuals of equivalent age and sex (n=25). Granger causality-based effective (directed) connectivity was used to define causal networks involving the right and left pACC. The interplay between fMRI connectivity, 1 H MRS and clinical data was explored by applying moderation and mediation analyses. We found that the causal influence of the right dorsal anterior midcingulate cortex (daMCC) on the right pACC was significantly lower in the OCD group and showed significant correlation with depressive symptom severity in the OCD group. Lower and moderate levels of glutamate (Glu) in the right pACC significantly moderated the interaction between right daMCC-pACC connectivity and depression severity. Our results suggest a biochemical-connectivity-psychological model of pACC dysfunction contributing to depression in OCD, particularly involving intracingulate connectivity and glutamate levels in the pACC. These findings have implications for potential molecular and network targets for treatment of this multi-faceted psychiatric condition.

Local-global classifier fusion for screening chest radiographs

Science.gov (United States)

Ding, Meng; Antani, Sameer; Jaeger, Stefan; Xue, Zhiyun; Candemir, Sema; Kohli, Marc; Thoma, George

2017-03-01

Tuberculosis (TB) is a severe comorbidity of HIV and chest x-ray (CXR) analysis is a necessary step in screening for the infective disease. Automatic analysis of digital CXR images for detecting pulmonary abnormalities is critical for population screening, especially in medical resource constrained developing regions. In this article, we describe steps that improve previously reported performance of NLM's CXR screening algorithms and help advance the state of the art in the field. We propose a local-global classifier fusion method where two complementary classification systems are combined. The local classifier focuses on subtle and partial presentation of the disease leveraging information in radiology reports that roughly indicates locations of the abnormalities. In addition, the global classifier models the dominant spatial structure in the gestalt image using GIST descriptor for the semantic differentiation. Finally, the two complementary classifiers are combined using linear fusion, where the weight of each decision is calculated by the confidence probabilities from the two classifiers. We evaluated our method on three datasets in terms of the area under the Receiver Operating Characteristic (ROC) curve, sensitivity, specificity and accuracy. The evaluation demonstrates the superiority of our proposed local-global fusion method over any single classifier.

Queering The Construction Of Gender Identity In Chris Columbus’ Movie Mrs. Doubtfire

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Pradipta Agustina

2013-12-01

Full Text Available The construction of traditional gender roles has affected the understanding of being feminine and masculine. This understanding seems to influence gender performance in the film Mrs. Doubtfire. This one­hour­and­fifty­seven­minute film was directed by Chris Columbus. This study is conducted to examine how gender performativity is illustrated in the film and what ideology lies within the film. Queer theory, especially gender performativity by Judith Butler is used as the framework of the study. The study is done by observing and analysing chosen scenes from the film focusing on the performance of Daniel Hillard as Euphegenia Doubtfire. Narrative aspect of the film is not only the main concern; the non­narrative is also part of the analysis especially on costume, makeup, performance and color. The main finding of this study is this film in one hand celebrates traditional gender roles but on the other hand promotes gender as performance. Femininity is pictured as fluid. Therefore, it is also a performativity. The contestation between those two opposing ideas is smoothly wrapped through amusing film such as Mrs. Doubtfire. Abstrak: Film Mrs. Doubtfire karya Chris Columbus menampilkan konstruksi yang berbeda dengan konstruksi peran gender yang telah menjadi mainstream. Berdurasi 1 jam dan 57 menit, film ini menampilkan konstruksi maskulinitas dan femininitas yang dapat saling bertukar, cair, dan tidak baku. Studi ini mengkaji dua pertanyaan utama. Pertama, bagaimana konstruksi peran gender digugat melalui performativitas gender? Kedua, ideologi apa yang terdapat dalam film? Teori Queer terutama gender performativitas yang dikemukakan oleh Judith Butler menjadi kerangka penelitian ini. Penelitian ini dilakukan dengan mengobservasi dan menganalisis adegan terpilih dengan berfokus pada penampilan Daniel Hillard sebagai Euphegenia Doubtfire. Aspek naratif dalam film bukan satu­satunya perhatian utama. Aspek non­naratif juga menjadi bagian

Relationship between Barthel Index (BI and the Modified Rankin Scale (mRS Score in Assessing Functional Outcome in Acute Ischemic Stroke

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C S Mohanty

2016-01-01

Conclusion: Our study has demonstrated that stroke functional outcome can be predicted from the baseline BI and mRS scales. It is concluded thatBI and mRS Stroke scale can be used to prognosticate functional outcome at admission and at follow up.

Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing.