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Sample records for diagnosis system based

  1. Integrated Knowledge Based Expert System for Disease Diagnosis System

    Science.gov (United States)

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

    2017-08-01

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

  2. Probabilistic Model-Based Diagnosis for Electrical Power Systems

    Data.gov (United States)

    National Aeronautics and Space Administration — We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system,...

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

    Directory of Open Access Journals (Sweden)

    Deng Xiao-Wen

    2017-01-01

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

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

    CERN Document Server

    Borutzky, Wolfgang

    2015-01-01

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

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

    Science.gov (United States)

    Mishra, Swagat; Khilar, Pabitra Mohan

    2012-01-01

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

  6. Agent-based intelligent medical diagnosis system for patients.

    Science.gov (United States)

    Zhang, Yingfeng; Liu, Sichao; Zhu, Zhenfei; Si, Shubin

    2015-01-01

    According to the analysis of the challenges faced by the current public health circumstances such as the sharp increase in elderly patients, limited medical personnel, resources and technology, the agent-based intelligent medical diagnosis system for patients (AIMDS) is proposed in this research. Based on advanced sensing technology and professional medical knowledge, the AIMDS can output the appropriate medical prescriptions and food prohibition when the physical signs and symptoms of the patient are inputted. Three core modules are designed include sensing module, intuition-based fuzzy set theory/medical diagnosis module, and medical knowledge module. The result shows that the optimized prescription can reach the desired level, with great curative effect for patient disease, through a case study simulation. The presented AIMDS can integrate sensor technique and intelligent medical diagnosis methods to make an accurate diagnosis, resulting in three-type of optimized descriptions for patient selection.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-07-01

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

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

    International Nuclear Information System (INIS)

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

    1987-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Kevin C. Tseng

    2014-01-01

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

  10. Intelligence system based classification approach for medical disease diagnosis

    Science.gov (United States)

    Sagir, Abdu Masanawa; Sathasivam, Saratha

    2017-08-01

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

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

    International Nuclear Information System (INIS)

    Ting, Y.T.D.

    1990-01-01

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

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

    Science.gov (United States)

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

    2008-03-01

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

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

    Science.gov (United States)

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

    1988-01-01

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

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

    African Journals Online (AJOL)

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

  15. Design of fault diagnosis system for inertial navigation system based on virtual technology

    Science.gov (United States)

    Hu, Baiqing; Wang, Boxiong; Li, An; Zhang, Mingzhao; Qin, Fangjun; Pan, Hua

    2006-11-01

    With regard to the complex structure of the inertial navigation system and the low rate of fault detection with BITE (built-in test equipment), a fault diagnosis system for INS based on virtual technologies (virtual instrument and virtual equipment) is proposed in this paper. The hardware of the system is a PXI computer with highly stable performance and strong extensibility. In addition to the basic functions of digital multimeter, oscilloscope and cymometer, it can also measure the attitude of the ship in real-time, connect and control the measurement instruments with digital interface. The software is designed with the languages of Measurement Studio for VB, JAVA, and CULT3D. Using the extensively applied fault-tree reasoning and fault cases makes fault diagnosis. To suit the system to the diagnosis for various navigation electronic equipments, the modular design concept is adopted for the software programming. Knowledge of the expert system is digitally processed and the parameters of the system's interface and the expert diagnosis knowledge are stored in the database. The application shows that system is stable in operation, easy to use, quick and accurate in fault diagnosis.

  16. Qualitative Event-based Diagnosis with Possible Conflicts Applied to Spacecraft Power Distribution Systems

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault...

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

    OpenAIRE

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

    1994-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  20. Development of a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System for diabetes diagnosis.

    Science.gov (United States)

    Mansourypoor, Fatemeh; Asadi, Shahrokh

    2017-12-01

    The early diagnosis of disease is critical to preventing the occurrence of severe complications. Diabetes is a serious health problem. A variety of methods have been developed for diagnosing diabetes. The majority of these methods have been developed in a black-box manner, which cannot be used to explain the inference and diagnosis procedure. Therefore, it is essential to develop methods with high accuracy and interpretability. In this study, a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System (RLEFRBS) is developed for diabetes diagnosis. The proposed model involves the building of a Rule Base (RB) and rule optimization. The initial RB is constructed using numerical data without initial rules; after learning the rules, redundant rules are eliminated based on the confidence measure. Next, redundant conditions in the antecedent parts are pruned to yield simpler rules with higher interpretability. Finally, an appropriate subset of the rules is selected using a Genetic Algorithm (GA), and the RB is constructed. Evolutionary tuning of the membership functions and weight adjusting using Reinforcement Learning (RL) are used to improve the performance of RLEFRBS. Moreover, to deal with uncovered instances, it makes use of an efficient rule stretching method. The performance of RLEFRBS was examined using two common datasets: Pima Indian Diabetes (PID) and BioSat Diabetes Dataset (BDD). The experimental results show that the proposed model provides a more compact, interpretable and accurate RB that can be considered to be a promising alternative for diagnosis of diabetes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

    Science.gov (United States)

    Lee, S. C.

    1989-01-01

    The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

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

    African Journals Online (AJOL)

    acer

    2005) is an artificial intelligence methodology that solves problems by using ... jCOLIBRI is a free and open source object oriented framework .... The source codes of some classes in jColibri framework were modified and integrated within the new system to suit the purpose of the proposed system. The system components ...

  3. DIAGNOSIS WINDOWS PROBLEMS BASED ON HYBRID INTELLIGENCE SYSTEMS

    Directory of Open Access Journals (Sweden)

    SAFWAN O. HASOON

    2013-10-01

    Full Text Available This paper describes the artificial intelligence technologies by integrating Radial Basis Function networks with expert systems to construct a robust hybrid system. The purpose of building the hybrid system is to give recommendations to repair the operating system (Windows problems and troubleshoot the problems that can be repaired. The neural network has unique characteristics which it can complete the uncompleted data, the expert system can't deal with data that is incomplete, but using the neural network individually has some disadvantages which it can't give explanations and recommendations to the problems. The expert system has the ability to explain and give recommendations by using the rules and the human expert in some conditions. Therefore, we have combined the two technologies. The paper will explain the integration methods between the two technologies and which method is suitable to be used in the proposed hybrid system.

  4. Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis.

    Science.gov (United States)

    Kim, Sewoong; Cho, Dongrae; Kim, Jihun; Kim, Manjae; Youn, Sangyeon; Jang, Jae Eun; Je, Minkyu; Lee, Dong Hun; Lee, Boreom; Farkas, Daniel L; Hwang, Jae Youn

    2016-12-01

    We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.

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

    International Nuclear Information System (INIS)

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

    1986-06-01

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

  6. An intelligent system based on fuzzy probabilities for medical diagnosis- a study in aphasia diagnosis.

    Science.gov (United States)

    Moshtagh-Khorasani, Majid; Akbarzadeh-T, Mohammad-R; Jahangiri, Nader; Khoobdel, Mehdi

    2009-03-01

    Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features. Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN results as well as author's earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, respectively, strongly rejecting the null hypothesis. THE TECHNIQUE IS APPLIED AND COMPARED ON BOTH COMPREHENSIVE AND SPONTANEOUS SPEECH TEST DATA FOR DIAGNOSIS OF FOUR APHASIA TYPES: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features.

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

    Directory of Open Access Journals (Sweden)

    Sakthisudhan K

    2017-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Majid Moshtagh Khorasani

    2009-04-01

    Full Text Available

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

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

    Science.gov (United States)

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

    2010-01-01

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

  10. Distributed Knowledge Base Systems for Diagnosis and Information Retrieval.

    Science.gov (United States)

    1983-11-01

    social system metaphors State University. for distributed problem solving: Introduction to the issue. IEEE Newell. A. and Simon, H. A. (1972) Human...experts and Sriram Mahalingam wha-helped think out the probLema associated with building Auto-Mech. Research on diagnostic expert systemas for the

  11. Performance based fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2002-01-01

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

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

    Science.gov (United States)

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

    1990-01-01

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

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

    Science.gov (United States)

    Zhang, Han

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

  14. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

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

    International Nuclear Information System (INIS)

    Dewidar, M.M.

    1997-01-01

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

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

    Science.gov (United States)

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

    2017-06-01

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

  17. An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

    Science.gov (United States)

    Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim

    2017-03-01

    Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.

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

    Science.gov (United States)

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

    2009-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Sun Zengqiang

    2017-01-01

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

  20. Investigation of candidate data structures and search algorithms to support a knowledge based fault diagnosis system

    Science.gov (United States)

    Bosworth, Edward L., Jr.

    1987-01-01

    The focus of this research is the investigation of data structures and associated search algorithms for automated fault diagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based Fault Diagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.

  1. An Intelligent Learning Diagnosis System for Web-Based Thematic Learning Platform

    Science.gov (United States)

    Huang, Chenn-Jung; Liu, Ming-Chou; Chu, San-Shine; Cheng, Chih-Lun

    2007-01-01

    This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to…

  2. Active Fault Diagnosis for Hybrid Systems Based on Sensitivity Analysis and EKF

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas

    2011-01-01

    An active fault diagnosis approach for different kinds of faults is proposed. The input of the approach is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision...

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

    Science.gov (United States)

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

    2006-03-01

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

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

    Science.gov (United States)

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Xianfeng Yuan

    2015-01-01

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

  6. Remote Fault Information Acquisition and Diagnosis System of the Combine Harvester Based on LabVIEW

    Science.gov (United States)

    Chen, Jin; Wu, Pei; Xu, Kai

    Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

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

    Science.gov (United States)

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

    2017-06-01

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

  8. A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

    Directory of Open Access Journals (Sweden)

    Xiao Liu

    2017-01-01

    Full Text Available Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i data discretization, (ii feature extraction using the ReliefF algorithm, and (iii feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.

  9. Image-based computer-assisted diagnosis system for benign paroxysmal positional vertigo

    Science.gov (United States)

    Kohigashi, Satoru; Nakamae, Koji; Fujioka, Hiromu

    2005-04-01

    We develop the image based computer assisted diagnosis system for benign paroxysmal positional vertigo (BPPV) that consists of the balance control system simulator, the 3D eye movement simulator, and the extraction method of nystagmus response directly from an eye movement image sequence. In the system, the causes and conditions of BPPV are estimated by searching the database for record matching with the nystagmus response for the observed eye image sequence of the patient with BPPV. The database includes the nystagmus responses for simulated eye movement sequences. The eye movement velocity is obtained by using the balance control system simulator that allows us to simulate BPPV under various conditions such as canalithiasis, cupulolithiasis, number of otoconia, otoconium size, and so on. Then the eye movement image sequence is displayed on the CRT by the 3D eye movement simulator. The nystagmus responses are extracted from the image sequence by the proposed method and are stored in the database. In order to enhance the diagnosis accuracy, the nystagmus response for a newly simulated sequence is matched with that for the observed sequence. From the matched simulation conditions, the causes and conditions of BPPV are estimated. We apply our image based computer assisted diagnosis system to two real eye movement image sequences for patients with BPPV to show its validity.

  10. An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method.

    Science.gov (United States)

    Lu, Chunhong; Zhu, Zhaomin; Gu, Xiaofeng

    2014-09-01

    In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis.

  11. A clinical decision support system for diagnosis of Allergic Rhinitis based on intradermal skin tests.

    Science.gov (United States)

    Jabez Christopher, J; Khanna Nehemiah, H; Kannan, A

    2015-10-01

    Allergic Rhinitis is a universal common disease, especially in populated cities and urban areas. Diagnosis and treatment of Allergic Rhinitis will improve the quality of life of allergic patients. Though skin tests remain the gold standard test for diagnosis of allergic disorders, clinical experts are required for accurate interpretation of test outcomes. This work presents a clinical decision support system (CDSS) to assist junior clinicians in the diagnosis of Allergic Rhinitis. Intradermal Skin tests were performed on patients who had plausible allergic symptoms. Based on patient׳s history, 40 clinically relevant allergens were tested. 872 patients who had allergic symptoms were considered for this study. The rule based classification approach and the clinical test results were used to develop and validate the CDSS. Clinical relevance of the CDSS was compared with the Score for Allergic Rhinitis (SFAR). Tests were conducted for junior clinicians to assess their diagnostic capability in the absence of an expert. The class based Association rule generation approach provides a concise set of rules that is further validated by clinical experts. The interpretations of the experts are considered as the gold standard. The CDSS diagnoses the presence or absence of rhinitis with an accuracy of 88.31%. The allergy specialist and the junior clinicians prefer the rule based approach for its comprehendible knowledge model. The Clinical Decision Support Systems with rule based classification approach assists junior doctors and clinicians in the diagnosis of Allergic Rhinitis to make reliable decisions based on the reports of intradermal skin tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Model-based diagnosis and prognosis of a water recycling system

    Science.gov (United States)

    Roychoudhury, I.; Hafiychuk, V.; Goebel, K.

    A water recycling system (WRS) deployed at NASA Ames Research Center's Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

  13. Model-Based Diagnosis and Prognosis of a Water Recycling System

    Science.gov (United States)

    Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

    2013-01-01

    A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

  14. Development of a GIS-Based Decision Support System for Diagnosis of River System Health and Restoration

    Directory of Open Access Journals (Sweden)

    Jihong Xia

    2014-10-01

    Full Text Available The development of a decision support system (DSS to inform policy making has been progressing rapidly. This paper presents a generic framework and the development steps of a decision tool prototype of geographic information systems (GIS-based decision support system of river health diagnosis (RHD-DSS. This system integrates data, calculation models, and human knowledge of river health status assessment, causal factors diagnosis, and restoration decision making to assist decision makers during river restoration and management in Zhejiang Province, China. Our RHD-DSS is composed of four main elements: the graphical user interface (GUI, the database, the model base, and the knowledge base. It has five functional components: the input module, the database management, the diagnostic indicators management, the assessment and diagnosis, and the visual result module. The system design is illustrated with particular emphasis on the development of the database, model schemas, diagnosis and analytical processing techniques, and map management design. Finally, the application of the prototype RHD-DSS is presented and implemented for Xinjiangtang River of Haining County in Zhejiang Province, China. This case study is used to demonstrate the advantages gained by the application of this system. We conclude that there is great potential for using the RHD-DSS to systematically manage river basins in order to effectively mitigate environmental issues. The proposed approach will provide river managers and designers with improved insight into river degradation conditions, thereby strengthening the assessment process and the administration of human activities in river management.

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

    Directory of Open Access Journals (Sweden)

    Teruyuki Takahashi

    2012-01-01

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

  16. Information Based Fault Diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems....... These inputs are disturbance inputs, reference inputs and auxilary inputs. The diagnosis of the system is derived by an evaluation of the signature from the inputs in the residual outputs. The changes of the signatures form the external inputs are used for detection and isolation of the parametric faults....

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

    Directory of Open Access Journals (Sweden)

    Zhaowen Lin

    2017-01-01

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

  18. Parasite-based malaria diagnosis: are health systems in Uganda equipped enough to implement the policy?

    Science.gov (United States)

    Kyabayinze, Daniel J; Achan, Jane; Nakanjako, Damalie; Mpeka, Betty; Mawejje, Henry; Mugizi, Rukaaka; Kalyango, Joan N; D'Alessandro, Umberto; Talisuna, Ambrose; Jean-Pierre, Van geertruyden

    2012-08-24

    Malaria case management is a key strategy for malaria control. Effective coverage of parasite-based malaria diagnosis (PMD) remains limited in malaria endemic countries. This study assessed the health system's capacity to absorb PMD at primary health care facilities in Uganda. In a cross sectional survey, using multi-stage cluster sampling, lower level health facilities (LLHF) in 11 districts in Uganda were assessed for 1) tools, 2) skills, 3) staff and infrastructure, and 4) structures, systems and roles necessary for the implementing of PMD. Tools for PMD (microscopy and/or RDTs) were available at 30 (24%) of the 125 LLHF. All LLHF had patient registers and 15% had functional in-patient facilities. Three months' long stock-out periods were reported for oral and parenteral quinine at 39% and 47% of LLHF respectively. Out of 131 health workers interviewed, 86 (66%) were nursing assistants; 56 (43%) had received on-job training on malaria case management and 47 (36%) had adequate knowledge in malaria case management. Overall, only 18% (131/730) Ministry of Health approved staff positions were filled by qualified personnel and 12% were recruited or transferred within six months preceding the survey. Of 186 patients that received referrals from LLHF, 130(70%) had received pre-referral anti-malarial drugs, none received pre-referral rectal artesunate and 35% had been referred due to poor response to antimalarial drugs. Primary health care facilities had inadequate human and infrastructural capacity to effectively implement universal parasite-based malaria diagnosis. The priority capacity building needs identified were: 1) recruitment and retention of qualified staff, 2) comprehensive training of health workers in fever management, 3) malaria diagnosis quality control systems and 4) strengthening of supply chain, stock management and referral systems.

  19. Model-Based Sensor Placement for Component Condition Monitoring and Fault Diagnosis in Fossil Energy Systems

    Energy Technology Data Exchange (ETDEWEB)

    Mobed, Parham [Texas Tech Univ., Lubbock, TX (United States); Pednekar, Pratik [West Virginia Univ., Morgantown, WV (United States); Bhattacharyya, Debangsu [West Virginia Univ., Morgantown, WV (United States); Turton, Richard [West Virginia Univ., Morgantown, WV (United States); Rengaswamy, Raghunathan [Texas Tech Univ., Lubbock, TX (United States)

    2016-01-29

    Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desired for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.

  20. Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2018-01-01

    Full Text Available The battery is a key component and the major fault source in electric vehicles (EVs. Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.

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

    Directory of Open Access Journals (Sweden)

    Chin-Tsung Hsieh

    2014-01-01

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

  2. CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency.

    Science.gov (United States)

    Bhavani, Selvaraj Rani; Senthilkumar, Jagatheesan; Chilambuchelvan, Arul Gnanaprakasam; Manjula, Dhanabalachandran; Krishnamoorthy, Ramasamy; Kannan, Arputharaj

    2015-03-27

    The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called "CIMIDx", based on representative association rules that support the diagnosis of medical images (mammograms). The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype's classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user's perspective. We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information from 150 breast cancer survivors from hospitals

  3. Model-based energy monitoring and diagnosis of telecommunication cooling systems

    International Nuclear Information System (INIS)

    Sorrentino, Marco; Acconcia, Matteo; Panagrosso, Davide; Trifirò, Alena

    2016-01-01

    A methodology is proposed for on-line monitoring of cooling load supplied by Telecommunication (TLC) cooling systems. Sensible cooling load is estimated via a proportional integral controller-based input estimator, whereas a lumped parameters model was developed aiming at estimating air handling units (AHUs) latent heat load removal. The joint deployment of above estimators enables accurate prediction of total cooling load, as well as of related AHUs and free-coolers energy performance. The procedure was then proven effective when extended to cooling systems having a centralized chiller, through model-based estimation of a key performance metric, such as the energy efficiency ratio. The results and experimental validation presented throughout the paper confirm the suitability of the proposed procedure as a reliable and effective energy monitoring and diagnostic tool for TLC applications. Moreover, the proposed modeling approach, beyond its direct contribution towards smart use and conservation of energy, can be fruitfully deployed as a virtual sensor of removed heat load into a variety of residential and industrial applications. - Highlights: • Accurate cooling load prediction in telecommunication rooms. • Development of an input-estimator for sensible cooling load simulation. • Model-based estimation of latent cooling load. • Model-based prediction of centralized chiller energy performance in central offices. • Diagnosis-oriented application of proposed cooling load estimator.

  4. Knowledge-based fuzzy system for diagnosis and control of an integrated biological wastewater treatment process.

    Science.gov (United States)

    Pires, O C; Palma, C; Costa, J C; Moita, I; Alves, M M; Ferreira, E C

    2006-01-01

    A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a laboratory- scale plant comprising anaerobic digestion and anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 h. Recycle flow rate from the aerobic to the anoxic module and bypass flow rate from the influent directly to the anoxic reactor were the output variables of the fuzzy system. They were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognised by the expert system. Denitrification efficiency higher than 85% was achieved 30 h after the disturbance and 15 h after the system response at an HRT as low as 1.5 h. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 h. The system proved to react properly in order to set adequate operating conditions that led to timely and efficient recovery of N and C removal rates.

  5. Design of dynamic power quality monitoring and fault diagnosis system of ship-power system based on Ethernet

    Directory of Open Access Journals (Sweden)

    HU Hongqian

    2018-02-01

    Full Text Available [Objectives] According to situation that the ship power information exchange system based on the traditional field bus has been unable to meet the needs of modern ship power system for informatization, automation, intelligent and safe operation. [Methods] This paper proposes the use of industrial Ethernet Modbus/TCP to make up for lack of field-bus. Then, the data center is established by collecting the inherent data of the field bus of the combined ship power system and collecting the real-time data from the online measurement device based on the Modbus/TCP. Correlation theory and neural network intelligent algorithm are used to analyze big data to complete the dynamic power quality monitoring and fault diagnosis of ship power system. [Results] Finally, the man-machine interface is designed with LabVIEW. [Conclusions] The feasibility of the software and hardware implementation of the scheme is verified by the laboratory platform.

  6. A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.

    Science.gov (United States)

    Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R

    A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.

  7. Wavelet transform-based fault diagnosis and line selection method of small current grounding system

    Science.gov (United States)

    Yang, Ni; Zhang, Shuqing; Zhang, Liguo; Zhang, Kexin; Sun, Lingyun

    2008-12-01

    Small current grounding system is the system that the neutral point doesn't ground or grounds across the arc suppressing coils, which has been applied commonly in distribution system of many countries. As the grounding fault occurs, current is the one caused by capacity of circuit to ground only and it is rather small. The status of fault is complexity, e.g., the electromagnet interferes together with the amplified impact of zero-order loops to high-order singularity waves and various temporary variables. All these result in the lower ratio of the fault element signal to noise caused by zero-order current. In this paper, the position of signal singularity and the magnitude of the singularity degree are analyzed based on the variable focus character of wavelet, and the time fault occurs is then determined. The series db wavelet with close sustain is adopted, and the line selection is according to the zero-order voltage of the generatrix and the current of various outlet line. It is proved by the experiment that the fault circuit diagnosis method based on wavelet analysis to the character of temporary status of single-phase grounding fault plays an important role to a finer line selection.

  8. The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis.

    Science.gov (United States)

    Oliveira, Allisson Dantas; Prats, Clara; Espasa, Mateu; Zarzuela Serrat, Francesc; Montañola Sales, Cristina; Silgado, Aroa; Codina, Daniel Lopez; Arruda, Mercia Eliane; I Prat, Jordi Gomez; Albuquerque, Jones

    2017-04-25

    Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority. The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development. The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells. As a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly. Accessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment. ©Allisson Dantas Oliveira, Clara Prats, Mateu Espasa, Francesc Zarzuela Serrat, Cristina Montañola Sales, Aroa Silgado, Daniel Lopez Codina, Mercia Eliane Arruda, Jordi Gomez i Prat, Jones Albuquerque. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 25.04.2017.

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

    Science.gov (United States)

    Sun, Baiqing; Li, Yange; Zhang, Lin

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

  10. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

    KAUST Repository

    Harrou, Fouzi

    2017-09-18

    This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array\\'s residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.

  11. Power distribution system diagnosis with uncertainty information based on rough sets and clouds model

    Science.gov (United States)

    Sun, Qiuye; Zhang, Huaguang

    2006-11-01

    During the distribution system fault period, usually the explosive growth signals including fuzziness and randomness are too redundant to make right decision for the dispatcher. The volume of data with a few uncertainties overwhelms classic information systems in the distribution control center and exacerbates the existing knowledge acquisition process of expert systems. So intelligent methods must be developed to aid users in maintaining and using this abundance of information effectively. An important issue in distribution fault diagnosis system (DFDS) is to allow the discovered knowledge to be as close as possible to natural languages to satisfy user needs with tractability, and to offer DFDS robustness. At this junction, the paper describes a systematic approach for detecting superfluous data. The approach therefore could offer user both the opportunity to learn about the data and to validate the extracted knowledge. It is considered as a "white box" rather than a "black box" like in the case of neural network. The cloud theory is introduced and the mathematical description of cloud has effectively integrated the fuzziness and randomness of linguistic terms in a unified way. Based on it, a method of knowledge representation in DFDS is developed which bridges the gap between quantitative knowledge and qualitative knowledge. In relation to classical rough set, the cloud-rough method can deal with the uncertainty of the attribute and make a soft discretization for continuous ones (such as the current and the voltage). A novel approach, including discretization, attribute reduction, rule reliability computation and equipment reliability computation, is presented. The data redundancy is greatly reduced based on an integrated use of cloud theory and rough set theory. Illustrated with a power distribution DFDS shows the effectiveness and practicality of the proposed approach.

  12. An English Pronunciation Learning System for Japanese Students Based on Diagnosis of Critical Pronunciation Errors

    Science.gov (United States)

    Tsubota, Yasushi; Dantsuji, Masatake; Kawahara, Tatsuya

    2004-01-01

    We have developed an English pronunciation learning system which estimates the intelligibility of Japanese learners' speech and ranks their errors from the viewpoint of improving their intelligibility to native speakers. Error diagnosis is particularly important in self-study since students tend to spend time on aspects of pronunciation that do…

  13. Sub-typing of rheumatic diseases based on a systems diagnosis questionnaire

    NARCIS (Netherlands)

    Wietmarschen, H.A. van; Reijmers, T.H.; Kooij, A.J. van der; Schroën, J.; Wei, H.; Hankemeier, T.; Meulman, J.J.; Greef, J. van der

    2011-01-01

    Background: The future of personalized medicine depends on advanced diagnostic tools to characterize responders and non-responders to treatment. Systems diagnosis is a new approach which aims to capture a large amount of symptom information from patients to characterize relevant sub-groups.

  14. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization.

    Science.gov (United States)

    Miranda, Gisele Helena Barboni; Felipe, Joaquim Cezar

    2015-09-01

    Fuzzy logic can help reduce the difficulties faced by computational systems to represent and simulate the reasoning and the style adopted by radiologists in the process of medical image analysis. The study described in this paper consists of a new method that applies fuzzy logic concepts to improve the representation of features related to image description in order to make it semantically more consistent. Specifically, we have developed a computer-aided diagnosis tool for automatic BI-RADS categorization of breast lesions. The user provides parameters such as contour, shape and density and the system gives a suggestion about the BI-RADS classification. Initially, values of malignancy were defined for each image descriptor, according to the BI-RADS standard. When analyzing contour, for example, our method considers the matching of features and linguistic variables. Next, we created the fuzzy inference system. The generation of membership functions was carried out by the Fuzzy Omega algorithm, which is based on the statistical analysis of the dataset. This algorithm maps the distribution of different classes in a set. Images were analyzed by a group of physicians and the resulting evaluations were submitted to the Fuzzy Omega algorithm. The results were compared, achieving an accuracy of 76.67% for nodules and 83.34% for calcifications. The fit of definitions and linguistic rules to numerical models provided by our method can lead to a tighter connection between the specialist and the computer system, yielding more effective and reliable results. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. The fault diagnosis of large-scale wind turbine based on expert system

    Science.gov (United States)

    Chen, Changzheng; Li, Yun

    2011-10-01

    The wind turbine is the critical equipment for wind power, due to the poor working environment and the long running, the wind turbine components will have a variety of failures. Planned maintenance which has long been used is unable to understand the operational status of equipment comprehensively and timely in a way, especially for large wind machine, the repair work took too long time and cause serious damage. Therefore, fault diagnosis and predictive maintenance becomes more imminent. In this paper, the fault symptoms and corresponding reason of the large-scale wind turbine parts are analyzed and summarized ,such as gear box, generator, yaw system, and so on . And on this basis, the large-scale wind turbine fault diagnosis expert system was constructed by using expert system tool CLIPS and Visual C + +.

  16. Hierarchical Fault Diagnosis for a Hybrid System Based on a Multidomain Model

    Directory of Open Access Journals (Sweden)

    Jiming Ma

    2015-01-01

    Full Text Available The diagnosis procedure is performed by integrating three steps: multidomain modeling, event identification, and failure event classification. Multidomain model can describe the normal and fault behaviors of hybrid systems efficiently and can meet the diagnosis requirements of hybrid systems. Then the multidomain model is used to simulate and obtain responses under different failure events; the responses are further utilized as a priori information when training the event identification library. Finally, a brushless DC motor is selected as the study case. The experimental result indicates that the proposed method could identify the known and unknown failure events of the studied system. In particular, for a system with less response information under a failure event, the accuracy of diagnosis seems to be higher. The presented method integrates the advantages of current quantitative and qualitative diagnostic procedures and can distinguish between failures caused by parametric and abrupt structure faults. Another advantage of our method is that it can remember unknown failure types and automatically extend the adaptive resonance theory neural network library, which is extremely useful for complex hybrid systems.

  17. A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes.

    Science.gov (United States)

    Rodríguez-Cristerna, Arturo; Gómez-Flores, Wilfrido; de Albuquerque Pereira, Wagner Coelho

    2018-01-01

    Conventional computer-aided diagnosis (CAD) systems for breast ultrasound (BUS) are trained to classify pathological classes, that is, benign and malignant. However, from a clinical perspective, this kind of classification does not agree totally with radiologists' diagnoses. Usually, the tumors are assessed by using a BI-RADS (Breast Imaging-Reporting and Data System) category and, accordingly, a recommendation is emitted: annual study for category 2 (benign), six-month follow-up study for category 3 (probably benign), and biopsy for categories 4 and 5 (suspicious of malignancy). Hence, in this paper, a CAD system based on BI-RADS categories weighted by pathological information is presented. The goal is to increase the classification performance by reducing the common class imbalance found in pathological classes as well as to provide outcomes quite similar to radiologists' recommendations. The BUS dataset considers 781 benign lesions and 347 malignant tumors proven by biopsy. Moreover, every lesion is associated to one BI-RADS category in the set {2, 3, 4, 5}. Thus, the dataset is split into three weighted classes: benign, BI-RADS 2 in benign lesions; probably benign, BI-RADS 3 and 4 in benign lesions; and malignant, BI-RADS 4 and 5 in malignant lesions. Thereafter, a random forest (RF) classifier, denoted by RF w , is trained to predict the weighted BI-RADS classes. In addition, for comparison purposes, a RF classifier is trained to predict pathological classes, denoted as RF p . The ability of the classifiers to predict the pathological classes is measured by the area under the ROC curve (AUC), sensitivity (SEN), and specificity (SPE). The RF w classifier obtained AUC=0.872,SEN=0.826, and SPE=0.919, whereas the RF p classifier reached AUC=0.868,SEN=0.808, and SPE=0.929. According to a one-way analysis of variance test, the RF w classifier statistically outperforms (p BI-RADS classes is given by the Matthews correlation coefficient that obtained 0

  18. Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

    Directory of Open Access Journals (Sweden)

    Yuan Pu

    2015-01-01

    Full Text Available BP neural network (Back-Propagation Neural Network, BP-NN is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.

  19. Discrete event systems diagnosis and diagnosability

    CERN Document Server

    Sayed-Mouchaweh, Moamar

    2014-01-01

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

  20. Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network.

    Science.gov (United States)

    Alizadeh, Behrouz; Safdari, Reza; Zolnoori, Maryam; Bashiri, Azadeh

    2015-08-01

    Lack of proper diagnosis and inadequate treatment of asthma, leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different modes was made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. So considering the data mining approaches due to the nature of medical data is necessary.

  1. A statistical-based approach for fault detection and diagnosis in a photovoltaic system

    KAUST Repository

    Garoudja, Elyes

    2017-07-10

    This paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short-circuit faults; open-circuit faults; and partial shading faults). Towards this end, we apply exponentially-weighted moving average (EWMA) control chart on the residuals obtained from the one-diode model. Such a choice is motivated by the greater sensitivity of EWMA chart to incipient faults and its low-computational cost making it easy to implement in real time. Practical data from a 3.2 KWp photovoltaic plant located within an Algerian research center is used to validate the proposed approach. Results show clearly the efficiency of the developed method in monitoring PV system status.

  2. Decision Support System In Heart Disease Diagnosis By Case Based Recommendation

    Directory of Open Access Journals (Sweden)

    Prinsha Prakash

    2015-02-01

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

  3. Condition-based diagnosis of mechatronic systems using a fractional calculus approach

    Science.gov (United States)

    Gutiérrez-Carvajal, Ricardo Enrique; Flávio de Melo, Leonimer; Maurício Rosário, João; Tenreiro Machado, J. A.

    2016-07-01

    While fractional calculus (FC) is as old as integer calculus, its application has been mainly restricted to mathematics. However, many real systems are better described using FC equations than with integer models. FC is a suitable tool for describing systems characterised by their fractal nature, long-term memory and chaotic behaviour. It is a promising methodology for failure analysis and modelling, since the behaviour of a failing system depends on factors that increase the model's complexity. This paper explores the proficiency of FC in modelling complex behaviour by tuning only a few parameters. This work proposes a novel two-step strategy for diagnosis, first modelling common failure conditions and, second, by comparing these models with real machine signals and using the difference to feed a computational classifier. Our proposal is validated using an electrical motor coupled with a mechanical gear reducer.

  4. Data acquisition for a real time fault monitoring and diagnosis knowledge-based system for space power system

    Science.gov (United States)

    Wilhite, Larry D.; Lee, S. C.; Lollar, Louis F.

    1989-01-01

    The design and implementation of the real-time data acquisition and processing system employed in the AMPERES project is described, including effective data structures for efficient storage and flexible manipulation of the data by the knowledge-based system (KBS), the interprocess communication mechanism required between the data acquisition system and the KBS, and the appropriate data acquisition protocols for collecting data from the sensors. Sensor data are categorized as critical or noncritical data on the basis of the inherent frequencies of the signals and the diagnostic requirements reflected in their values. The critical data set contains 30 analog values and 42 digital values and is collected every 10 ms. The noncritical data set contains 240 analog values and is collected every second. The collected critical and noncritical data are stored in separate circular buffers. Buffers are created in shared memory to enable other processes, i.e., the fault monitoring and diagnosis process and the user interface process, to freely access the data sets.

  5. Monodetector system for diagnosis (DETEC)

    International Nuclear Information System (INIS)

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

    1997-01-01

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

  6. Fault diagnosis of air conditioning systems based on qualitative bond graph

    International Nuclear Information System (INIS)

    Ghiaus, C.

    1999-01-01

    The bond graph method represents a unified approach for modeling engineering systems. The main idea is that power transfer bonds the components of a system. The bond graph model is the same for both quantitative representation, in which parameters have numerical values, and qualitative approach, in which they are classified qualitatively. To infer the cause of faults using a qualitative method, a system of qualitative equations must be solved. However, the characteristics of qualitative operators require specific methods for solving systems of equations having qualitative variables. This paper proposes both a method for recursively solving the qualitative system of equations derived from bond graph, and a bond graph model of a direct-expansion, mechanical vapor-compression air conditioning system. Results from diagnosing two faults in a real air conditioning system are presented and discussed. Occasionally, more than one fault candidate is inferred for the same set of qualitative values derived from measurements. In these cases, additional information is required to localize the fault. Fault diagnosis is initiated by a fault detection mechanism which also classifies the quantitative measurements into qualitative values; the fault detection is not presented here. (author)

  7. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships.

    Science.gov (United States)

    Silva, Andre A; Gupta, Shalabh; Bazzi, Ali M; Ulatowski, Arthur

    2017-09-21

    Electric machines and drives have enjoyed extensive applications in the field of electric vehicles (e.g., electric ships, boats, cars, and underwater vessels) due to their ease of scalability and wide range of operating conditions. This stems from their ability to generate the desired torque and power levels for propulsion under various external load conditions. However, as with the most electrical systems, the electric drives are prone to component failures that can degrade their performance, reduce the efficiency, and require expensive maintenance. Therefore, for safe and reliable operation of electric vehicles, there is a need for automated early diagnostics of critical failures such as broken rotor bars and electrical phase failures. In this regard, this paper presents a fault diagnosis methodology for electric drives in electric ships. This methodology utilizes the two-dimensional, i.e. scale-shift, wavelet transform of the sensor data to filter optimal information-rich regions which can enhance the diagnosis accuracy as well as reduce the computational complexity of the classifier. The methodology was tested on sensor data generated from an experimentally validated simulation model of electric drives under various cruising speed conditions. The results in comparison with other existing techniques show a high correct classification rate with low false alarm and miss detection rates. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Model-Based Diagnosis and Prognosis of a Water Recycling System

    Data.gov (United States)

    National Aeronautics and Space Administration — A water recycling system (WRS) deployed at NASA Ames Research Center’s Sustainability Base (an energy efficient office building that integrates some novel...

  9. Fault-diagnosis applications. Model-based condition monitoring. Acutators, drives, machinery, plants, sensors, and fault-tolerant systems

    Energy Technology Data Exchange (ETDEWEB)

    Isermann, Rolf [Technische Univ. Darmstadt (DE). Inst. fuer Automatisierungstechnik (IAT)

    2011-07-01

    Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity. This book is a sequel of the book ''Fault-Diagnosis Systems'' published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as: Electrical drives (DC, AC) Electrical actuators Fluidic actuators (hydraulic, pneumatic) Centrifugal and reciprocating pumps Pipelines (leak detection) Industrial robots Machine tools (main and feed drive, drilling, milling, grinding) Heat exchangers Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented. The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful. The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers. (orig.)

  10. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    Science.gov (United States)

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

  11. Robust Model-Based Fault Diagnosis for DC Zonal Electrical Distribution System

    Science.gov (United States)

    2007-06-01

    want to thank CAPT Norbert Doerry, whose influence and sound advice extend beyond xiv academics. Specifically, I thank him for persuading me to...1984. [182] A. Janczak, "Parameter Estimation Based Fault Detection and Isolation in Wiener and Hammerstein Systems," Int. J. Appl. Math. and Comp

  12. Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter

    Science.gov (United States)

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system’s efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  13. PACS-based teleconsultation system in support of the diagnosis of nasopharyngeal carcinoma in Zhejiang, China

    Science.gov (United States)

    Zhang, Hong; Hu, Dake; Zhou, Shengmin; Hu, Jun; Yang, Yang

    1997-05-01

    It is shown in the paper that nasopharyngeal carcinoma is commonly encountered in southeastern China and we, considering the urgent need to diagnose the tumor as early as possible and the lack of enough expert radiologists throughout the province, have launched a project to develop a teleconsultation systems with the expert radiologist center at the Zhejiang Cancer Hospital serving rural hospitals in Zhejiang Province. Our client/server teleconsultation system consists of three subsystems: a mini PACS-based Teleconsultation Expert Center, the remote referring physician's workstation and networking subsystem connecting not only LAN but also remote workstation. The software facilities include Preparation Manager, Archiving Manager, Consultation Manager, Report Wizard and Query Manager, all of which simulates a step of the traditional travel-based consultation. In this paper, we also discuss briefly the systems performance and future improvement consideration.

  14. A web-based knowledge management system integrating Western and Traditional Chinese Medicine for relational medical diagnosis.

    Science.gov (United States)

    Herrera-Hernandez, Maria C; Lai-Yuen, Susana K; Piegl, Les A; Zhang, Xiao

    2016-10-26

    This article presents the design of a web-based knowledge management system as a training and research tool for the exploration of key relationships between Western and Traditional Chinese Medicine, in order to facilitate relational medical diagnosis integrating these mainstream healing modalities. The main goal of this system is to facilitate decision-making processes, while developing skills and creating new medical knowledge. Traditional Chinese Medicine can be considered as an ancient relational knowledge-based approach, focusing on balancing interrelated human functions to reach a healthy state. Western Medicine focuses on specialties and body systems and has achieved advanced methods to evaluate the impact of a health disorder on the body functions. Identifying key relationships between Traditional Chinese and Western Medicine opens new approaches for health care practices and can increase the understanding of human medical conditions. Our knowledge management system was designed from initial datasets of symptoms, known diagnosis and treatments, collected from both medicines. The datasets were subjected to process-oriented analysis, hierarchical knowledge representation and relational database interconnection. Web technology was implemented to develop a user-friendly interface, for easy navigation, training and research. Our system was prototyped with a case study on chronic prostatitis. This trial presented the system's capability for users to learn the correlation approach, connecting knowledge in Western and Traditional Chinese Medicine by querying the database, mapping validated medical information, accessing complementary information from official sites, and creating new knowledge as part of the learning process. By addressing the challenging tasks of data acquisition and modeling, organization, storage and transfer, the proposed web-based knowledge management system is presented as a tool for users in medical training and research to explore, learn and

  15. RadPath: A Web-based System for Integrating and Correlating Radiology and Pathology Findings During Cancer Diagnosis.

    Science.gov (United States)

    Arnold, Corey W; Wallace, W Dean; Chen, Shawn; Oh, Andrea; Abtin, Fereidoun; Genshaft, Scott; Binder, Scott; Aberle, Denise; Enzmann, Dieter

    2016-01-01

    The current paradigm of cancer diagnosis involves uncoordinated communication of findings from radiology and pathology to downstream physicians. Discordance between these findings can require additional time from downstream users to resolve, or given incorrect resolution, may adversely impact treatment decisions. To mitigate this problem, we developed a web-based system, called RadPath, for correlating and integrating radiology and pathology reporting. RadPath includes interfaces to our institution's clinical information systems, which are used to retrieve reports, images, and test results that are structured into an interactive compendium for a diagnostic patient case. The system includes an editing interface for physicians, allowing for the inclusion of additional clinical data, as well as the ability to retrospectively correlate and contextualize imaging findings following pathology diagnosis. During pilot deployment and testing over the course of 1 year, physicians at our institution have completed 60 RadPath cases, requiring an average of 128 seconds from a radiologist and an average of 93 seconds from a pathologist per case. Several technical and workflow challenges were encountered during development, including interfacing with diverse clinical information systems, automatically structuring report contents, and determining the appropriate physicians to create RadPath summaries. Reaction to RadPath has been positive, with users valuing the system's ability to consolidate diagnostic information. With the increasing complexity of medicine and the movement toward team-based disease management, there is a need for improved clinical communication and information exchange. RadPath provides a platform for generating coherent and correlated diagnostic summaries in cancer diagnosis with minimal additional effort from physicians. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  16. Automatic Vertebral Fracture Assessment System (AVFAS) for Spinal Pathologies Diagnosis Based on Radiograph X-Ray Images

    Science.gov (United States)

    Mustapha, Aouache; Hussain, Aini; Samad, Salina Abd; Bin Abdul Hamid, Hamzaini; Ariffin, Ahmad Kamal

    Nowadays, medical imaging has become a major tool in many clinical trials. This is because the technology enables rapid diagnosis with visualization and quantitative assessment that facilitate health practitioners or professionals. Since the medical and healthcare sector is a vast industry that is very much related to every citizen's quality of life, the image based medical diagnosis has become one of the important service areas in this sector. As such, a medical diagnostic imaging (MDI) software tool for assessing vertebral fracture is being developed which we have named as AVFAS short for Automatic Vertebral Fracture Assessment System. The developed software system is capable of indexing, detecting and classifying vertebral fractures by measuring the shape and appearance of vertebrae of radiograph x-ray images of the spine. This paper describes the MDI software tool which consists of three main sub-systems known as Medical Image Training & Verification System (MITVS), Medical Image and Measurement & Decision System (MIMDS) and Medical Image Registration System (MIRS) in term of its functionality, performance, ongoing research and outstanding technical issues.

  17. Case-based reasoning as a decision support system for cancer diagnosis: A case study

    OpenAIRE

    Bajo Pérez, Javier; Paz Santana, Juan Francisco de; Rodríguez, Sara; Corchado Rodríguez, Juan Manuel

    2009-01-01

    [EN]Microarray technology can measure the expression levels of thousands of genes in an experiment. This fact makes the use of computational methods in cancer research absolutely essential. One of the possible applications is in the use of Artificial Intelligence techniques. Several of these techniques have been used to analyze expression arrays, but there is a growing need for new and effective solutions. This paper presents a Case-based reasoning (CBR) system for automatic classification of...

  18. Web Based Cattle Disease Expert System Diagnosis with forward Chaining Method

    Science.gov (United States)

    Zamsuri, Ahmad; Syafitri, Wenni; Sadar, Muhamad

    2017-12-01

    Cattle is one of the livestock who have high economic potential, whether for livestock, cattle seed, or even for food stock. Everything that comes from Cattle is a treasure for example the Milk, the Meat, and Cattle-hide. The factor that cause Cattles to die is the spread of disease that could crock up the Cattle’s health. So that the Expert system is needed to utilize and analye the Cattle’s disease so it could detect the disease without going to the veterinarian. Forward chaining method is one of the correct method in this expert system wherein began with Symptoms to determine the illness. From this matter, we built a web based expert system application on Cattles disease to ease the disease detection and showing the brief information about the Cattles itself.

  19. Study on a Real-Time BEAM System for Diagnosis Assistance Based on a System on Chips Design

    Directory of Open Access Journals (Sweden)

    Kung-Wei Chang

    2013-05-01

    Full Text Available As an innovative as well as an interdisciplinary research project, this study performed an analysis of brain signals so as to establish BrainIC as an auxiliary tool for physician diagnosis. Cognition behavior sciences, embedded technology, system on chips (SOC design and physiological signal processing are integrated in this work. Moreover, a chip is built for real-time electroencephalography (EEG processing purposes and a Brain Electrical Activity Mapping (BEAM system, and a knowledge database is constructed to diagnose psychosis and body challenges in learning various behaviors and signals antithesis by a fuzzy inference engine. This work is completed with a medical support system developed for the mentally disabled or the elderly abled.

  20. Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support.

    Science.gov (United States)

    Khedher, Laila; Illán, Ignacio A; Górriz, Juan M; Ramírez, Javier; Brahim, Abdelbasset; Meyer-Baese, Anke

    2017-05-01

    Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.

  1. Clinical diagnosis and typing of systemic amyloidosis in subcutaneous fat aspirates by mass spectrometry-based proteomics.

    Science.gov (United States)

    Vrana, Julie A; Theis, Jason D; Dasari, Surendra; Mereuta, Oana M; Dispenzieri, Angela; Zeldenrust, Steven R; Gertz, Morie A; Kurtin, Paul J; Grogg, Karen L; Dogan, Ahmet

    2014-07-01

    Examination of abdominal subcutaneous fat aspirates is a practical, sensitive and specific method for the diagnosis of systemic amyloidosis. Here we describe the development and implementation of a clinical assay using mass spectrometry-based proteomics to type amyloidosis in subcutaneous fat aspirates. First, we validated the assay comparing amyloid-positive (n=43) and -negative (n=26) subcutaneous fat aspirates. The assay classified amyloidosis with 88% sensitivity and 96% specificity. We then implemented the assay as a clinical test, and analyzed 366 amyloid-positive subcutaneous fat aspirates in a 4-year period as part of routine clinical care. The assay had a sensitivity of 90%, and diverse amyloid types, including immunoglobulin light chain (74%), transthyretin (13%), serum amyloid A (%1), gelsolin (1%), and lysozyme (1%), were identified. Using bioinformatics, we identified a universal amyloid proteome signature, which has high sensitivity and specificity for amyloidosis similar to that of Congo red staining. We curated proteome databases which included variant proteins associated with systemic amyloidosis, and identified clonotypic immunoglobulin variable gene usage in immunoglobulin light chain amyloidosis, and the variant peptides in hereditary transthyretin amyloidosis. In conclusion, mass spectrometry-based proteomic analysis of subcutaneous fat aspirates offers a powerful tool for the diagnosis and typing of systemic amyloidosis. The assay reveals the underlying pathogenesis by identifying variable gene usage in immunoglobulin light chains and the variant peptides in hereditary amyloidosis. Copyright© Ferrata Storti Foundation.

  2. European evidence-based recommendations for diagnosis and treatment of childhood-onset systemic lupus erythematosus: the SHARE initiative.

    Science.gov (United States)

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

    2017-11-01

    Childhood-onset systemic lupus erythematosus (cSLE) is a rare, multisystem and potentially life-threatening autoimmune disorder with significant associated morbidity. Evidence-based guidelines are sparse and management is often based on clinical expertise. SHARE (Single Hub and Access point for paediatric Rheumatology in Europe) was launched to optimise and disseminate management regimens for children and young adults with rheumatic diseases like cSLE. Here, we provide evidence-based recommendations for diagnosis and treatment of cSLE. In view of extent and complexity of cSLE and its various manifestations, recommendations for lupus nephritis and antiphospholipid syndrome will be published separately. Recommendations were generated using the EULAR (European League Against Rheumatism) standard operating procedure. An expert committee consisting of paediatric rheumatologists and representation of paediatric nephrology from across Europe discussed evidence-based recommendations during two consensus meetings. Recommendations were accepted if >80% agreement was reached. A total of 25 recommendations regarding key approaches to diagnosis and treatment of cSLE were made. The recommendations include 11 on diagnosis, 9 on disease monitoring and 5 on general treatment. Topics included: appropriate use of SLE classification criteria, disease activity and damage indices; adequate assessment of autoantibody profiles; secondary macrophage activation syndrome; use of hydroxychloroquine and corticosteroid-sparing regimens; and the importance of addressing poor adherence. Ten recommendations were accepted regarding general diagnostic strategies and treatment indications of neuropsychiatric cSLE. The SHARE recommendations for cSLE and neuropsychiatric manifestations of cSLE have been formulated by an evidence-based consensus process to support uniform, high-quality standards of care for children with cSLE. © Article author(s) (or their employer(s) unless otherwise stated in the

  3. A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system

    Directory of Open Access Journals (Sweden)

    Hamid Reza Marateb

    2015-01-01

    Full Text Available Background: Coronary heart diseases/coronary artery diseases (CHDs/CAD, the most common form of cardiovascular disease (CVD, are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. Materials and Methods: In this study, the Cleveland CAD dataset from the University of California UCI (Irvine was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS methods, multiple logistic regression (MLR and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. Results: In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard for 272 subjects (68% male were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results. Conclusion: The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.

  4. A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system.

    Science.gov (United States)

    Marateb, Hamid Reza; Goudarzi, Sobhan

    2015-03-01

    Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results. The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.

  5. Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems.

    Science.gov (United States)

    Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; Huang, Yi-Shan; Zhao, Kai-Hui

    2015-05-11

    This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in αβ coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method.

  6. Microarray based on autodisplayed Ro proteins for medical diagnosis of systemic lupus erythematosus (SLE).

    Science.gov (United States)

    Yoo, Gu; Bong, Ji-Hong; Kim, Sinyoung; Jose, Joachim; Pyun, Jae-Chul

    2014-07-15

    A microarray-based immunoassay for the detection of autoantibodies against Ro protein was developed using Escherichia coli with autodisplayed Ro proteins (Ro(+)-E. coli). Patient serum usually contains various antibodies against the outer membrane components of E. coli as well as autoantibodies against the Ro protein. Therefore, the conventional immunoassay based on Ro(+)-E. coli requires both wild type E. coli (blank test) and Ro(+)-E. coli, and both strains of E. coli must be prepared in situ for each individual test serum. In this study, we tested the feasibility of using several types of animal sera as a replacement for individual human sera. An immunoassay without the blank test was developed using Ro(+)-E. coli by (1) blocking with rabbit serum, and (2) cleaving the Fc region from antibodies using papain. Modified E. coli with autodisplayed Ro protein was immobilized to a surface-modified microplate and the applicability of the immunoassay without the blank test was demonstrated using sera from patients with systemic lupus erythematosus (SLE). Using this approach, a microarray-based fluorescence immunoassay with immobilized Ro(+)-E. coli was able to detect anti-Ro autoantibodies in SLE patient sera with high specificity and selectivity and improved efficiency. Copyright © 2014 Elsevier B.V. All rights reserved.

  7. Nanobody-Based Delivery Systems for Diagnosis and Targeted Tumor Therapy

    Directory of Open Access Journals (Sweden)

    Yaozhong Hu

    2017-11-01

    Full Text Available The development of innovative targeted therapeutic approaches are expected to surpass the efficacy of current forms of treatments and cause less damage to healthy cells surrounding the tumor site. Since the first development of targeting agents from hybridoma’s, monoclonal antibodies (mAbs have been employed to inhibit tumor growth and proliferation directly or to deliver effector molecules to tumor cells. However, the full potential of such a delivery strategy is hampered by the size of mAbs, which will obstruct the targeted delivery system to access the tumor tissue. By serendipity, a new kind of functional homodimeric antibody format was discovered in camelidae, known as heavy-chain antibodies (HCAbs. The cloning of the variable domain of HCAbs produces an attractive minimal-sized alternative for mAbs, referred to as VHH or nanobodies (Nbs. Apart from their dimensions in the single digit nanometer range, the unique characteristics of Nbs combine a high stability and solubility, low immunogenicity and excellent affinity and specificity against all possible targets including tumor markers. This stimulated the development of tumor-targeted therapeutic strategies. Some autonomous Nbs have been shown to act as antagonistic drugs, but more importantly, the targeting capacity of Nbs has been exploited to create drug delivery systems. Obviously, Nb-based targeted cancer therapy is mainly focused toward extracellular tumor markers, since the membrane barrier prevents antibodies to reach the most promising intracellular tumor markers. Potential strategies, such as lentiviral vectors and bacterial type 3 secretion system, are proposed to deliver target-specific Nbs into tumor cells and to block tumor markers intracellularly. Simultaneously, Nbs have also been employed for in vivo molecular imaging to diagnose diseased tissues and to monitor the treatment effects. Here, we review the state of the art and focus on recent developments with Nbs as

  8. Porous TiO2-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis

    Directory of Open Access Journals (Sweden)

    Vardan Galstyan

    2017-12-01

    Full Text Available Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO2, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO2-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO2 may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material.

  9. Porous TiO₂-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis.

    Science.gov (United States)

    Galstyan, Vardan

    2017-12-19

    Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO₂, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO₂-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO₂ may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material.

  10. Porous TiO2-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis

    Science.gov (United States)

    2017-01-01

    Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO2, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO2-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO2 may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material. PMID:29257076

  11. Machinery Fault Diagnosis Using Two-Channel Analysis Method Based on Fictitious System Frequency Response Function

    Directory of Open Access Journals (Sweden)

    Kihong Shin

    2015-01-01

    Full Text Available Most existing techniques for machinery health monitoring that utilize measured vibration signals usually require measurement points to be as close as possible to the expected fault components of interest. This is particularly important for implementing condition-based maintenance since the incipient fault signal power may be too small to be detected if a sensor is located further away from the fault source. However, a measurement sensor is often not attached to the ideal point due to geometric or environmental restrictions. In such a case, many of the conventional diagnostic techniques may not be successfully applicable. In this paper, a two-channel analysis method is proposed to overcome such difficulty. It uses two vibration signals simultaneously measured at arbitrary points in a machine. The proposed method is described theoretically by introducing a fictitious system frequency response function. It is then verified experimentally for bearing fault detection. The results show that the suggested method may be a good alternative when ideal points for measurement sensors are not readily available.

  12. Robust observer-based fault diagnosis for nonlinear systems using Matlab

    CERN Document Server

    Zhang, Jian; Nguang, Sing Kiong

    2016-01-01

    This book introduces several observer-based methods, including: • the sliding-mode observer • the adaptive observer • the unknown-input observer and • the descriptor observer method for the problem of fault detection, isolation and estimation, allowing readers to compare and contrast the different approaches. The authors present basic material on Lyapunov stability theory, H¥ control theory, sliding-mode control theory and linear matrix inequality problems in a self-contained and step-by-step manner. Detailed and rigorous mathematical proofs are provided for all the results developed in the text so that readers can quickly gain a good understanding of the material. MATLAB® and Simulink® codes for all the examples, which can be downloaded from http://extras.springer.com, enable students to follow the methods and illustrative examples easily. The systems used in the examples make the book highly relevant to real-world problems in industrial control engineering and include a seventh-order aircraft mod...

  13. A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.

    Science.gov (United States)

    Lu, Wei; Li, Zhe; Chu, Jinghui

    2017-04-01

    Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improving the performance of base classifiers. We extracted morphological, various texture, and Gabor features. To clarify the feature subsets' physical meaning, subspaces are built by combining morphological features with each kind of texture or Gabor feature. We tested our proposal using a manually segmented Region of Interest (ROI) data set, which contains 438 images of malignant tumors and 1898 images of normal tissues or benign tumors. Our proposal achieves an area under the ROC curve (AUC) value of 0.9617, which outperforms most other state-of-the-art breast MRI CADx systems. Compared with other methods, our proposal significantly reduces the false-positive classification rate. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Approximation Algorithms for Model-Based Diagnosis

    NARCIS (Netherlands)

    Feldman, A.B.

    2010-01-01

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

  15. Autonomous power system intelligent diagnosis and control

    Science.gov (United States)

    Ringer, Mark J.; Quinn, Todd M.; Merolla, Anthony

    1991-01-01

    The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution hardware. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System.

  16. Differential diagnosis of follicular tumor by expert systems based on a set of quantitative features of thyrocyte nuclei and aggregates.

    Science.gov (United States)

    Kirillov, Vladimir; Emeliyanova, Olga

    2012-04-01

    To develop expert systems for classification of follicular thyroid tumor at a preoperative stage. Fine needle aspiration biopsy of the thyroid gland with a histologic conclusion of follicular cancer and follicular adenoma were the object of the morphometric study. General sample size was 4500 nuclei and 3000 aggregates. Quantitative regularities of pathologic changes in thyrocyte nuclei and aggregates in follicular cancer and follicular adenoma were revealed. Threshold values and weighting coefficients of quantitative features of thyrocyte nuclei and aggregates characterizing cancer made the basis of the two expert systems. Expert systems included standard 2-D S-matrix containing threshold values of nuclei and aggregates in cancer and their weighting coefficients as well as 1-D scientific X-matrix designed for filling with quantitative features of the studied object. The diagnosis was verified by the value of a diagnostic index by means of comparing feature values in the corresponding elements of S- and X-matrices. After that, a diagnostic index was calculated taking into account the features' weighting coefficient. The developed expert systems based on a set of quantitative features of thyrocyte nuclei and aggregates will allow assessing the malignant potential of a follicular thyroid tumor at a preoperative stage.

  17. Case-Exercises, Diagnosis, and Explanations in a Knowledge Based Tutoring System for Project Planning.

    Science.gov (United States)

    Pulz, Michael; Lusti, Markus

    PROJECTTUTOR is an intelligent tutoring system that enhances conventional classroom instruction by teaching problem solving in project planning. The domain knowledge covered by the expert module is divided into three functions. Structural analysis, identifies the activities that make up the project, time analysis, computes the earliest and latest…

  18. Diagnosis for ecological intensification of maize-based smallholder farming systems in the Costa Chica, Mexico

    NARCIS (Netherlands)

    Flores-Sanchez, D.; Kleine Koerkamp-Rabelista, J.; Navarro-Garza, H.; Lantinga, E.A.; Groot, J.C.J.; Kropff, M.J.; Rossing, W.A.H.

    2011-01-01

    Enhanced utilization of ecological processes for food and feed production as part of the notion of ecological intensification starts from location-specific knowledge of production constraints. A diagnostic systems approach which combined social-economic and production ecological methods at farm and

  19. mCOPD: Mobile Phone Based Lung Function Diagnosis and Exercise System for COPD

    OpenAIRE

    Liu, Xiao

    2013-01-01

    COPD (Chronic Obstructive Pulmonary Disease) is a serious lung disease which makes people hard to breathe. The number of people who have COPD is on the rise. COPD patients require lung function examinations and perform breathing exercises on a regular basis in order to be more aware of their lung functions, get diagnosed early, and control the shortness of their breaths. In order to help people with COPD, we developed mCOPD which is a smartphone based Android application made especially for C...

  20. A system for tumor heterogeneity evaluation and diagnosis based on tumor markers measured routinely in the laboratory.

    Science.gov (United States)

    Hui, Liu; Rixv, Liu; Xiuying, Zhou

    2015-12-01

    To develop an efficient and reliable approach to estimate tumor heterogeneity and improve tumor diagnosis using multiple tumor markers measured routinely in the clinical laboratory. A total of 161 patients with different cancers were recruited as the cancer group, and 91 patients with non-oncological conditions were required as the non-oncological disease group. The control group comprised 90 randomly selected healthy subjects. AFP, CEA, CYFRA, CA125, CA153, CA199, CA724, and NSE levels were measured in all these subjects with a chemiluminescent microparticle immunoassay. The tumor marker with the maximum S/CO value (sample test value:cutoff value for discriminating individuals with and without tumors) was considered as a specific tumor marker (STM) for an individual. Tumor heterogeneity index (THI)=N/P (N: number of STMs; P: percentage of individuals with STMs in a certain tumor population) was used to quantify tumor heterogeneity: high THI indicated high tumor heterogeneity. The tumor marker index (TMI), TMI = STM×(number of positive tumor markers+1), was used for diagnosis. The THIs of lung, gastric, and liver cancers were 8.33, 9.63, and 5.2, respectively, while the ROC-areas under the curve for TMI were 0.862, 0.809, and 0.966. In this study, we developed a novel index for tumor heterogeneity based on the expression of various routinely evaluated serum tumor markers. Development of an evaluation system for tumor heterogeneity on the basis of this index could provide an effective diagnostic tool for some cancers. Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  1. Effect of diagnosis and treatment of clinical endometritis based on vaginal discharge score grading system in postpartum Holstein cows.

    Science.gov (United States)

    Okawa, Hiroaki; Fujikura, Atsushi; Wijayagunawardane, Missaka M P; Vos, Peter L A M; Taniguchi, Masayasu; Takagi, Mitsuhiro

    2017-09-12

    In this study, the prevalence, effectiveness of diagnosis, and treatment based on vaginal discharge score (VDS) of clinical endometritis in cattle were evaluated. To detect clinical endometritis and classify its severity, vaginoscopy was performed during 21 to 60 days postpartum in 164 Holstein cows consisting of 229 lactations. Groups were defined using the 4-point VDS scale. Study groups included the following: non-endometritis (VDS=0; no/clear mucus; NEM group; n=168); mild endometritis, no treatment (VDS=1; mucus containing flecks of white/off-white pus; NTR group; n=30); and severe endometritis, treated with PGF2α (VDS≥2; discharge containing discharge containing >50% pus, and fluid or uterine horn asymmetry; TEM group; n=31). Cows treated with PGF2α that did not recover (VDS≥1, n=5) received intrauterine procaine penicillin and streptomycin. Prevalence of clinical endometritis (VDS≥1) was 26.6%. The NTR group required significantly more artificial inseminations per pregnancy than NEM and TEM groups (2.8 ± 1.8 vs 2.0 ± 1.3, 1.9 ± 0.8, PDiagnosis and treatment of clinical endometritis based on a VDS grading system may improve dairy herd reproductive performance.

  2. An expert system in medical diagnosis

    International Nuclear Information System (INIS)

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

    2001-01-01

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

  3. Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

    Directory of Open Access Journals (Sweden)

    Tian Han

    2006-01-01

    Full Text Available This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT, feature extraction, genetic algorithm (GA, and neural network (ANN techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

  4. An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine.

    Science.gov (United States)

    Avci, Derya; Dogantekin, Akif

    2016-01-01

    Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

  5. How did market competition affect outpatient utilization under the diagnosis-related group-based payment system?

    Science.gov (United States)

    Kim, Seung Ju; Park, Eun-Cheol; Kim, Sun Jung; Han, Kyu-Tae; Jang, Sung-In

    2017-06-01

    Although competition is known to affect quality of care, less is known about the effects of competition on outpatient health service utilization under the diagnosis-related group payment system. This study aimed to evaluate these effects and assess differences before and after hospitalization in South Korea. Population-based retrospective observational study. We used two data set including outpatient data and hospitalization data from National Health Claim data from 2011 to 2014. Participants who were admitted to the hospital for hemorrhoidectomy were included. A total of 804 884 hospitalizations were included in our analysis. The outcome variables included the costs associated with outpatient examinations and the number of outpatient visits within 30 days before and after hospitalization. High-competition areas were associated with lower pre-surgery examination costs (rate ratio [RR]: 0.88, 95% confidence interval [CI]: 0.88-0.89) and fewer outpatient visits before hospitalization (RR: 0.98, 95% CI: 0.98-0.99) as well as after hospitalization compared with moderate-competition areas. Our study reveals that outpatient health service utilization is affected by the degree of market competition. Future evaluations of hospital performance should consider external factors such as market structure and hospital location. © The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  6. Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

    Directory of Open Access Journals (Sweden)

    Runxia Guo

    2016-01-01

    Full Text Available The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

  7. Intelligent fault isolation and diagnosis for communication satellite systems

    Science.gov (United States)

    Tallo, Donald P.; Durkin, John; Petrik, Edward J.

    1992-01-01

    Discussed here is a prototype diagnosis expert system to provide the Advanced Communication Technology Satellite (ACTS) System with autonomous diagnosis capability. The system, the Fault Isolation and Diagnosis EXpert (FIDEX) system, is a frame-based system that uses hierarchical structures to represent such items as the satellite's subsystems, components, sensors, and fault states. This overall frame architecture integrates the hierarchical structures into a lattice that provides a flexible representation scheme and facilitates system maintenance. FIDEX uses an inexact reasoning technique based on the incrementally acquired evidence approach developed by Shortliffe. The system is designed with a primitive learning ability through which it maintains a record of past diagnosis studies.

  8. Model based fault detection and diagnosis using structured residual approach in a multi-input multi-output system

    Directory of Open Access Journals (Sweden)

    Asokan A.

    2007-01-01

    Full Text Available Fault detection and isolation (FDI is a task to deduce from observed variable of the system if any component is faulty, to locate the faulty components and also to estimate the fault magnitude present in the system. This paper provides a systematic method of fault diagnosis to detect leak in the three-tank process. The proposed scheme makes use of structured residual approach for detection, isolation and estimation of faults acting on the process [1]. This technique includes residual generation and residual evaluation. A literature review showed that the conventional fault diagnosis methods like the ordinary Chisquare (ψ2 test method, generalized likelihood ratio test have limitations such as the "false alarm" problem. From the results it is inferred that the proposed FDI scheme diagnoses better when compared to other conventional methods.

  9. A distributed expert system for fault diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Cardozo, E.; Talukdar, S.N.

    1988-05-01

    This paper describes a hybrid approach to synthesizing solutions for diagnosis and set covering problems from the area of power system operations. The approach combines expert systems written in a rule-based language (OPS5) with algorithmic programs written in C and Lisp. An environment called DPSK has been developed to allow these programs to be run in parallel in a network of computers. Speeds sufficient for real-time applications can thereby be obtained.

  10. Fault diagnosis for dynamic power system

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  11. Expert system for nuclear power plant feedwater system diagnosis

    International Nuclear Information System (INIS)

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

    1987-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Wei Li

    2013-01-01

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

  13. Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

    Science.gov (United States)

    Gadsden, S. Andrew; Kirubarajan, T.

    2017-05-01

    Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

  14. Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants.

    Science.gov (United States)

    Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

    2013-02-01

    Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and

  15. Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants

    Science.gov (United States)

    Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

    2013-02-01

    Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and

  16. Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

    Science.gov (United States)

    Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

    2016-03-01

    The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Active fault diagnosis in closed-loop uncertain systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2006-01-01

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

  18. An efficient diagnosis system for Parkinson's disease using kernel-based extreme learning machine with subtractive clustering features weighting approach.

    Science.gov (United States)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Zhao, Xue-Hua

    2014-01-01

    A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

  19. Design of a system based on diffuse logic for the diagnosis of the epilepsy starting from the interpretation of the electroencephalogram

    International Nuclear Information System (INIS)

    Buitrago, Eder

    2002-01-01

    The purpose of this investigation was to design of a system based on Diffuse Logic for the diagnosis of the epilepsy starting from the electroencephalogram interpretation. To achieve the elaboration of the design of the system, they were carried out bibliographical consultations in different sources of information related with the topic, like interviews semi structured and structured to an intentional sample contained by a group of experts in the area of diagnostic of the epilepsy. These techniques contributed the necessary information to determine the current situation of the process of diagnostic of the epilepsy and the bases of the proposed system, as well as they allowed to determine the necessity and feasibility of the application of the Diffuse Logic in the diagnosis of the epilepsy. The proposal is presented like a simple useful tool for the experts in diagnostic, but it is not conceived to substitute the expert in its functions. The diagnosis processes are of complex type, and in great measure they are numerous the variables that intervene in them, are for this reason that the knowledge and the expert's abilities will be the determinant for the elaboration of the definitive diagnosis

  20. Expert System For Diagnosis Of Skin Diseases

    Directory of Open Access Journals (Sweden)

    A.A.L.C. Amarathunga

    2015-01-01

    Full Text Available Abstract Dermatology is a one of major session of medicine that concerned with the diagnosis and treatment of skin diseases. Skin diseases are the most common form of disease in humans. Recently many of researchers have advocated and developed the imaging of human vision or in the loop approach to visual object recognition. This research paper presents a development of a skin diseases diagnosis system which allows user to identify diseases of the human skin and to provide advises or medical treatments in a very short time period. For this purpose user will have to upload an image of skin disease to our system and answer questions based on their skin condition or symptoms. It will be used to detect diseases of the skin and offer a treatment recommendation. This system uses technologies such as image processing and data mining for the diagnosis of the disease of the skin. The image of skin disease is taken and it must be subjected to various preprocessing for noise eliminating and enhancement of the image. This image is immediately segmentation of images using threshold values. Finally data mining techniques are used to identify the skin disease and to suggest medical treatments or advice for users. This expert system exhibits disease identification accuracy of 85 for Eczema 95 for Impetigo and 85 for Melanoma.

  1. Energy systems Diagnosis in developing countries

    International Nuclear Information System (INIS)

    Girod, J.

    1991-01-01

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

  2. Diffuse neuroendocrine system and mitochondrial diseases: molecular and cellular bases of pathogenesis, new approaches to diagnosis and therapy.

    Science.gov (United States)

    Kvetnoy, Igor M.; Hernández-Yago, José; Hernández, José Miguel; Kvetnaia, Tatiana V.; Reiter, Russel J.; Khavinson, Vladimir Kh.

    2000-01-01

    Structural and functional alterations of mitochondria have been shown to be responsible for a wide variety of clinical disorders that are referred to as "mitochondrial diseases." It is now obvious that many factors are involved in transport of mitochondrial proteins including cytokines, chaperones, chemokines, neurosteroids, ubiquitin and many others. At the same time the participation and the role of biogenic amines and peptide hormones (which are produced by the diffuse neuroendocrine system cells located in different organs) in endogenous mechanisms of mitochondrial diseases are still unknown. Taking into account the wide spectrum of biological effects of biogenic amines and peptide hormones, and especially their regulatory role for intercellular communication, it seems important to analyze the possible participation of these molecules in the pathogenesis of mitochondrial disorders as well as to draw up new ways for elaboration of new markers for lifetime diagnosis, definition of prognosis and efficiency of specific therapy in neurodegenerative diseases.

  3. A Fuzzy Petri Nets System for Heart Disease Diagnosis

    Directory of Open Access Journals (Sweden)

    Hussin Attya Lafta

    2017-02-01

    Full Text Available In this paper we have proposed a Fuzzy Petri Nets Expert System for heart disease diagnosis. The aim of the proposed system is simulating experience of experts in Diagnosis Heart Disease stage, based on Fuzzy Rule System and modeling reasoning operation by using Fuzzy Petri Nets. The database taken from Machine Learning Repository Center for machine learning and intelligent system. The system has 11 input fields and one output field. The accuracy of proposed system is 75%.

  4. Fault detection and diagnosis of photovoltaic systems

    Science.gov (United States)

    Wu, Xing

    The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on fault diagnosis for PV systems. Faults in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of faults will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the fault modalities will be characterized for each type of fault. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a fault occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline fault curves will aid in fault diagnosis.

  5. European evidence-based recommendations for diagnosis and treatment of childhood-onset systemic lupus erythematosus : The SHARE initiative

    NARCIS (Netherlands)

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

    2017-01-01

    Childhood-onset systemic lupus erythematosus (cSLE) is a rare, multisystem and potentially life-Threatening autoimmune disorder with significant associated morbidity. Evidence-based guidelines are sparse and management is often based on clinical expertise. SHARE (Single Hub and Access point for

  6. Active fault diagnosis in closed-loop uncertain systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2006-01-01

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

  7. Development of a model based scoring system for diagnosis of canine disseminated intravascular coagulation with independent assessment of sensitivity and specificity

    DEFF Research Database (Denmark)

    Wiinberg, Bo; Jensen, Asger Lundorff; Johansson, Per Ingemar

    2010-01-01

    coagulation tests for the diagnosis of DIC in dogs. To develop the scoring system, 100 dogs consecutively admitted to an intensive care unit (ICU) with diseases predisposing for DIC were enrolled prospectively (group A). The validation involved 50 dogs consecutively diagnosed with diseases predisposing......A template for a scoring system for disseminated intravascular coagulation (DIC) in humans has been proposed by the International Society on Thrombosis and Haemostasis (ISTH). The objective of this study was to develop and validate a similar objective scoring system based on generally available...... of the model was sustained by prospective evaluation in group B (sensitivity 83.3%, specificity 77.3%). Based on commonly used, plasma-based coagulation assays, it was possible to design an objective diagnostic scoring system for canine DIC with a high sensitivity and specificity....

  8. Diagnosis in the Enterprise Management System

    OpenAIRE

    Skrynkovskyy Ruslan M.; Pawlowski Grzegorz

    2016-01-01

    The aim of the article is to define the role and place of the diagnosis management system in the structure of the task system of the enterprise diagnosis. There suggested the essence of the concept of «diagnosis of the enterprise», which is understood as the process of identification, analysis and evaluation of the enterprise state and trends in its changes (changes of the state) on the basis of relevant business indicators in order to develop recommendations on the eliminat...

  9. Systemic AA amyloidosis: epidemiology, diagnosis, and management.

    Science.gov (United States)

    Real de Asúa, Diego; Costa, Ramón; Galván, Jose María; Filigheddu, María Teresa; Trujillo, Davinia; Cadiñanos, Julen

    2014-01-01

    The term "amyloidosis" encompasses the heterogeneous group of diseases caused by the extracellular deposition of autologous fibrillar proteins. The global incidence of amyloidosis is estimated at five to nine cases per million patient-years. While amyloid light-chain (AL) amyloidosis is more frequent in developed countries, amyloid A (AA) amyloidosis is more common in some European regions and in developing countries. The spectrum of AA amyloidosis has changed in recent decades owing to: an increase in the median age at diagnosis; a percent increase in the frequency of primary AL amyloidosis with respect to the AA type; and a substantial change in the epidemiology of the underlying diseases. Diagnosis of amyloidosis is based on clinical organ involvement and histological evidence of amyloid deposits. Among the many tinctorial characteristics of amyloid deposits, avidity for Congo red and metachromatic birefringence under unidirectional polarized light remain the gold standard. Once the initial diagnosis has been made, the amyloid subtype must be identified and systemic organ involvement evaluated. In this sense, the (123)I-labeled serum amyloid P component scintigraphy is a safe and noninvasive technique that has revolutionized the diagnosis and monitoring of treatment in systemic amyloidosis. It can successfully identify anatomical patterns of amyloid deposition throughout the body and enables not only an initial estimation of prognosis, but also the monitoring of the course of the disease and the response to treatment. Given the etiologic diversity of AA amyloidosis, common therapeutic strategies are scarce. All treatment options should be based upon a greater control of the underlying disease, adequate organ support, and treatment of symptoms. Nevertheless, novel therapeutic strategies targeting the formation of amyloid fibrils and amyloid deposition may generate new expectations for patients with AA amyloidosis.

  10. Organizational Diagnosis in Project-Based Companies

    Directory of Open Access Journals (Sweden)

    Behrouz Zarei

    2014-05-01

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

  11. Intelligent Systems for Active Program Diagnosis

    Directory of Open Access Journals (Sweden)

    Haider Ali Ramadhan

    2000-12-01

    Full Text Available Intelligent program diagnosis systems are computer programs capable of analyzing logical and design-level errors and misconceptions in programs. Upon discovering the errors, these systems provide intelligent feedback and thus guide the users in the problem-solving process. Intelligent program diagnosis systems are classified by their primary means of program analysis. The most distinct split is between those systems that are unable to analyze partial code segments as they are provided by the user and must wait until the entire solution code is completed before attempting any diagnosis, and those that are capable of analyzing partial solutions and providing proper guidance whenever an error or misconception is encountered. This paper gives an overview of the field and then critically compares work accomplished on several closely related active diagnosis systems, emphasizing such issues as the representation techniques used to capture the domain knowledge required for the diagnosis, ability to handle the diagnosis of partial code segments of the solutions, features of the user interfaces, and methodologies used in conducting the diagnosis process. Finally the paper presents a detailed discussion on issues related to active program diagnosis along with various design considerations to improve the engineering of this approach to intelligent diagnosis. The discussion presented in this paper tackles the issues referred above within the context of DISCOVER, an intelligent system for programming by discovery.

  12. Aid system in the attention direction for accidents diagnosis at nuclear power plants based on artificial intelligence

    International Nuclear Information System (INIS)

    Costa, Rafael Gomes da

    2009-01-01

    Transient identification in Nuclear Power Plant (NPP) is often a very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event Several systems based on specialist systems, neural-networks, and fuzzy logic have been developed for transient identification. In the work, we investigate the possibility of using a Neuro Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A preliminary evaluation of the developed system was made at the Human-System Interface Laboratory (LABIHS). The obtained results show that the system can help the operators to take decisions during transients/accidents in the plant (author)

  13. A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer.

    Science.gov (United States)

    Miyaki, Rie; Yoshida, Shigeto; Tanaka, Shinji; Kominami, Yoko; Sanomura, Yoji; Matsuo, Taiji; Oka, Shiro; Raytchev, Bisser; Tamaki, Toru; Koide, Tetsushi; Kaneda, Kazufumi; Yoshihara, Masaharu; Chayama, Kazuaki

    2015-02-01

    To evaluate the usefulness of a newly devised computer system for use with laser-based endoscopy in differentiating between early gastric cancer, reddened lesions, and surrounding tissue. Narrow-band imaging based on laser light illumination has come into recent use. We devised a support vector machine (SVM)-based analysis system to be used with the newly devised endoscopy system to quantitatively identify gastric cancer on images obtained by magnifying endoscopy with blue-laser imaging (BLI). We evaluated the usefulness of the computer system in combination with the new endoscopy system. We evaluated the system as applied to 100 consecutive early gastric cancers in 95 patients examined by BLI magnification at Hiroshima University Hospital. We produced a set of images from the 100 early gastric cancers; 40 flat or slightly depressed, small, reddened lesions; and surrounding tissues, and we attempted to identify gastric cancer, reddened lesions, and surrounding tissue quantitatively. The average SVM output value was 0.846 ± 0.220 for cancerous lesions, 0.381 ± 0.349 for reddened lesions, and 0.219 ± 0.277 for surrounding tissue, with the SVM output value for cancerous lesions being significantly greater than that for reddened lesions or surrounding tissue. The average SVM output value for differentiated-type cancer was 0.840 ± 0.207 and for undifferentiated-type cancer was 0.865 ± 0.259. Although further development is needed, we conclude that our computer-based analysis system used with BLI will identify gastric cancers quantitatively.

  14. Fault diagnosis in sparse multiprocessor systems

    Science.gov (United States)

    Blough, Douglas M.; Sullivan, Gregory F.; Masson, Gerald M.

    1988-01-01

    The problem of fault diagnosis in multiprocessor systems is considered under a uniformly probabilistic model in which processors are faulty with probability p. This work focuses on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. The number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis technique.

  15. Thorax X-ray diagnostics. DDS (double base description system). Uniform terminology and standardized sytematics. Correct diagnosis

    International Nuclear Information System (INIS)

    Kulke, H.M.

    2012-01-01

    The booklet describes the so called DDS (double-base description system) to be used in the frame of medical thorax X-ray examinations with modern imaging devices. The following issues are discussed: Description features, shadow characterization, general fundamentals, procedural methodology, diagnostic findings protocol, examples and case descriptions.

  16. Diagnosis of wind turbine rotor system

    DEFF Research Database (Denmark)

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

    2016-01-01

    This paper describes a model free method for monitoring and fault diagnosis of the elements in a rotor system for a wind turbine. The diagnosis as well as the monitoring is done without using any model of the wind turbine and the applied controller or a description of the wind profile. The method...

  17. An intelligent diagnosis model based on rough set theory

    Science.gov (United States)

    Li, Ze; Huang, Hong-Xing; Zheng, Ye-Lu; Wang, Zhou-Yuan

    2013-03-01

    Along with the popularity of computer and rapid development of information technology, how to increase the accuracy of the agricultural diagnosis becomes a difficult problem of popularizing the agricultural expert system. Analyzing existing research, baseing on the knowledge acquisition technology of rough set theory, towards great sample data, we put forward a intelligent diagnosis model. Extract rough set decision table from the samples property, use decision table to categorize the inference relation, acquire property rules related to inference diagnosis, through the means of rough set knowledge reasoning algorithm to realize intelligent diagnosis. Finally, we validate this diagnosis model by experiments. Introduce the rough set theory to provide the agricultural expert system of great sample data a effective diagnosis model.

  18. Sub-module Short Circuit Fault Diagnosis in Modular Multilevel Converter Based on Wavelet Transform and Adaptive Neuro Fuzzy Inference System

    DEFF Research Database (Denmark)

    Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede

    2015-01-01

    by employing wavelet transform under different fault conditions. Then the fuzzy logic rules are automatically trained based on the fuzzified fault features to diagnose the different faults. Neither additional sensor nor the capacitor voltages are needed in the proposed method. The high accuracy, good...... for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...

  19. A rule-based decision-making diagnosis system to evaluate arteriovenous shunt stenosis for hemodialysis treatment of patients using fuzzy petri nets.

    Science.gov (United States)

    Chen, Wei-Ling; Kan, Chung-Dann; Lin, Chia-Hung; Chen, Tainsong

    2014-03-01

    This paper proposes a rule-based decision-making diagnosis system to evaluate arteriovenous shunt (AVS) stenosis for long-term hemodialysis treatment of patients using fuzzy petri nets (FPNs). AVS stenoses are often associated with blood sounds, resulting from turbulent flow over the narrowed blood vessel. Phonoangiography provides a noninvasive technique to monitor the sounds of the AVS. Since the power spectra changes in frequency and amplitude with the degree of AVS stenosis, it is difficult to make a human-made decision to judge the degree using a combination of those variances. The Burg autoregressive (AR) method is used to estimate the frequency spectra of a phonoangiographic signal and identify the characteristic frequencies. A rule-based decision-making method, FPNs, is designed as a decision-making system to evaluate the degree of stenosis (DOS) in routine examinations. For 42 long-term follow-up patients, the examination results show the proposed diagnosis system has greater efficiency in evaluating AVS stenosis.

  20. Creating of structure of facts for the knowledge base of an expert system for wind power plant's equipment diagnosis

    Science.gov (United States)

    Duer, Stanisław; Wrzesień, Paweł; Duer, Radosław

    2017-10-01

    This article describes rules and conditions for making a structure (a set) of facts for an expert knowledge base of the intelligent system to diagnose Wind Power Plants' equipment. Considering particular operational conditions of a technical object, that is a set of Wind Power Plant's equipment, this is a significant issue. A structural model of Wind Power Plant's equipment is developed. Based on that, a functional - diagnostic model of Wind Power Plant's equipment is elaborated. That model is a basis for determining primary elements of the object structure, as well as for interpreting a set of diagnostic signals and their reference signals. The key content of this paper is a description of rules for building of facts on the basis of developed analytical dependence. According to facts, their dependence is described by rules for transferring of a set of pieces of diagnostic information into a specific set of facts. The article consists of four chapters that concern particular issues on the subject.

  1. Fault tolerant control based on active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2005-01-01

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

  2. Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

    Science.gov (United States)

    Polverino, Pierpaolo; Frisk, Erik; Jung, Daniel; Krysander, Mattias; Pianese, Cesare

    2017-07-01

    The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.

  3. [Germ cell tumors - documentation of diagnosis and therapy by means of a web-based modular database system].

    Science.gov (United States)

    Schreiter, N; Jagota, A; Popken, G; Akhavuz, O; Nitzke, T; Düffelmeyer, M; Fischer, T; Schostak, M; Miller, K; Schrader, M

    2008-11-01

    Health telematics is gaining ground worldwide as it promises the bridging of distances in space and time as well as a highly effective use of financial and other resources. In Germany the development and introduction of a national telematic platform is in the foreground at present. However, there are a number of more specialised projects already in existence. The aim of this study was to develop an internet platform to document the quality of individual sections of treatment for patients with germ cell tumours in Berlin and to improve the therapy in conformity with the S2 guidelines. As a pilot project, a web-based modular database system (WBMDS) was developed, which can be used by any physician involved in the treatment of germ cell tumour patients from any computer connected to the internet. The WBMDS proved to be a practicable system of documentation. Data protection was ensured by pseudonyms as well as symmetrical and asymmetrical coding. The size of the extended documentation mask that had initially seemed to be necessary for valid documentation appeared to be too user-unfriendly with its 833 items. To meet the requirements of the user as well as of the documentation, a compact variant with 496 input fields was designed. On a random basis, treatment not in conformity with the guidelines could be detected in 20 % of 151 patients with the help of this system. For the successful use of an oncological database the following showed to be essential:[nl]Queries clearly defined for later statistical evaluation,[nl]clear separation between the phases of planning and implementation,[nl]a size of the database that does not make excessive demands on the user,[nl]intensive training of the users.[nl]The modular database system established proved to be well suitable for a quality-ensuring longitudinal case documentation, which can also be applied to other tumour entities.

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

    International Nuclear Information System (INIS)

    Tamilselvan, Prasanna; Wang, Pingfeng

    2013-01-01

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

  5. Qualitative model-based diagnosis using possibility theory

    Science.gov (United States)

    Joslyn, Cliff

    1994-01-01

    The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.

  6. Creating of structure of facts for the knowledge base of an expert system for wind power plant's equipment diagnosis

    Directory of Open Access Journals (Sweden)

    Duer Stanisław

    2017-01-01

    Full Text Available This article describes rules and conditions for making a structure (a set of facts for an expert knowledge base of the intelligent system to diagnose Wind Power Plants’ equipment. Considering particular operational conditions of a technical object, that is a set of Wind Power Plant's equipment, this is a significant issue. A structural model of Wind Power Plant's equipment is developed. Based on that, a functional – diagnostic model of Wind Power Plant's equipment is elaborated. That model is a basis for determining primary elements of the object structure, as well as for interpreting a set of diagnostic signals and their reference signals. The key content of this paper is a description of rules for building of facts on the basis of developed analytical dependence. According to facts, their dependence is described by rules for transferring of a set of pieces of diagnostic information into a specific set of facts. The article consists of four chapters that concern particular issues on the subject.

  7. A Computer-Aided Diagnosis System With EEG Based on the P3b Wave During an Auditory Odd-Ball Task in Schizophrenia.

    Science.gov (United States)

    Santos-Mayo, Lorenzo; San-Jose-Revuelta, Luis M; Arribas, Juan Ignacio

    2017-02-01

    To design a Computer-aided diagnosis (CAD) system using an optimized methodology over the P3b wave in order to objectively and accurately discriminate between healthy controls (HC) and schizophrenic subjects (SZ). We train, test, analyze, and compare various machine learning classification approaches optimized in terms of the correct classification rate (CCR), the degenerated Youden's index (DYI) and the area under the receiver operating curve (AUC). CAD system comprises five stages: electroencephalography (EEG) preprocessing, feature extraction, seven electrode groupings, discriminant feature selection, and binary classification. With two optimal combinations of electrode grouping, filtering, feature selection algorithm, and classification machine, we get either a mean CCR = 93.42%, specificity = 0.9673, sensitivity = 0.8727, DYI = 0.9188, and AUC = 0.9567 (total-15 Hz-J5-MLP), or a mean CCR = 92.23%, specificity = 0.9499, sensitivity = 0.8838, DYI = 0.9162, and AUC = 0.9807 (right hemisphere-35 Hz-J5-SVM), which to our knowledge are higher than those available to date. We have verified that a more restrictive low-pass filtering achieves higher CCR as compared to others at higher frequencies in the P3b wave. In addition, results validate previous hypothesis about the importance of the parietal-temporal region, associated with memory processing, allowing us to identify powerful {feature,electrode} pairs in the diagnosis of schizophrenia, achieving higher CCR and AUC in classification of both right and left Hemispheres, and parietal-temporal EEG signals, like, for instance, the {PSE, P4} pair (J5 and mutual information feature selection). Diagnosis of schizophrenia is made thoroughly by psychiatrists but as any human-based decision that has a subjective component. This CAD system provides the human expert with an objective complimentary measure to help him in diagnosing schizophrenia.

  8. Diagnosis in the Enterprise Management System

    Directory of Open Access Journals (Sweden)

    Skrynkovskyy Ruslan M.

    2016-08-01

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

  9. Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.

    Science.gov (United States)

    de Carvalho Filho, Antonio Oseas; Silva, Aristófanes Corrêa; de Paiva, Anselmo Cardoso; Nunes, Rodolfo Acatauassú; Gattass, Marcelo

    2017-08-01

    Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.

  10. Diagnosis aids with artificial intelligence in the PSAD system

    International Nuclear Information System (INIS)

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

    1996-01-01

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

  11. Bayesian-network-based fault diagnosis methodology of subsea jumper

    Science.gov (United States)

    Cai, Baoping; Liu, Yonghong; Huang, Lei; Hu, Song; Xue, Haitao; Wang, Jiaxing

    2017-10-01

    The paper proposes a Bayesian-network-based real-time fault diagnosis methodology of M-shaped subsea jumper. Finite element models of a typical M-shaped subsea jumper system are built to get the data for diagnosis. Netica is Bayesian-network -based software and is used to construct diagnosis models of the jumper in two main loading conditions which are falling objects and seabed moving. The results show that the accuracy of falling objects diagnosis model with four faults is 100%, and the accuracy of seabed moving diagnosis model with two faults is also 100%. Combine the two models into one and the accuracy of combined model is 96.59%. The effectiveness of the proposed method is validated.

  12. Application of computer-based operator instruction system to plant diagnosis on fission product transport and release in nuclear power plants

    International Nuclear Information System (INIS)

    Kodaira, Hideki; Kondo, Shunsuke; Togo, Yasumasa

    1985-01-01

    A computer-based operator instruction system (COINS) for diagnosing fission product (FP) transport and release in nuclear power plants (NPPs) is applied to plant diagnosis in combination with the computational code ''SACHET'', which evaluates the dynamic FP inventories in the multiple compartment system of pressurized water reactor (PWR) plants. The COINS can be described in the most general way as a computer-based information processing system which takes in plant data, processes it, and displays the result to the NPP's operating crew. Our major concern for the COINS is, however, not to evaluate general plant dynamics, but to monitor the distribution of the whole radioactive materials such as FP, and to diagnose the plant state in the view of FP transport during the NPP's lifetime. An algorithm of the stochastic approximation for the adaptive pattern classification of the dynamic distributed parameter system is introduced in the COINS software, where a nonlinear functional of the set of monitored data and the system parameters is used. By the use of the COINS, it becomes possible to understand PWR plant states precisely in the view of FP transport and release during normal operation, to identify the occurrenses of the unusual events clearly, and to forecast the potential hazards reasonably. (author)

  13. Fault diagnosis based on continuous simulation models

    Science.gov (United States)

    Feyock, Stefan

    1987-01-01

    The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.

  14. Wireless laptop-based phonocardiograph and diagnosis

    Directory of Open Access Journals (Sweden)

    Amy T. Dao

    2015-08-01

    Full Text Available Auscultation is used to evaluate heart health, and can indicate when it’s needed to refer a patient to a cardiologist. Advanced phonocardiograph (PCG signal processing algorithms are developed to assist the physician in the initial diagnosis but they are primarily designed and demonstrated with research quality equipment. Therefore, there is a need to demonstrate the applicability of those techniques with consumer grade instrument. Furthermore, routine monitoring would benefit from a wireless PCG sensor that allows continuous monitoring of cardiac signals of patients in physical activity, e.g., treadmill or weight exercise. In this work, a low-cost portable and wireless healthcare monitoring system based on PCG signal is implemented to validate and evaluate the most advanced algorithms. Off-the-shelf electronics and a notebook PC are used with MATLAB codes to record and analyze PCG signals which are collected with a notebook computer in tethered and wireless mode. Physiological parameters based on the S1 and S2 signals and MATLAB codes are demonstrated. While the prototype is based on MATLAB, the later is not an absolute requirement.

  15. A diagnosis system for plant operation support

    International Nuclear Information System (INIS)

    Sundheimer, S.; Lorenzetti, J.; Lamana, C.

    1990-01-01

    The present article describes a diagnosis system for abnormal power plant events. The design is modular and uses a shell written in C languaje and a knowledge basis that can be changed easily. At present the system works with a reduced knowledge for primary and secondery leacks. The mitigation procedure is being written with the help of operation staff

  16. Design of a nursing clinical decision support system applying nursing diagnosis and nursing evaluation model based data mining.

    Science.gov (United States)

    Kim, Hyungyung; Kim, Insook; Chae, Yougmoon

    2006-01-01

    This study a methodological study; to acquire knowledge on the nursing process by steps of knowledge definition, collection, and representation; then, to design a data warehouse and nursing process clinical decision support system.

  17. Active fault diagnosis based on stochastic tests

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2008-01-01

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

  18. Infrared thermography based diagnosis of inter-turn fault and cooling system failure in three phase induction motor

    Science.gov (United States)

    Singh, Gurmeet; Naikan, V. N. A.

    2017-12-01

    Thermography has been widely used as a technique for anomaly detection in induction motors. International Electrical Testing Association (NETA) proposed guidelines for thermographic inspection of electrical systems and rotating equipment. These guidelines help in anomaly detection and estimating its severity. However, it focus only on location of hotspot rather than diagnosing the fault. This paper addresses two such faults i.e. inter-turn fault and failure of cooling system, where both results in increase of stator temperature. Present paper proposes two thermal profile indicators using thermal analysis of IRT images. These indicators are in compliance with NETA standard. These indicators help in correctly diagnosing inter-turn fault and failure of cooling system. The work has been experimentally validated for healthy and with seeded faults scenarios of induction motors.

  19. An intelligent medical system for diagnosis of bone diseases

    International Nuclear Information System (INIS)

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

    1994-01-01

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

  20. Implementation of a Model-Tracing-Based Learning Diagnosis System to Promote Elementary Students' Learning in Mathematics

    Science.gov (United States)

    Chu, Yian-Shu; Yang, Haw-Ching; Tseng, Shian-Shyong; Yang, Che-Ching

    2014-01-01

    Of all teaching methods, one-to-one human tutoring is the most powerful method for promoting learning. To achieve this aim and reduce teaching load, researchers developed intelligent tutoring systems (ITSs) to employ one-to-one tutoring (Aleven, McLaren, & Sewall, 2009; Aleven, McLaren, Sewall, & Koedinger, 2009; Anderson, Corbett,…

  1. Neural expert decision support system for stroke diagnosis

    Science.gov (United States)

    Kupershtein, Leonid M.; Martyniuk, Tatiana B.; Krencin, Myhail D.; Kozhemiako, Andriy V.; Bezsmertnyi, Yurii; Bezsmertna, Halyna; Kolimoldayev, Maksat; Smolarz, Andrzej; Weryńska-Bieniasz, RóŻa; Uvaysova, Svetlana

    2017-08-01

    In the work the hybrid expert system for stroke diagnosis was presented. The base of expert system consists of neural network and production rules. This program can quickly and accurately set to the patient preliminary and final diagnoses, get examination and treatment plans, print data of patient, analyze statistics data and perform parameterized search for patients.

  2. Fault Diagnosis of Power Systems Using Intelligent Systems

    Science.gov (United States)

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

    1996-01-01

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

  3. Expert system based on cases for a diagnosis system in real time; Sistema experto basado en casos para un sistema de diagnostico en tiempo real

    Energy Technology Data Exchange (ETDEWEB)

    Espinosa R, Alfredo; Quintero R, Agustin; Zambrano D, S Venecia [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico)

    2006-07-01

    This article presents the development of an Expert System based in the Reasoning Based on Cases methodology. Such system was performed with the purpose of creating an information system in charge of supervising and diagnosing the status of the main equipment of fossil fuel power plants for electricity generation. Here is presented the reasons why this methodology was used for the expert system and why Induce-It -the specialized tool that implements it- was also chosen, as well as the analysis made for the disposition of the operative architecture of the Expert System, the very development of this software architecture and, finally, the validation of the correct operation of this system by means of a simulator that simultaneously puts to the test the error handling of the Expert System. [Spanish] Este articulo presenta el procedimiento que siguio el desarrollo de un Sistema Experto asentado en la metodologia de Razonamiento Basado en Casos; realizado con el fin de crear un sistema de informacion encargado de supervisar y diagnosticar el estado de los equipos principales de centrales de generacion termoelectrica. Se expone justificadamente la seleccion de la metodologia del sistema experto y de la herramienta especializada que lo implementa (Induce-It), asi como el analisis realizado para la disposicion de la arquitectura operativa del Sistema Experto, el desarrollo mismo de esta arquitectura del software y, finalmente, la validacion del correcto funcionamiento de este sistema mediante un simulador que a la vez pone a prueba el manejo de errores del Sistema Experto.

  4. Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers

    Directory of Open Access Journals (Sweden)

    N. V. Kolesov

    2013-01-01

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

  5. Failure Diagnosis for the Holdup Tank System via ISFA

    Energy Technology Data Exchange (ETDEWEB)

    Li, Huijuan; Bragg-Sitton, Shannon; Smidts, Carol

    2016-11-01

    This paper discusses the use of the integrated system failure analysis (ISFA) technique for fault diagnosis for the holdup tank system. ISFA is a simulation-based, qualitative and integrated approach used to study fault propagation in systems containing both hardware and software subsystems. The holdup tank system consists of a tank containing a fluid whose level is controlled by an inlet valve and an outlet valve. We introduce the component and functional models of the system, quantify the main parameters and simulate possible failure-propagation paths based on the fault propagation approach, ISFA. The results show that most component failures in the holdup tank system can be identified clearly and that ISFA is viable as a technique for fault diagnosis. Since ISFA is a qualitative technique that can be used in the very early stages of system design, this case study provides indications that it can be used early to study design aspects that relate to robustness and fault tolerance.

  6. An expert system approach for safety diagnosis

    International Nuclear Information System (INIS)

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

    1988-01-01

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

  7. Development of water chemistry diagnosis system for BWR primary loop

    International Nuclear Information System (INIS)

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

    1988-01-01

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

  8. A fault diagnosis system for nuclear power plant operation

    International Nuclear Information System (INIS)

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

    2002-01-01

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

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

    NARCIS (Netherlands)

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

    2006-01-01

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

  10. Fault diagnosis based on controller modification

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2015-01-01

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

  11. Faults and Diagnosis Systems in Power Converters

    DEFF Research Database (Denmark)

    Lee, Kyo-Beum; Choi, Uimin

    2014-01-01

    efforts have been put into making these systems better in terms of reliability in order to achieve high power source availability, reduce the cost of energy and also increase the reliability of overall systems. Among the components used in power converters, a power device and a capacitor fault occurs most...... frequently. Therefore, it is important to monitor the power device and capacitor fault to increase the reliability of power electronics. In this chapter, the diagnosis methods for power device fault will be discussed by dividing into open- and short-circuit faults. Then, the condition monitoring methods...

  12. Navigation System Fault Diagnosis for Underwater Vehicle

    DEFF Research Database (Denmark)

    Falkenberg, Thomas; Gregersen, Rene Tavs; Blanke, Mogens

    2014-01-01

    This paper demonstrates fault diagnosis on unmanned underwater vehicles (UUV) based on analysis of structure of the nonlinear dynamics. Residuals are generated using dierent approaches in structural analysis followed by statistical change detection. Hypothesis testing thresholds are made signal...... based to cope with non-ideal properties seen in real data. Detection of both sensor and thruster failures are demonstrated. Isolation is performed using the residual signature of detected faults and the change detection algorithm is used to assess severity of faults by estimating their magnitude...

  13. Computerized tongue diagnosis based on Bayesian networks.

    Science.gov (United States)

    Pang, Bo; Zhang, David; Li, Naimin; Wang, Kuanquan

    2004-10-01

    Tongue diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited-application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, Bayesian networks are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on a group of 455 patients affected by 13 common diseases as well as other 70 healthy volunteers, and the diagnostic results predicted by the previously trained Bayesian network classifiers are reported.

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

    Data.gov (United States)

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

  15. Least-cost failure diagnosis in uncertain reliability systems

    International Nuclear Information System (INIS)

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

    1996-01-01

    In many textbook solutions, for systems failure diagnosis problems studied using reliability theory and artificial intelligence, the prior probabilities of different failure states can be estimated and used to guide the sequential search for failed components after the whole system fails. In practice, however, both the component failure probabilities and the structure function of the system being examined--i.e., the mapping between the states of its components and the state of the system--may not be known with certainty. At best:, the probabilities of different hypothesized system descriptions, each specifying the component failure probabilities and the system's structure function, may be known to a useful approximation, perhaps based on sample data and previous experience. Cost-effective diagnosis of the system's failure state is then a challenging problem. Although the probabilities of component failures are aleatory, uncertainties about these probabilities and about the system structure function are epistemic. This paper examines how to make best use of both epistemic prior probabilities for system descriptions and the information gleaned from costly inspections of component states after the system fails, to minimize the average cost of identifying the failure state. Two approaches are introduced for systems dominated by aleatory uncertainties, one motivated by information theory and the other based on the idea of trying to prove a hypothesis about the identity of the failure state as efficiently as possible. While the general problem of cost-effective failure diagnosis is computationally intractable (NP-hard), both heuristics provide useful approximations on small to moderate sized problems and optimal results for certain common types of reliability systems, including series, parallel, parallel-series, and k-out-of-n systems. A hybrid heuristic that adaptively chooses which heuristic to apply next after any sequence of observations (component test results

  16. Data monitoring system of technical diagnosis system for EAST

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  17. The use of multiple models in case-based diagnosis

    Science.gov (United States)

    Karamouzis, Stamos T.; Feyock, Stefan

    1993-01-01

    The work described in this paper has as its goal the integration of a number of reasoning techniques into a unified intelligent information system that will aid flight crews with malfunction diagnosis and prognostication. One of these approaches involves using the extensive archive of information contained in aircraft accident reports along with various models of the aircraft as the basis for case-based reasoning about malfunctions. Case-based reasoning draws conclusions on the basis of similarities between the present situation and prior experience. We maintain that the ability of a CBR program to reason about physical systems is significantly enhanced by the addition to the CBR program of various models. This paper describes the diagnostic concepts implemented in a prototypical case based reasoner that operates in the domain of in-flight fault diagnosis, the various models used in conjunction with the reasoner's CBR component, and results from a preliminary evaluation.

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

    International Nuclear Information System (INIS)

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

    1996-01-01

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

  19. Tuberculosis disease diagnosis using artificial immune recognition system.

    Science.gov (United States)

    Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

    2014-01-01

    There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

  20. A Distributed and Collaborative Intelligent System for Medical Diagnosis

    Directory of Open Access Journals (Sweden)

    Wided LEJOUAD-CHAARI

    2013-08-01

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

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

    Science.gov (United States)

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

    2018-05-01

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

  2. Intelligent System for Diagnosis of a Three-Phase Separator

    OpenAIRE

    Irina Ioniţă; Liviu Ioniţă

    2016-01-01

    Intelligent systems for diagnosis have been used in a variety of domains: financial evaluation, credit scoring problem, identification of software and hardware problems of mechanical and electronic equipment, medical diagnosis, fault detection in gas-oil production plants etc. The goal of diagnosis systems is to classify the observed symptoms as being caused by some diagnosis class while advising systems perform such a classification and offer corrective remedies (recommendations). The curren...

  3. Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems.

    Science.gov (United States)

    Gao, Yangde; Karimi, Mohammad; Kudreyko, Aleksey A; Song, Wanqing

    2017-12-30

    In the marine systems, engines represent the most important part of ships, the probability of the bearings fault is the highest in the engines, so in the bearing vibration analysis, early weak fault detection is very important for long term monitoring. In this paper, we propose a novel method to solve the early weak fault diagnosis of bearing. Firstly, we should improve the alternating direction method of multipliers (ADMM), structure of the traditional ADMM is changed, and then the improved ADMM is applied to the compressed sensing (CS) theory, which realizes the sparse optimization of bearing signal for a mount of data. After the sparse signal is reconstructed, the calculated signal is restored with the minimum entropy de-convolution (MED) to get clear fault information. Finally we adopt the sample entropy. Morphological mean square amplitude and the root mean square (RMS) to find the early fault diagnosis of bearing respectively, at the same time, we plot the Boxplot comparison chart to find the best of the three indicators. The experimental results prove that the proposed method can effectively identify the early weak fault diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  4. An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

    Directory of Open Access Journals (Sweden)

    Chao Ma

    2014-01-01

    Full Text Available A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM, has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC curve (AUC, f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

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

    Science.gov (United States)

    Hoppe, H. Ulrich

    1994-01-01

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

  6. A qualitative diagnosis method for a continuous process monitor system

    Energy Technology Data Exchange (ETDEWEB)

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

    1993-12-31

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

  7. A qualitative diagnosis method for a continuous process monitor system

    International Nuclear Information System (INIS)

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

    1993-01-01

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

  8. Consultation system for image diagnosis: Report formation support system

    International Nuclear Information System (INIS)

    Ikeda, M.; Sakuma, S.; Ishigaki, T.; Suzuki, K.; Oikawa, K.

    1987-01-01

    The authors developed a consultation system for image diagnosis, involving artificial intelligence ideas. In this system, the authors proposed a new report formation support system and implemented it in lymphangiography. This support system starts with the input of image interpretation. The input process is made mainly by selecting items. This system encodes the input findings into the semantic network, which is represented as a directed graph, and it reserves them into the knowledge database in the above structure. Finally, the output (report) is made in the near natural language, which corresponds to the input findings

  9. Expert system for surveillance and diagnosis of breach fuel elements

    International Nuclear Information System (INIS)

    Gross, K.C.

    1989-01-01

    An apparatus and method are disclosed for surveillance and diagnosis of breached fuel elements in a nuclear reactor. A delayed neutron monitoring system provides output signals indicating the delayed neutron activity and age and the equivalent recoil areas of a breached fuel element. Sensors are used to provide outputs indicating the status of each component of the delayed neutron monitoring system. Detectors also generate output signals indicating the reactor power level and the primary coolant flow rate of the reactor. The outputs from the detectors and sensors are interfaced with an artificial intelligence-based knowledge system which implements predetermined logic and generates output signals indicating the operability of the reactor

  10. Expert system for surveillance and diagnosis of breach fuel elements

    Science.gov (United States)

    Gross, K.C.

    1988-01-21

    An apparatus and method are disclosed for surveillance and diagnosis of breached fuel elements in a nuclear reactor. A delayed neutron monitoring system provides output signals indicating the delayed neutron activity and age and the equivalent recoil area of a breached fuel element. Sensors are used to provide outputs indicating the status of each component of the delayed neutron monitoring system. Detectors also generate output signals indicating the reactor power level and the primary coolant flow rate of the reactor. The outputs from the detectors and sensors are interfaced with an artificial intelligence-based knowledge system which implements predetermined logic and generates output signals indicating the operability of the reactor. 2 figs.

  11. Expert system for surveillance and diagnosis of breach fuel elements

    Science.gov (United States)

    Gross, Kenny C.

    1989-01-01

    An apparatus and method are disclosed for surveillance and diagnosis of breached fuel elements in a nuclear reactor. A delayed neutron monitoring system provides output signals indicating the delayed neutron activity and age and the equivalent recoil areas of a breached fuel element. Sensors are used to provide outputs indicating the status of each component of the delayed neutron monitoring system. Detectors also generate output signals indicating the reactor power level and the primary coolant flow rate of the reactor. The outputs from the detectors and sensors are interfaced with an artificial intelligence-based knowledge system which implements predetermined logic and generates output signals indicating the operability of the reactor.

  12. Expert systems application to plant diagnosis and sensor data validation

    International Nuclear Information System (INIS)

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

    1986-01-01

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

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

    CERN Document Server

    Isermann, Rolf

    2017-01-01

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

  14. A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection

    Directory of Open Access Journals (Sweden)

    Zhiyong Pang

    2015-01-01

    Full Text Available This study established a fully automated computer-aided diagnosis (CAD system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI. A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL segmentation was included in the proposed CAD system. The Chan-Vese (CV model level set (LS segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

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

    DEFF Research Database (Denmark)

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

    Diagnosis and control modules based on fuzzy set theory were tested for novel bioreactor monitoring and control. Two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation...... in diagnosis and control allowed a more intuitive design of the membership functions and the production rules. Hence, the resulting diagnosis-control module is simple to tune, update and maintain while providing a good control performance. In particular the diagnosis-control system was designed for a complete...

  16. Active Fault Diagnosis in Sampled-data Systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2015-01-01

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

  17. Computer-aided diagnosis expert system for cerebrovascular diseases.

    Science.gov (United States)

    Chen, Xu; Wang, Zhijun; Sy, Chrisopher; Liu, Xiaokun; Qian, Jinwu; Zheng, Jia; Dong, Zhiqiang; Cao, Limei; Geng, Xiang; Xu, Shuye; Liu, Xueyuan

    2014-05-01

    To establish an expert diagnosis system for cerebrovascular diseases (CVDs) and assess accuracy of the diagnosis system. An expert diagnosis system for CVDs was established and evaluated using actual clinical cases. An expert diagnosis system for CVDs was established and tested in 319 clinical patients. Diagnosis accordance was obtained in 307 patients (the diagnosis accordance rate was 96.2%). Involved were 223, 7, 23, 54 and 12 patients with cerebral thrombosis, cerebral embolism, transient ischemic attack, cerebral hemorrhage and subarachnoid hemorrhage, respectively; and diagnosis accordance was obtained in 219 (98.2%), 6 (85.7%), 23 (100%), 48 (88.9%) and 11 (91.7%), respectively. Overall, the case analysis results support and demonstrate the diagnostic reasoning accuracy of the expert diagnosis system for CVDs. With the expert diagnosis system, medical experts' diagnosis of CVDs can be effectively mimicked and auxiliary diagnosis of CVDs has been preliminarily realized, laying a foundation for increasing the diagnostic accuracy of clinical diagnoses as it pertains to CVDs.

  18. Design for testability and diagnosis at the system-level

    Science.gov (United States)

    Simpson, William R.; Sheppard, John W.

    1993-01-01

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

  19. [Diagnosis and therapy of systemic necrotizing vasculitis].

    Science.gov (United States)

    Aksamija-Rizvić, B

    1992-01-01

    Through the presentation of the case of Leucocytoclastic vasculitis of a young man we enlighten the problems of diagnosis, differential diagnosis, ethiology, development of disease, and therapeutic approach. We presented the new knowledge in the patogenesis of changes in the vessel wall and possible correlation with thrombotic thrombocytopenic purpura.

  20. Fault Diagnosis and Fault Tolerant Control for Non-Gaussian Singular Time-Delayed Stochastic Distribution Systems with Disturbance Based on the Rational Square-Root Model

    Directory of Open Access Journals (Sweden)

    Yuancheng Sun

    2016-01-01

    Full Text Available For the non-Gaussian singular time-delayed stochastic distribution control (SDC system with unknown external disturbance where the output probability density function (PDF is approximated by the rational square-root B-spline basis function, a robust fault diagnosis and fault tolerant control algorithm is presented. A full-order observer is constructed to estimate the exogenous disturbance and an adaptive observer is used to estimate the fault size. A fault tolerant tracking controller is designed using the feedback of distribution tracking error, fault, and disturbance estimation to let the postfault output PDF still track desired distribution. Finally, a simulation example is included to illustrate the effectiveness of the proposed algorithms and encouraging results have been obtained.

  1. Intelligent System for Diagnosis of a Three-Phase Separator

    Directory of Open Access Journals (Sweden)

    Irina Ioniţă

    2016-03-01

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

  2. Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis

    Science.gov (United States)

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

    2012-01-01

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

  3. A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

    Directory of Open Access Journals (Sweden)

    Guojiang Xiong

    2013-01-01

    Full Text Available Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.

  4. Semi-automatic ROI placement system for analysis of brain PET images based on elastic model. Application to diagnosis of Alzheimer's disease

    International Nuclear Information System (INIS)

    Ohyama, Masashi; Mishina, Masahiro; Kitamura, Shin; Katayama, Yasuo; Senda, Michio; Tanizaki, Naoki; Ishii, Kenji

    2000-01-01

    PET with 18F-fluorodeoxyglucose (FDG) is a useful technique to image cerebral glucose metabolism and to detect patients with Alzheimer's disease in the early stage, in which characteristic temporoparietal hypometabolism is visualized. We have developed a new system, in which the standard brain ROI atlas made of networks of segments is elastically transformed to match the subject brain images, so that standard ROIs defined on the segments are placed on the individual brain images and are used to measure radioactivity over each brain region. We applied this methods to Alzheimer's disease. This method was applied to the images of 10 normal subjects (ages 55 +/- 12) and 21 patients clinically diagnosed as Alzheimer's disease (age 61 +/- 10). The FDG uptake reflecting glucose metabolism was evaluated with SUV, i.e. decay corrected radioactivity divided by injected dose per body weight in (Bq/ml)/(Bq/g). The system worked all right in every subject including those with extensive hypometabolism. Alzheimer patients showed markedly lower in the parietal cortex (4.0-4.1). When the threshold value of FDG uptake in the parietal lobe was set as 5 (Bq/ml)/(Bq/g), we could discriminate the patients with Alzheimer's disease from the normal subjects. The sensitivity was 86% and the specificity was 90%. This system can assist diagnosis of FDG images and may be useful for treating data of a large number of subjects; e.g. when PET is applied to health screening. (author)

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

    Directory of Open Access Journals (Sweden)

    Asaranti Kar

    2016-01-01

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

  6. Intelligent Mechatronic Systems Modeling, Control and Diagnosis

    CERN Document Server

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

    2013-01-01

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

  7. Diagnostic performance of a CT-based scoring system for diagnosis of anastomotic leakage after esophagectomy: comparison with subjective CT assessment

    Energy Technology Data Exchange (ETDEWEB)

    Goense, Lucas; Rossum, Peter S.N. van [University Medical Center Utrecht, Department of Surgery, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiation Oncology, Utrecht (Netherlands); Stassen, Pauline M.C.; Ruurda, Jelle P.; Hillegersberg, Richard van [University Medical Center Utrecht, Department of Surgery, Utrecht (Netherlands); Wessels, Frank J.; Leeuwen, Maarten S. van [University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands)

    2017-10-15

    To develop a CT-based prediction score for anastomotic leakage after esophagectomy and compare it to subjective CT interpretation. Consecutive patients who underwent a CT scan for a clinical suspicion of anastomotic leakage after esophagectomy with cervical anastomosis between 2003 and 2014 were analyzed. The CT scans were systematically re-evaluated by two radiologists for the presence of specific CT findings and presence of an anastomotic leak. Also, the original CT interpretations were acquired. These results were compared to patients with and without a clinical confirmed leak. Out of 122 patients that underwent CT for a clinical suspicion of anastomotic leakage; 54 had a confirmed leak. In multivariable analysis, anastomotic leakage was associated with mediastinal fluid (OR = 3.4), esophagogastric wall discontinuity (OR = 4.9), mediastinal air (OR = 6.6), and a fistula (OR = 7.2). Based on these criteria, a prediction score was developed resulting in an area-under-the-curve (AUC) of 0.86, sensitivity of 80%, and specificity of 84%. The original interpretation and the systematic subjective CT assessment by two radiologists resulted in AUCs of 0.68 and 0.75 with sensitivities of 52% and 69%, and specificities of 84% and 82%, respectively. This CT-based score may provide improved diagnostic performance for diagnosis of anastomotic leakage after esophagectomy. (orig.)

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

    NARCIS (Netherlands)

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

    2011-01-01

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

  9. Systems for early damage detection and on-line diagnosis

    International Nuclear Information System (INIS)

    Wach, D.; Dio, W.H.; Schuette, A.

    1987-01-01

    Advanced microelectronics and computer technology allow to implement powerful and cost-effective systems. Comprehensive expert knowledge (knowledge base) is available and has been implemented in part in in-situ systems. Further developments to comprehensive on-line diagnosis systems will enhance effectivity further and will be available to the reactor operator as an early-warning system. Optimum and reliable provision of information to assess safety-related reactor systems in one of the basic requirements for the operator to be able to correctly assess the situation at any point of time and to settle for the correct actions to be taken in particular in deviations/incidents. (orig./DG) [de

  10. Implementation of a model based fault detection and diagnosis technique for actuation faults of the SSME

    Science.gov (United States)

    Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

    1991-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the Space Shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the Space Shuttle Main Engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  11. Suggested guidelines for using systemic antimicrobials in bacterial skin infections: part 1—diagnosis based on clinical presentation, cytology and culture

    Science.gov (United States)

    Beco, L.; Guaguère, E.; Méndez, C. Lorente; Noli, C.; Nuttall, T.; Vroom, M.

    2013-01-01

    Systemic antimicrobials are critically important in veterinary healthcare, and resistance is a major concern. Antimicrobial stewardship will be important in maintaining clinical efficacy by reducing the development and spread of antimicrobial resistance. Bacterial skin infections are one of the most common reasons for using systemic antimicrobials in dogs and cats. Appropriate management of these infections is, therefore, crucial in any policy for responsible antimicrobial use. The goals of therapy are to confirm that an infection is present, identify the causative bacteria, select the most appropriate antimicrobial, ensure that the infection is treated correctly, and to identify and manage any underlying conditions. This is the first of two articles that will provide evidence-led guidelines to help practitioners address these issues. This article covers diagnosis, including descriptions of the different clinical presentations of surface, superficial and deep bacterial skin infections, how to perform and interpret cytology, and how to best use bacterial culture and sensitivity testing. Part 2 will discuss therapy, including choice of drug and treatment regimens. PMID:23292951

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

    Science.gov (United States)

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

    2007-10-01

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

  13. An approach for solving multi-level diagnosis in high sensitivity medical diagnosis systems through the application of semantic technologies.

    Science.gov (United States)

    Rodríguez-González, Alejandro; Alor-Hernández, Giner

    2013-01-01

    The capability of medical diagnosis systems to provide results in different situations depends on the modeling of the knowledge base. In the case of high sensitivity systems, the capability of having an adequate model allows to increase the accuracy of the system even in situations where the number of input elements is low. In this context the concept of multi-level diagnosis emerges, where a pathology can be assumed as a diagnostic element of another pathology (acting as a finding). In this paper this concept is studied in depth from the modeling point of view, providing a solution based on rule inference techniques modeled with semantic technologies, and allowing solving the problem generated by multi-level diagnosis. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Solar Dynamic Power System Fault Diagnosis

    Science.gov (United States)

    Momoh, James A.; Dias, Lakshman G.

    1996-01-01

    The objective of this research is to conduct various fault simulation studies for diagnosing the type and location of faults in the power distribution system. Different types of faults are simulated at different locations within the distribution system and the faulted waveforms are monitored at measurable nodes such as at the output of the DDCU's. These fault signatures are processed using feature extractors such as FFT and wavelet transforms. The extracted features are fed to a clustering based neural network for training and subsequent testing using previously unseen data. Different load models consisting of constant impedance and constant power are used for the loads. Open circuit faults and short circuit faults are studied. It is concluded from present studies that using features extracted from wavelet transforms give better success rates during ANN testing. The trained ANN's are capable of diagnosing fault types and approximate locations in the solar dynamic power distribution system.

  15. Fuzzy Expert System for Heart Attack Diagnosis

    Science.gov (United States)

    Hassan, Norlida; Arbaiy, Nureize; Shah, Noor Aziyan Ahmad; Afizah Afif@Afip, Zehan

    2017-08-01

    Heart attack is one of the serious illnesses and reported as the main killer disease. Early prevention is significant to reduce the risk of having the disease. The prevention efforts can be strengthen through awareness and education about risk factor and healthy lifestyle. Therefore the knowledge dissemination is needed to play role in order to distribute and educate public in health care management and disease prevention. Since the knowledge dissemination in medical is important, there is a need to develop a knowledge based system that can emulate human intelligence to assist decision making process. Thereby, this study utilized hybrid artificial intelligence (AI) techniques to develop a Fuzzy Expert System for Diagnosing Heart Attack Disease (HAD). This system integrates fuzzy logic with expert system, which helps the medical practitioner and people to predict the risk and as well as diagnosing heart attack based on given symptom. The development of HAD is expected not only providing expert knowledge but potentially become one of learning resources to help citizens to develop awareness about heart-healthy lifestyle.

  16. Expert systems for real-time monitoring and fault diagnosis

    Science.gov (United States)

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

    1989-01-01

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

  17. Semi-automatic ROI placement system for analysis of brain PET images based on elastic model. Application to diagnosis of Alzheimer's disease

    Energy Technology Data Exchange (ETDEWEB)

    Ohyama, Masashi; Mishina, Masahiro; Kitamura, Shin; Katayama, Yasuo [Nippon Medical School, Tokyo (Japan); Senda, Michio; Tanizaki, Naoki; Ishii, Kenji

    2000-02-01

    PET with 18F-fluorodeoxyglucose (FDG) is a useful technique to image cerebral glucose metabolism and to detect patients with Alzheimer's disease in the early stage, in which characteristic temporoparietal hypometabolism is visualized. We have developed a new system, in which the standard brain ROI atlas made of networks of segments is elastically transformed to match the subject brain images, so that standard ROIs defined on the segments are placed on the individual brain images and are used to measure radioactivity over each brain region. We applied this methods to Alzheimer's disease. This method was applied to the images of 10 normal subjects (ages 55 +/- 12) and 21 patients clinically diagnosed as Alzheimer's disease (age 61 +/- 10). The FDG uptake reflecting glucose metabolism was evaluated with SUV, i.e. decay corrected radioactivity divided by injected dose per body weight in (Bq/ml)/(Bq/g). The system worked all right in every subject including those with extensive hypometabolism. Alzheimer patients showed markedly lower in the parietal cortex (4.0-4.1). When the threshold value of FDG uptake in the parietal lobe was set as 5 (Bq/ml)/(Bq/g), we could discriminate the patients with Alzheimer's disease from the normal subjects. The sensitivity was 86% and the specificity was 90%. This system can assist diagnosis of FDG images and may be useful for treating data of a large number of subjects; e.g. when PET is applied to health screening. (author)

  18. Diagnosis for Control and Decision Support in Complex Systems

    DEFF Research Database (Denmark)

    Blanke, Mogens; Hansen, Søren; Blas, Morten Rufus

    2011-01-01

    Diagnosis and, when possible, prognosis of faults are essential for safe and reliable operation. The area of fault diagnosis has emerged over three decades. The majority of studies related to linear systems but real-life systems are complex and nonlinear. The development of methodologies coping w...

  19. Expert System Diagnosis of Cataract Eyes Using Fuzzy Mamdani Method

    Science.gov (United States)

    Santosa, I.; Romla, L.; Herawati, S.

    2018-01-01

    Cataracts are eye diseases characterized by cloudy or opacity of the lens of the eye by changing the colour of black into grey-white which slowly continues to grow and develop without feeling pain and pain that can cause blindness in human vision. Therefore, researchers make an expert system of cataract eye disease diagnosis by using Fuzzy Mamdani and how to care. The fuzzy method can convert the crisp value to linguistic value by fuzzification and includes in the rule. So this system produces an application program that can help the public in knowing cataract eye disease and how to care based on the symptoms suffered. From the results of the design implementation and testing of expert system applications to diagnose eye disease cataracts, it can be concluded that from a trial of 50 cases of data, obtained test results accuracy between system predictions with expert predictions obtained a value of 78% truth.

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

    CERN Document Server

    Shen, Qikun; Shi, Peng

    2017-01-01

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

  1. The lacrimal system: diagnosis, management, and surgery

    National Research Council Canada - National Science Library

    Cohen, Adam J; Mercandetti, Michael; Brazzo, Brian G

    2006-01-01

    ... and techniques presented represent the state of the art of lacrimal diagnosis and surgery. There is mention of lacrimal infection dating back to the Code of Hammurabi in 2250 BC, but it was not until the late 1800s that real progress began to be made. Toti, an ENT surgeon in Florence, Italy, described external dacryocystorhinostomy (DCR) with turb...

  2. Improving Diagnosability of Hybrid Systems through Active Diagnosis

    Data.gov (United States)

    National Aeronautics and Space Administration — Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete...

  3. Computer Aided Diagnosis System for Early Lung Cancer Detection

    Directory of Open Access Journals (Sweden)

    Fatma Taher

    2015-11-01

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

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

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  5. Active fault diagnosis in closed-loop systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2005-01-01

    Active fault diagnosis (AFD) of parametric faults is considered in connection with closed loop feedback systems. AFD involves auxiliary signals applied on the closed loop system. A fault signature matrix is introduced in connection with AFD and it is shown that if a limited number of faults can...... occur in the system, a fault separation in the fault signature matrix can be obtained. Then the single elements in the matrix only depend of a reduced number of parametric faults. This can directly be applied for fault isolation. If it is not possible to obtain this separation, it is shown how the fault...... signature matrix can be applied for a dynamical fault isolation, i.e. fault isolation based on the dynamic characteristic of the fault signature matrix as function of the different parametric faults....

  6. New ICPMS based strategies for clinical diagnosis

    International Nuclear Information System (INIS)

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

    2009-01-01

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

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

    International Nuclear Information System (INIS)

    Zhou Yong; Zhang Li

    2012-01-01

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

  8. Chip-Based Sensors for Disease Diagnosis

    Science.gov (United States)

    Fang, Zhichao

    Nucleic acid analysis is one of the most important disease diagnostic approaches in medical practice, and has been commonly used in cancer biomarker detection, bacterial speciation and many other fields in laboratory. Currently, the application of powerful research methods for genetic analysis, including the polymerase chain reaction (PCR), DNA sequencing, and gene expression profiling using fluorescence microarrays, are not widely used in hospitals and extended-care units due to high-cost, long detection times, and extensive sample preparation. Bioassays, especially chip-based electrochemical sensors, may be suitable for the next generation of rapid, sensitive, and multiplexed detection tools. Herein, we report three different microelectrode platforms with capabilities enabled by nano- and microtechnology: nanoelectrode ensembles (NEEs), nanostructured microelectrodes (NMEs), and hierarchical nanostructured microelectrodes (HNMEs), all of which are able to directly detect unpurified RNA in clinical samples without enzymatic amplification. Biomarkers that are cancer and infectious disease relevant to clinical medicine were chosen to be the targets. Markers were successfully detected with clinically-relevant sensitivity. Using peptide nucleic acids (PNAs) as probes and an electrocatalytic reporter system, NEEs were able to detect prostate cancer-related gene fusions in tumor tissue samples with 100 ng of RNA. The development of NMEs improved the sensitivity of the assay further to 10 aM of DNA target, and multiplexed detection of RNA sequences of different prostate cancer-related gene fusion types was achieved on the chip-based NMEs platform. An HNMEs chip integrated with a bacterial lysis device was able to detect as few as 25 cfu bacteria in 30 minutes and monitor the detection in real time. Bacterial detection could also be performed in neat urine samples. The development of these versatile clinical diagnostic tools could be extended to the detection of various

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

    Directory of Open Access Journals (Sweden)

    Vollmer Ekkehard

    2008-04-01

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

  10. Fuzzy Timing Petri Net for Fault Diagnosis in Power System

    Directory of Open Access Journals (Sweden)

    Alireza Tavakholi Ghainani

    2012-01-01

    Full Text Available A model-based system for fault diagnosis in power system is presented in this paper. It is based on fuzzy timing Petri net (FTPN. The ordinary Petri net (PN tool is used to model the protective components, relays, and circuit breakers. In addition, fuzzy timing is associated with places (token/transition to handle the uncertain information of relays and circuits breakers. The received delay time information of relays and breakers is mapped to fuzzy timestamps, π(τ, as initial marking of the backward FTPN. The diagnosis process starts by marking the backward sub-FTPNs. The final marking is found by going through the firing sequence, σ, of each sub-FTPN and updating fuzzy timestamp in each state of σ. The final marking indicates the estimated fault section. This information is then in turn used in forward FTPN to evaluate the fault hypothesis. The FTPN will increase the speed of the inference engine because of the ability of Petri net to describe parallel processing, and the use of time-tag data will cause the inference procedure to be more accurate.

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

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  12. CT diagnosis of congenital anomalies of the central nervous system

    International Nuclear Information System (INIS)

    Mori, Koreaki

    1980-01-01

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

  13. PSYCHOLOGICAL AND PEDAGOGICAL BASES FOR THE CREATION OF AN AUTOMATED SYSTEM FOR THE DIAGNOSIS OF LEVELS OF PERCEPTION CHANNELS DEVELOPMENT (PERCEPTION.GUIDE

    Directory of Open Access Journals (Sweden)

    Petro P. Vorobiyenko

    2017-12-01

    Full Text Available The article describes the results of research on the process of human perception of information, the definitions of the main channels of perception of the external world, and the diagnoses of the prevailing channel. The experience and practical achievements of scientists are analyzed in the context of psychology, pedagogy, socionics, neurolinguistic programming. Certain methods for diagnosing the dominant channel of human perception of information using electronic resources and software complexes are considered. An extended model of the process of human information perception is presented based on the three-element classification of personality types: visual, audial, kinesthetic. The main stages of the creation of an automated "perception.guide" system for diagnosing the leading channel of information perception with the purpose of choosing the most effective teaching methods using information and communication technologies are revealed. The advantages of using the system for educational purposes are determined.

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

    International Nuclear Information System (INIS)

    Lucas, B.

    1994-05-01

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

  15. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

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

  16. Systemic lupus erythematosus diagnosis and management.

    Science.gov (United States)

    Thong, Bernard; Olsen, Nancy J

    2017-04-01

    SLE presents many challenges for clinicians. The onset of disease may be insidious, with many different symptoms and signs, making early and accurate diagnosis challenging. Tests for SLE in the early stages lack specificity; those that are useful later often appear only after organ damage is manifest. Disease patterns are highly variable; flares are not predictable and not always associated with biomarkers. Children with SLE may have severe disease and present special management issues. Older SLE patients have complicating co-morbid conditions. Therapeutic interventions have improved over recent decades, but available drugs do not adequately control disease in many patients, and successful outcomes are limited by off-target effects; some of these become manifest with longer duration of treatment, now in part revealed by improved rates of survival. Despite all of these challenges, advances in understanding the biological basis of SLE have translated into more effective approaches to patient care. This review considers the current state of SLE diagnosis and management, with a focus on new approaches and anticipated advances. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  17. Neural Network Expert System in the Application of Tower Fault Diagnosis

    Science.gov (United States)

    Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian

    For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.

  18. Phronesis, a diagnosis and recovery tool for system administrators

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  19. Plant experience with an expert system for alarm diagnosis

    International Nuclear Information System (INIS)

    Gimmy, K.L.

    1986-01-01

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

  20. Automated system for periodontal disease diagnosis

    Science.gov (United States)

    Albalat, Salvador E.; Alcaniz-Raya, Mariano L.; Juan, M. Carmen; Grau Colomer, Vincente; Monserrat, Carlos

    1997-04-01

    Evolution of periodontal disease is one of the most important data for the clinicians in order to achieve correct planning and treatment. Clinical measure of the periodontal sulcus depth is the most important datum to know the exact state of periodontal disease. These measures must be done periodically study bone resorption evolution around teeth. Time factor of resorption indicates aggressiveness of periodontitis. Manual probes are commonly used with direct reading. Mechanical probes give automatic signal but this method uses complicated and heavy probes that are only limited for University researchers. Probe position must be the same to have right diagnosis. Digital image analysis of periodontal probing provides practical, accurate and easy tool. Gum and plaque index could also be digitally measured with this method.

  1. Biopsy Diagnosis of Oral Carcinoma by the Combination of Morphological and Spectral Methods Based on Embedded Relay Lens Microscopic Hyperspectral Imaging System.

    Science.gov (United States)

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

    Cytopathological examination through biopsy is very important for carcinoma detection. The embedded relay lens microscopic hyperspectral imaging system (ERL-MHIS) provides a morphological image of a biopsy sample and the spectrum of each pixel in the image simultaneously. Based on the ERL-MHIS, this work develops morphological and spectral methods to diagnose oral carcinoma biopsy. In morphological discrimination, the fractal dimension method is applied to differentiate between normal and abnormal tissues. In spectral identification, normal and cancerous cells are distinguished using five methods. However, the spectra of normal and cancerous cells vary with patient. The diagnostic performances of the five methods are thus not ideal. Hence, the proposed cocktail approach is used to determine the effectiveness of the spectral methods in correlating with the sampling conditions. And then we use a combination of effective spectral methods according to the sample conditions for diagnosing a sample. A total of 68 biopsies from 34 patients are analyzed using the ERL-MHIS. The results demonstrate a sensitivity of 90 ± 4.53 % and a specificity of 87.8 ± 5.21 %. Furthermore, in our survey, this system is the first time utilized to study oral carcinoma biopsies.

  2. A Research and Implementation of Internal Medicine Diagnosis Assisted by Intelligence Knowledge Base

    Directory of Open Access Journals (Sweden)

    Zhang Xiaohui

    2015-01-01

    Full Text Available Intelligent knowledge system is an important knowledge base for internal medicine diagnosis. Intelligent diagnosis of the knowledge base can be realized by establishing appropriate expert models to assist diagnosis and treatment. By building the hierarchical model of internal diseases, this paper established an internal medicine diagnostic system assisted by intelligence knowledge base with the mathematical model of analytic hierarchy. The hierarchical model is able to summarize characteristics of diseases and quantize the determinant criterion of diseases. The weighted value of a possible disease can be obtained through the judgment of physicians on the weight of factors of the criterion layer and the compared calculation of database. It is concluded that the analytic hierarchy model can realize the auxiliary diagnosis function of intelligence knowledge base and the weight of a disease providing diagnostic reference for physicians.

  3. Faults Diagnosis for Vibration Signal Based on HMM

    Directory of Open Access Journals (Sweden)

    Shao Qiang

    2014-02-01

    Full Text Available Faults behaviors of automotive engine in running-up stage are shown a multidimensional pattern that evolves as a function of time (called dynamic patterns. It is necessary to identify the type of fault during early running stages of automotive engine for the selection of appropriate operator actions to prevent a more severe situation. In this situation, the Faults diagnosis method based on continuous HMM is proposed. Feature vectors of main FFT spectrum component are extracted from vibration signals and looked up as observation vectors of HMM. Several HMMs which substitute the types of faults in automotive engine vibration system are modeled. Decision-making for faults classification is performed. The results of experiment are shown the proposed method is executable and effective.

  4. Thermoeconomic Diagnosis of an Energy System for Ship Propulsion

    DEFF Research Database (Denmark)

    Sigthorsson, Oskar; Elmegaard, Brian; Ommen, Torben Schmidt

    2013-01-01

    A thermoeconomic diagnosis of an energy system for ship propulsion is performed. We consider a Thermo Efficiency System (TES), for a Post-Panamax class ship where the waste heat from the main engine is utilised with a waste heat recovery system consisting of a power turbine expander and a single...... pressure level steam cycle. In complex energy systems, such as the TES, it may be difficult to identify operation anomalies as the effects of an intrinsic malfunction in one component spreads through the whole energy system and induces malfunctions in other components. Exergy and thermoeconomic analyses...... are used to investigate the system with the goal of more efficient use of energy resources and in a cost-effective manner. Moreover, the respective analyses identify the components that the thermoeconomic diagnosis is focused on. The thermoeconomic diagnosis is done with the characteristic curve method...

  5. Pulse to pulse klystron diagnosis system

    International Nuclear Information System (INIS)

    Nowak, J.; Davidson, V.; Genova, L.; Johnson, R.; Reagan, D.

    1981-03-01

    This report describes a system used to study the behavior of SLAC high powered klystrons operating with a twice normal pulse width of 5 μs. At present, up to eight of the klystrons installed along the accelerator can be operated with long pulses and monitored by this system. The report will also discuss some of the recent findings and investigations

  6. Multi-Level Models For Diagnosis Of Complex Electro-Mechanical Systems

    Science.gov (United States)

    Smith, John A.; Biswas, Gautam

    1989-03-01

    This paper discusses a knowledge-based system for diagnostic problem solving based on a multi-level representational structure and associated reasoning methods. The motivation behind this approach is to combine shallow evidential models for fault diagnosis with deep qualitative models that derive behavior from structural descriptions. In addition, the reasoning scheme utilizes historical data based on past experience for diagnosis. Using this integrated framework, we concentrate on the following issues: (i) Multi-level knowledge based system design, and (ii) Reasoning systems that exploit the multi-level representational structure for diagnostic problem solving. This system is applied to the diagnosis of a complex electro-mechanical system, specifically, the upper cargo door of the DC-10 aircraft in use at Federal Express Corporation.

  7. Probabilistic Fault Diagnosis in Electrical Power Systems

    Data.gov (United States)

    National Aeronautics and Space Administration — Electrical power systems play a critical role in spacecraft and aircraft. This paper discusses our development of a diagnostic capability for an electrical power...

  8. Disseminated paracoccidioidomycosis diagnosis based on oral lesions

    Directory of Open Access Journals (Sweden)

    Liana Preto Webber

    2014-01-01

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

  9. Role of fluorographic examinations in diagnosis of respiratory system diseases

    International Nuclear Information System (INIS)

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

    1984-01-01

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

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

    Science.gov (United States)

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

    2018-02-28

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

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

    Science.gov (United States)

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

    2017-06-08

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

  12. The Diagnostic Value of Skin Disease Diagnosis Expert System.

    Science.gov (United States)

    Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Arabkermany, Zahra; Gilasi, Hamidreza

    2016-02-01

    Evaluation is a necessary measure to ensure the effectiveness and efficiency of all systems, including expert systems. The aim of this study was to determine the diagnostic value of expert system for diagnosis of complex skin diseases. A case-control study was conducted in 2015 to determine the diagnostic value of an expert system. The study population included patients who were referred to Razi Specialized Hospital, affiliated to Tehran University of Medical Sciences. The control group was selected from patients without the selected skin diseases. Data collection tool was a checklist of clinical signs of diseases including pemphigus vulgaris, lichen planus, basal cell carcinoma, melanoma, and scabies. The sample size formula estimated 400 patients with skin diseases selected by experts and 200 patients without the selected skin diseases. Patient selection was undertaken with randomized stratified sampling and their sign and symptoms were logged into the system. Physician's diagnosis was determined as the gold standard and was compared with the diagnosis of expert system by SPSS software version 16 and STATA. Kappa statistics, indicators of sensitivity, specificity, accuracy and confidence intervals were calculated for each disease. An accuracy of 90% was considered appropriate. Comparing the results of expert system and physician's diagnosis at the evaluation stage showed an accuracy of 97.1%, sensitivity of 97.5% and specificity of 96.5% The Kappa test indicated a high agreement of 93.6%. The expert system can diagnose complex skin diseases. Development of such systems is recommended to identify all skin diseases.

  13. Hospitality unit diagnosis: an expert system approach

    OpenAIRE

    Balfe, Andrew J.

    1998-01-01

    Formal methods of management problem-solving have been extensively researched. However, these concepts are incomplete in that they assume a problem has been correctly identified before initiating the problem-solving process. In reality management may not realise that a problem exists or may identify an incorrect problem. As a result, considerable time and effort may be wasted correcting symptoms rather than the true problem. This research describes the development of a computerised system...

  14. Backward reachability of Colored Petri Nets for systems diagnosis

    International Nuclear Information System (INIS)

    Bouali, Mohamed; Barger, Pavol; Schon, Walter

    2012-01-01

    Embedded systems development creates a need of new design, verification and validation technics. Formal methods appear as a very interesting approach for embedded systems analysis, especially for dependability studies. The chosen formalism for this work is based on Colored Petri Net (CPN) for two main reasons: the expressivity and the formal nature. Also, they model easily the static and the dynamic natures of the studied systems. The main challenge of this work is to use existing models, which describe the system structure and/or behavior, to extract the dependability information in a most general case and failure diagnosis information in a particular case. The proposed approach is a CPN structural backward reachability analysis. It can be split into two parts. The first one is to perform the proposed analysis: inverse CPN. It is obtained thanks to structural transformations applied on the original CPN. The second part is the analysis implementation. This part needs some complementary concepts. Among them, the most important is the marking enhancement. The proposed approach is studied under two complementary aspects: algorithmic and theoretic aspects. The first one proposes transformations for the CPN inversion and the analysis implementation. The second aspect (the theoretical one) aims to offer a formal proof for the approach by applying two methods which are linear algebra and Linear Logic.

  15. Data-driven design of fault diagnosis systems nonlinear multimode processes

    CERN Document Server

    Haghani Abandan Sari, Adel

    2014-01-01

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

  16. Artificial intelligence tools decision support systems in condition monitoring and diagnosis

    CERN Document Server

    Galar Pascual, Diego

    2015-01-01

    Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource: Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniques Considers the merits of each technique as well as the issues associated with real-life application Covers classification methods, from neural networks to Bayesian and support vector machines Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes Provides data sets, sample signals, and MATLAB® code for algorithm testing Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis delivers a thorough evaluation of the latest AI tools for CM, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied.

  17. Machine Learning Based Diagnosis of Lithium Batteries

    Science.gov (United States)

    Ibe-Ekeocha, Chinemerem Christopher

    The depletion of the world's current petroleum reserve, coupled with the negative effects of carbon monoxide and other harmful petrochemical by-products on the environment, is the driving force behind the movement towards renewable and sustainable energy sources. Furthermore, the growing transportation sector consumes a significant portion of the total energy used in the United States. A complete electrification of this sector would require a significant development in electric vehicles (EVs) and hybrid electric vehicles (HEVs), thus translating to a reduction in the carbon footprint. As the market for EVs and HEVs grows, their battery management systems (BMS) need to be improved accordingly. The BMS is not only responsible for optimally charging and discharging the battery, but also monitoring battery's state of charge (SOC) and state of health (SOH). SOC, similar to an energy gauge, is a representation of a battery's remaining charge level as a percentage of its total possible charge at full capacity. Similarly, SOH is a measure of deterioration of a battery; thus it is a representation of the battery's age. Both SOC and SOH are not measurable, so it is important that these quantities are estimated accurately. An inaccurate estimation could not only be inconvenient for EV consumers, but also potentially detrimental to battery's performance and life. Such estimations could be implemented either online, while battery is in use, or offline when battery is at rest. This thesis presents intelligent online SOC and SOH estimation methods using machine learning tools such as artificial neural network (ANN). ANNs are a powerful generalization tool if programmed and trained effectively. Unlike other estimation strategies, the techniques used require no battery modeling or knowledge of battery internal parameters but rather uses battery's voltage, charge/discharge current, and ambient temperature measurements to accurately estimate battery's SOC and SOH. The developed

  18. The use of mathematical models for diagnosis of activated sludge systems in WWTP

    Science.gov (United States)

    Drewnowski, Jakub; Zmarzły, Marcin

    2017-11-01

    In this study diagnosis of activated sludge systems in wastewater treatment plant (WWTP) was investigated. Diagnosis of technical objects can be realized in many ways. One of the divisions of the diagnostic methods include modelling with or without a model of the object. The first of these is the analysis of the symptoms for which, based on the parameter values, the abnormality in the diagnosed objects are sought. Another way is to use models of objects undergoing diagnosis. In this case, the diagnosis comes down to a comparison of information from the response object model or the estimated parameters of the model with data from the real object. The aim of this study was to evaluate an innovative concept of the possible use the mathematical model and computer simulation in the diagnosis and control of activated sludge systems in WWTP.

  19. Lessons learned from implementing the HIV infant tracking system (HITSystem): A web-based intervention to improve early infant diagnosis in Kenya.

    Science.gov (United States)

    Finocchario-Kessler, S; Odera, I; Okoth, V; Bawcom, C; Gautney, B; Khamadi, S; Clark, K; Goggin, K

    2015-12-01

    Guided by the RE-AIM model, we describe preliminary data and lessons learned from multiple serial implementations of an eHealth intervention to improve early infant diagnosis (EID) of HIV in Kenya. We describe the reach, effectiveness, adoption, implementation and maintenance of the HITSystem, an eHealth intervention that links key stakeholders to improve retention and outcomes in EID. Our target community includes mother-infant pairs utilizing EID services and government health care providers and lab personnel. We also explore our own role as program and research personnel supporting the dissemination and scale up of the HITSystem in Kenya. Key findings illustrate the importance of continual adaptation of the HITSystem interface to accommodate varied stakeholders' workflows in different settings. Surprisingly, technology capacity and internet connectivity posed minimal short-term challenges. Early and sustained ownership of the HITSystem among stakeholders proved critical to reach, effectiveness and successful adoption, implementation and maintenance. Preliminary data support the ability of the HITSystem to improve EID outcomes in Kenya. Strong and sustained collaborations with stakeholders improve the quality and reach of eHealth public health interventions. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Water chemistry diagnosis system for nuclear power plants

    International Nuclear Information System (INIS)

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

    1990-01-01

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

  1. The Design and Implementation of a Remote Fault Reasoning Diagnosis System for Meteorological Satellites Data Acquisition

    Directory of Open Access Journals (Sweden)

    Zhu Jie

    2017-01-01

    Full Text Available Under the background of the trouble shooting requirements of FENGYUN-3 (FY-3 meteorological satellites data acquisition in domestic and oversea ground stations, a remote fault reasoning diagnosis system is developed by Java 1.6 in eclipse 3.6 platform. The general framework is analyzed, the workflow is introduced. Based on the system, it can realize the remote and centralized monitoring of equipment running status in ground stations,triggering automatic fault diagnosis and rule based fault reasoning by parsing the equipment quality logs, generating trouble tickets and importing expert experience database, providing text and graphics query methods. Through the practical verification, the system can assist knowledge engineers in remote precise and rapid fault location with friendly graphical user interface, boost the fault diagnosis efficiency, enhance the remote monitoring ability of integrity operating control system. The system has a certain practical significance to improve reliability of FY-3 meteorological satellites data acquisition.

  2. The diagnosis of skull base metastases by radionuclide bone scan.

    Science.gov (United States)

    Brillman, J; Valeriano, J; Adatepe, M H

    1987-06-01

    The differential diagnosis of multiple cranial nerve palsies in patients with cancer includes meningeal infections, meningeal carcinomatosis, and skull base metastases. In distinguishing these, spinal fluid analysis and skull base tomography should be helpful in most cases. In circumstances when results of skull base tomography are negative, radionuclide bone scans can demonstrate metastatic disease in the base of the skull, and it should be obtained in all patients who are highly suspicious for having skull base metastasis with negative skull base tomography, including computed tomography (CT).

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

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2007-01-01

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

  4. Model-Based Diagnosis in a Power Distribution Test-Bed

    Science.gov (United States)

    Scarl, E.; McCall, K.

    1998-01-01

    The Rodon model-based diagnosis shell was applied to a breadboard test-bed, modeling an automated power distribution system. The constraint-based modeling paradigm and diagnostic algorithm were found to adequately represent the selected set of test scenarios.

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

    International Nuclear Information System (INIS)

    Nicolini, C.

    1998-01-01

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

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

    CERN Multimedia

    Haen, C; Bonaccorsi, E; Neufeld, N

    2013-01-01

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

  7. A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

    OpenAIRE

    Xiong, Guojiang; Shi, Dongyuan; Zhu, Lin; Duan, Xianzhong

    2013-01-01

    Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems) are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusi...

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

    African Journals Online (AJOL)

    Abstract. This paper is a preview of the work so far concluded on Expert Systems implementation for the diagnosis of hepatitis B, which is one of the most common of all hepatitis ravaging mankind today. A user friendly application programme has been developed which can diagnose and prescribe solutions to the treatment ...

  9. [Imaging diagnosis of central nervous system involvement in panarteritis nodosa].

    Science.gov (United States)

    Wildhagen, K; Stoppe, G; Meyer, G J; Heintz, P; Hundeshagen, H; Deicher, H

    1989-01-01

    Central nervous system involvement of periarteritis nodosa is a rare complication of this disease. The diagnosis of CNS manifestation in vasculitis has been improved by using imaging techniques (i.e., magnetic resonance tomography, computed tomography, positron emission tomography). A case of polyarteritis nodosa with CNS involvement is presented; the diagnostic value of magnetic resonance imaging and positron emission tomography is discussed.

  10. Dynamic Observers for Fault Diagnosis of Timed Systems

    OpenAIRE

    Cassez, Franck

    2010-01-01

    In this paper we extend the work on \\emph{dynamic ob\\-servers} for fault diagnosis to timed automata. We study sensor minimization problems with static observers and then address the problem of computing the most permissive dynamic observer for a system given by a timed automaton.

  11. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

    Directory of Open Access Journals (Sweden)

    Xiang Wang

    2015-07-01

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

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

    Science.gov (United States)

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-18

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

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

    Science.gov (United States)

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

    2015-07-06

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

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

    Directory of Open Access Journals (Sweden)

    Hodjat Hamidi

    2017-02-01

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

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

    Data.gov (United States)

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

  16. Emergency diesel dynamic diagnosis system of nuclear power plant

    International Nuclear Information System (INIS)

    Bae, S. M.; Jung, H. S.; Kim, T. W.; Kim, K. J.; Choi, K. H.

    1999-01-01

    An emergency diesel generator of nuclear power plant should supply the rated power to safety class load within a limited time, if a station black out occurs. The emergency diesel generator must have higher reliability than any other industrial diesel generator due to nuclear safety. Most of the problems of the emergency diesel generator were trade off between stability and performance in control system. From the viewpoint of nuclear safety, the performance such as start time and load sequence time was more focused than stability. From the viewpoint of overall reliability, however, performance and stability of control system were equally important. The emergency diesel dynamic diagnosis system was developed in order to tune a dynamic control parameter optimally, verify a static engine parameter, and assist a human decision making. The emergency diesel dynamic diagnosis system really improved the reliability of the emergency diesel generator of the nuclear power plant

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

    Science.gov (United States)

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

    2010-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Jose M. Bernal-de-Lázaro

    2016-05-01

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

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

    International Nuclear Information System (INIS)

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

    1994-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Detang Zeng

    2018-01-01

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

  1. Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

    Science.gov (United States)

    Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

    1992-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  2. Logic-Dynamic Approach to Fault Diagnosis in Mechatronic Systems

    Directory of Open Access Journals (Sweden)

    V. F. Filaretov

    2006-12-01

    Full Text Available This paper presents a problem of fault detection and isolation (FDI in mechatronic systems described by nonlinear dynamic models with such types of no differentiable nonlinearities as saturation, Coulomb friction, backlash, and hysteresis. To solve this problem, so-called logic-dynamic approach is suggested. This approach consists of three main steps: replacing the initial nonlinear system by certain linear logic-dynamic system, obtaining the bank of linear logic-dynamic observers, and transforming these observes into the nonlinear ones. Logic-dynamic approach allows one to use the linear FDI methods for diagnosis in nonlinear mechatronic systems.

  3. The CDD system in computed tomographic diagnosis of diverticular disease

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  4. Saliency based ulcer detection for wireless capsule endoscopy diagnosis.

    Science.gov (United States)

    Yuan, Yixuan; Wang, Jiaole; Li, Baopu; Meng, Max Q-H

    2015-10-01

    Ulcer is one of the most common symptoms of many serious diseases in the human digestive tract. Especially for the ulcers in the small bowel where other procedures cannot adequately visualize, wireless capsule endoscopy (WCE) is increasingly being used in the diagnosis and clinical management. Because WCE generates large amount of images from the whole process of inspection, computer-aided detection of ulcer is considered an indispensable relief to clinicians. In this paper, a two-staged fully automated computer-aided detection system is proposed to detect ulcer from WCE images. In the first stage, we propose an effective saliency detection method based on multi-level superpixel representation to outline the ulcer candidates. To find the perceptually and semantically meaningful salient regions, we first segment the image into multi-level superpixel segmentations. Each level corresponds to different initial region sizes of the superpixels. Then we evaluate the corresponding saliency according to the color and texture features in superpixel region of each level. In the end, we fuse the saliency maps from all levels together to obtain the final saliency map. In the second stage, we apply the obtained saliency map to better encode the image features for the ulcer image recognition tasks. Because the ulcer mainly corresponds to the saliency region, we propose a saliency max-pooling method integrated with the Locality-constrained Linear Coding (LLC) method to characterize the images. Experiment results achieve promising 92.65% accuracy and 94.12% sensitivity, validating the effectiveness of the proposed method. Moreover, the comparison results show that our detection system outperforms the state-of-the-art methods on the ulcer classification task.

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

    Directory of Open Access Journals (Sweden)

    Kayser Gian

    2009-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Mingtao Ge

    2017-12-01

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

  7. Combining knowledge and historical data for system-level fault diagnosis of HVAC systems

    NARCIS (Netherlands)

    Verbert, K.A.J.; Babuska, R.; De Schutter, B.H.K.

    2017-01-01

    Interdependencies among system components and the existence of multiple operating modes present a challenge for fault diagnosis of Heating, Ventilation, and Air Conditioning (HVAC) systems. Reliable and timely diagnosis can only be ensured when it is performed in all operating modes, and at the

  8. Fault diagnosis of rolling bearing based on cyclic spectrum density

    International Nuclear Information System (INIS)

    Shi Qingfeng; Yan Junming; Zhang Yanhong

    2009-01-01

    The paper considered the vibration signals of rotating equipment as cyclo stationary signals through analyzing the features of this kind of signals. Based on the analytic method of cyclic spectrum density, the paper pointed out that the impact frequency could be extracted effectively with the help of scanning cyclic frequency domain. The validity of the method of cyclic spectrum density is proved by simulating signals and the method is applied to the diagnosis of rolling bearings. (authors)

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  10. Thorax X-ray diagnostics. DDS (double base description system). Uniform terminology and standardized sytematics. Correct diagnosis; Thorax Roentgendiagnostik. DDS-System*, Einheitliche Terminologie und Standardisierte Systematik {yields} Richtige Diagnose

    Energy Technology Data Exchange (ETDEWEB)

    Kulke, H.M. [Wuerzburg Univ. (Germany). Universitaetsklinikum

    2012-07-01

    The booklet describes the so called DDS (double-base description system) to be used in the frame of medical thorax X-ray examinations with modern imaging devices. The following issues are discussed: Description features, shadow characterization, general fundamentals, procedural methodology, diagnostic findings protocol, examples and case descriptions.

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

    OpenAIRE

    Bear, Robert A.

    1986-01-01

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

  12. The system of prenatal diagnosis of fetal harm

    OpenAIRE

    ŠUSTROVÁ, Tereza

    2014-01-01

    The bachelor's thesis deals with a topic: "The System of the prenatal diagnosis of fetal harm" and it's divided to two parts, the theoretical part and the research section. The theoretical part, which is the introductory part of the thesis, has three main chapters. The first chapter is focused on the prenatal care. This chapter describes the prenatal care in general, who provides that. It is dealt by pregnancy card, which is the essential component of the prenatal care and frequency of checks...

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

    International Nuclear Information System (INIS)

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

    1996-01-01

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

  14. Diagnosis by integrating model-based reasoning with knowledge-based reasoning

    Science.gov (United States)

    Bylander, Tom

    1988-01-01

    Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.

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

    Directory of Open Access Journals (Sweden)

    Zhheng Ni

    2016-01-01

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

  16. Assessment of an outreach street-based HIV rapid testing programme as a strategy to promote early diagnosis: a comparison with two surveillance systems in Spain, 2008-2011.

    Science.gov (United States)

    Belza, M J; Hoyos, J; Fernández-Balbuena, S; Diaz, A; Bravo, M J; de la Fuente, L

    2015-04-09

    We assess the added value of a multisite, street-based HIV rapid testing programme by comparing its results to pre-existing services and assessing its potential to reduce ongoing transmission. Between 2008 and 2011, 8,923 individuals underwent testing. We compare outcomes with those of a network of 20 sexually transmitted infections (STI)/HIV clinics (EPI-VIH) and the Spanish National HIV Surveillance System (SNHSS); evaluate whether good visibility prompts testing and assess whether it reaches under-tested populations. 89.2% of the new infections were in men who have sex with men (MSM) vs 78.0% in EPI-VIH and 56.0% in SNHSS. 83.6% of the MSM were linked to care and 20.9% had VIH. 56.5% of the HIV-positive MSM tested because they happened to see the programme, 18.4% were previously untested and 26.3% had their last test ≥2 years ago. The programme provided linkage to care and early diagnosis mainly to MSM but attendees presented a lower HIV prevalence than EPI-VIH. From a cost perspective it would benefit from being implemented in locations highly frequented by MSM. Conversely, its good visibility led to reduced periods of undiagnosed infection in a high proportion of MSM who were not testing with the recommended frequency.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  18. Combining Model-Based and Feature-Driven Diagnosis Approaches – A Case Study on Electromechanical Actuators

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this...

  19. Diagnostic performance and system delay using telemedicine for prehospital diagnosis in triaging and teatment of STEMI

    DEFF Research Database (Denmark)

    Rasmussen, Martin Bøhme; Frost, Lars; Stengaard, Carsten

    2014-01-01

    percutaneous coronary intervention (PPCI)). There are limited data on achievable system delays in an optimal STEMI system of care using prehospital diagnosis to triage patients with STEMI directly to percutaneous coronary intervention (PCI) centres. We examined the proportion of tentative prehospital STEMI...... diagnoses established by telemedicine confirmed on hospital arrival, and we determined system delay in patients diagnosed before hospital arrival and triaged directly to the catheterisation laboratory. Methods: Design: Population-based follow-up study. Setting: Central Denmark Region. Participants: 15 992...... patients diagnosed using telemedicine. Results: During the study period, a tentative diagnosis of STEMI was established in 1061 patients, of whom 919 were triaged directly to the PCI centre. In 771 (84%) patients, a diagnosis of STEMI was confirmed. Patients transported

  20. Robust fault diagnosis of physical systems in operation. Ph.D. Thesis - Rutgers - The State Univ.

    Science.gov (United States)

    Abbott, Kathy Hamilton

    1991-01-01

    Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative diagnosis. The first idea is that graceful degradation can be viewed as reasoning at higher levels of abstraction whenever the more detailed levels proved to be incomplete or inadequate. A form of abstraction is defined that applies this view to the problem of diagnosis. In this form of abstraction, named status abstraction, two levels are defined. The lower level of abstraction corresponds to the level of detail at which most current knowledge-based diagnosis systems reason. At the higher level, a graph representation is presented that describes the real-world physical system. An incremental, constructive approach to manipulating this graph representation is demonstrated that supports certain characteristics of operative diagnosis. The suitability of this constructive approach is shown for diagnosing fault propagation behavior over time, and for sometimes diagnosing systems with feedback. A way is shown to represent different semantics in the same type of graph representation to characterize different types of fault propagation behavior. An approach is demonstrated that threats these different behaviors as different fault classes, and the approach moves to other classes when previous classes fail to generate suitable hypotheses. These ideas are implemented in a computer program named Draphys (Diagnostic Reasoning About Physical Systems) and demonstrated for the domain of inflight aircraft subsystems, specifically a propulsion system (containing two turbofan systems and a fuel system) and hydraulic subsystem.

  1. Flow cytometry-based diagnosis of primary immunodeficiency diseases.

    Science.gov (United States)

    Kanegane, Hirokazu; Hoshino, Akihiro; Okano, Tsubasa; Yasumi, Takahiro; Wada, Taizo; Takada, Hidetoshi; Okada, Satoshi; Yamashita, Motoi; Yeh, Tzu-Wen; Nishikomori, Ryuta; Takagi, Masatoshi; Imai, Kohsuke; Ochs, Hans D; Morio, Tomohiro

    2018-01-01

    Primary immunodeficiencies (PIDs) are a heterogeneous group of inherited diseases of the immune system. The definite diagnosis of PID is ascertained by genetic analysis; however, this takes time and is costly. Flow cytometry provides a rapid and highly sensitive tool for diagnosis of PIDs. Flow cytometry can evaluate specific cell populations and subpopulations, cell surface, intracellular and intranuclear proteins, biologic effects associated with specific immune defects, and certain functional immune characteristics, each being useful for the diagnosis and evaluation of PIDs. Flow cytometry effectively identifies major forms of PIDs, including severe combined immunodeficiency, X-linked agammaglobulinemia, hyper IgM syndromes, Wiskott-Aldrich syndrome, X-linked lymphoproliferative syndrome, familial hemophagocytic lymphohistiocytosis, autoimmune lymphoproliferative syndrome, IPEX syndrome, CTLA 4 haploinsufficiency and LRBA deficiency, IRAK4 and MyD88 deficiencies, Mendelian susceptibility to mycobacterial disease, chronic mucocuneous candidiasis, and chronic granulomatous disease. While genetic analysis is the definitive approach to establish specific diagnoses of PIDs, flow cytometry provides a tool to effectively evaluate patients with PIDs at relatively low cost. Copyright © 2017 Japanese Society of Allergology. Production and hosting by Elsevier B.V. All rights reserved.

  2. Design-based approach to ethics in computer-aided diagnosis

    Science.gov (United States)

    Collmann, Jeff R.; Lin, Jyh-Shyan; Freedman, Matthew T.; Wu, Chris Y.; Hayes, Wendelin S.; Mun, Seong K.

    1996-04-01

    A design-based approach to ethical analysis examines how computer scientists, physicians and patients make and justify choices in designing, using and reacting to computer-aided diagnosis (CADx) systems. The basic hypothesis of this research is that values are embedded in CADx systems during all phases of their development, not just retrospectively imposed on them. This paper concentrates on the work of computer scientists and physicians as they attempt to resolve central technical questions in designing clinically functional CADx systems for lung cancer and breast cancer diagnosis. The work of Lo, Chan, Freedman, Lin, Wu and their colleagues provides the initial data on which this study is based. As these researchers seek to increase the rate of true positive classifications of detected abnormalities in chest radiographs and mammograms, they explore dimensions of the fundamental ethical principal of beneficence. The training of CADx systems demonstrates the key ethical dilemmas inherent in their current design.

  3. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy.

    Science.gov (United States)

    Firmino, Macedo; Angelo, Giovani; Morais, Higor; Dantas, Marcel R; Valentim, Ricardo

    2016-01-06

    CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings. The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives. The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant. From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and

  4. Diagnosis and office-based treatment of urinary incontinence in adults. Part one: diagnosis and testing.

    Science.gov (United States)

    Cameron, Anne P; Heidelbaugh, Joel J; Jimbo, Masahito

    2013-08-01

    Urinary incontinence is a common problem in both men and women. This review article addresses its prevalence, risk factors, cost, the various types of incontinence, as well as how to diagnose them. The US Preventive Services Task Force, the Cochrane Database of Systematic Reviews, and PubMed were reviewed for articles focusing on urinary incontinence. Incontinence is a common problem with a high societal cost. It is frequently underreported by patients so it is appropriate for primary-care providers to screen all women and older men during visits. A thorough history and physical examination combined with easy office-based tests can often yield a clear diagnosis and rule out other transient illnesses contributing to the incontinence. Specialist referral is occasionally needed in specific situations before embarking on a treatment plan.

  5. A neuro-fuzzy decision support system for the diagnosis of heart failure.

    Science.gov (United States)

    Akinyokun, Charles O; Obot, Okure U; Uzoka, Faith-Michael E; Andy, John J

    2010-01-01

    A neuro-fuzzy decision support system is proposed for the diagnosis of heart failure. The system comprises; knowledge base (database, neural networks and fuzzy logic) of both the quantitative and qualitative knowledge of the diagnosis of heart failure, neuro-fuzzy inference engine and decision support engine. The neural networks employ a multi-layers perception back propagation learning process while the fuzzy logic uses the root sum square inference procedure. The neuro-fuzzy inference engine uses a weighted average of the premise and consequent parameters with the fuzzy rules serving as the nodes and the fuzzy sets representing the weights of the nodes. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. An experimental study of the decision support system was carried out using cases of some patients from three hospitals in Nigeria with the assistance of their medical personnel who collected patients' data over a period of six months. The results of the study show that the neuro-fuzzy system provides a highly reliable diagnosis, while the emotional and cognitive filters further refine the diagnosis results by taking care of the contextual elements of medical diagnosis.

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

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

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

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

    KAUST Repository

    Benzineb, Omar

    2013-01-01

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

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

    International Nuclear Information System (INIS)

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

    2001-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2001-08-01

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

  10. An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

    Data.gov (United States)

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

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

    International Nuclear Information System (INIS)

    Kim, Dae Sik

    1993-02-01

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

  12. A distributed fault-detection and diagnosis system using on-line parameter estimation

    Science.gov (United States)

    Guo, T.-H.; Merrill, W.; Duyar, A.

    1991-01-01

    The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.

  13. Expert system application to fault diagnosis and procedure synthesis

    International Nuclear Information System (INIS)

    Hajek, B.K.; Hashemi, S.; Bhatnagar, R.; Miller, D.W.; Stasenko, J.

    1987-01-01

    Two knowledge based systems have been developed to detect plant faults, to validate sensor data in a nuclear power plant, and to synthesize procedures to assure safety goals are met when a transient occurs. These two systems are being combined into a single system through a Plant Status Monitoring System (PSMS) and a common data base accessed by all the components of the integrated system. The system is designed to sit on top of an existing Safety Parameter Display System (SPDS), and to use the existing data acquisition and data control software of the SPDS. The integrated system will communicate with the SPDS software through a single database. This database will receive sensor values and equipment status indications in a form acceptable to the knowledge based system and according to an update plan designed specifically for the system

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

    Directory of Open Access Journals (Sweden)

    Yujie Cheng

    2017-05-01

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

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

    Science.gov (United States)

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

    2017-05-25

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

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

    Science.gov (United States)

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

    2014-09-01

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

  17. ADWICE - Advanced Diagnosis and Warning system for aircraft ICing Environments

    Science.gov (United States)

    Leifeld, C.; Hauf, T.; Tafferner, A.; Leykauf, H.

    2003-04-01

    Inflight icing is a serious hazard, as attested by recent crashes of aircraft. The number of world-wide known accidents and serious incidents in which icing played a major role exceeds 800. Obviously current protection systems and icing forecasting, the latter relying mostly on reported icing by pilots and the evaluation of radiosonde ascents, are inadequate to control the threat. Aircraft inflight icing occurs when areas of supercooled liquid cloud droplets or precipitation are traversed. Ice accumulation on aerodynamic surfaces causes modification of the aerodynamics of the aircraft up to the point of uncontrolled flight. The safest way and the recommended practise would be to avoid the icing conditions. This however requires the forecast of supercooled liquid water (SLWC) in clouds and complete ice microphysics model scheme. Since the forecast quality of SLWC still is insufficient to completely rely on that quality for forecasting aircraft icing, other methods are under development. They rely on algorithms which deduce the potential icing threat from measured (mainly radiosonde ascents) or forecast (numerical models) distributions of temperature and humidity. ADWICE, the Advanced Diagnosis and Warning System for aircraft ICing Environments, has been developed since 1998 in a joint cooperation between the Institut für Physik der Atmosphäre at DLR, the Deutscher Wetterdienst (DWD) and the Institut für Meteorologie und Klimatologie (IMUK) at the University of Hannover. To identify icing environments, ADWICE merges forecast model data of the Local Model of the DWD with SYNOP and radar data. Using a slightly modified version of the NCAR/RAP algorithm, which is based on temperature and humidity fields, a first guess icing volume is calculated. Under certain conditions radar and SYNOP data allow corrections of the icing volume. Other data e.g. from satellites may be used in future, too. Since January 2001 ADWICE is running in a testing phase at the DWD. Using PIREPs

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

    DEFF Research Database (Denmark)

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

    1995-01-01

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

  19. An evaluation of a real-time fault diagnosis expert system for aircraft applications

    Science.gov (United States)

    Schutte, Paul C.; Abbott, Kathy H.; Palmer, Michael T.; Ricks, Wendell R.

    1987-01-01

    A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

  20. Review on the current trends in tongue diagnosis systems.

    Science.gov (United States)

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

    2012-12-01

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

  1. RNA Systems Biology for Cancer: From Diagnosis to Therapy.

    Science.gov (United States)

    Amirkhah, Raheleh; Farazmand, Ali; Wolkenhauer, Olaf; Schmitz, Ulf

    2016-01-01

    It is due to the advances in high-throughput omics data generation that RNA species have re-entered the focus of biomedical research. International collaborate efforts, like the ENCODE and GENCODE projects, have spawned thousands of previously unknown functional non-coding RNAs (ncRNAs) with various but primarily regulatory roles. Many of these are linked to the emergence and progression of human diseases. In particular, interdisciplinary studies integrating bioinformatics, systems biology, and biotechnological approaches have successfully characterized the role of ncRNAs in different human cancers. These efforts led to the identification of a new tool-kit for cancer diagnosis, monitoring, and treatment, which is now starting to enter and impact on clinical practice. This chapter is to elaborate on the state of the art in RNA systems biology, including a review and perspective on clinical applications toward an integrative RNA systems medicine approach. The focus is on the role of ncRNAs in cancer.

  2. Utility of hand-held devices in diagnosis and triage of cardiovascular emergencies. Observations during implementation of a PACS-based system in an acute aortic syndrome (AAS) network.

    Science.gov (United States)

    Matar, Ralph; Renapurkar, Rahul; Obuchowski, Nancy; Menon, Venu; Piraino, David; Schoenhagen, Paul

    2015-01-01

    Prompt diagnosis and early referral to specialized centers is critical for patients presenting with cardiovascular emergencies, including acute aortic syndromes (AAS). Prior data has suggested that mobile access to imaging studies with hand-held devices can accelerate diagnosis and management. We conducted a study to determine the diagnostic accuracy of a hand-held device compared to conventional dedicated work-stations for diagnosing a spectrum of cardiovascular emergencies, predominantly acute aortic pathology. This study included 104 cases who underwent computed tomography (CT)-scan during "on-call'' hours between January, 2013 and August, 2014 for suspected AAS. Assessment was performed on a hand-held device independently by two readers using an iPhone5 connected via secure connection to web-based PACS servers. The subsequent interpretation from a dedicated workstation coupled with the diagnosis at the time of discharge was used as the reference standard for determining the presence or absence of an acute abnormality. Sensitivity and Specificity were calculated on a per patient basis. Readers' sensitivity and specificity using the hand-held device to diagnose acute chest pathology were calculated. Hand-held device evaluation was determined to have a sensitivity of 85.2% and a specificity of 98.6% by reader A and a sensitivity of 96.3% and specificity of 100% by reader B. Of 103 cases interpreted by both readers, the readers agreed about the diagnosis in 98 cases (95.1%). This study demonstrates that hand-held devices can be a potential useful tool to assist in diagnosis and triage of patients presenting with cardiovascular emergencies. Further studies are needed to assess the impact of screen size and resolution. Copyright © 2015 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

  3. Application of a diagnosis-based clinical decision guide in patients with neck pain

    OpenAIRE

    Murphy, Donald R; Hurwitz, Eric L

    2011-01-01

    Abstract Background Neck pain (NP) is a common cause of disability. Accurate and efficacious methods of diagnosis and treatment have been elusive. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based guide in applying the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to ...

  4. Application of a diagnosis-based clinical decision guide in patients with low back pain

    OpenAIRE

    Murphy, Donald R; Hurwitz, Eric L

    2011-01-01

    Abstract Background Low back pain (LBP) is common and costly. Development of accurate and efficacious methods of diagnosis and treatment has been identified as a research priority. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based means to apply the biopsychosocial model of care. The approach is based on three questions of diagnosis. T...

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

    Directory of Open Access Journals (Sweden)

    Pelin GORGEL

    2013-01-01

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

  6. Residual-based model diagnosis methods for mixture cure models.

    Science.gov (United States)

    Peng, Yingwei; Taylor, Jeremy M G

    2017-06-01

    Model diagnosis, an important issue in statistical modeling, has not yet been addressed adequately for cure models. We focus on mixture cure models in this work and propose some residual-based methods to examine the fit of the mixture cure model, particularly the fit of the latency part of the mixture cure model. The new methods extend the classical residual-based methods to the mixture cure model. Numerical work shows that the proposed methods are capable of detecting lack-of-fit of a mixture cure model, particularly in the latency part, such as outliers, improper covariate functional form, or nonproportionality in hazards if the proportional hazards assumption is employed in the latency part. The methods are illustrated with two real data sets that were previously analyzed with mixture cure models. © 2016, The International Biometric Society.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-02-15

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

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  9. Interactive tele-radiological segmentation systems for treatment and diagnosis.

    Science.gov (United States)

    Zimeras, S; Gortzis, L G

    2012-01-01

    Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

  10. Interactive Tele-Radiological Segmentation Systems for Treatment and Diagnosis

    Directory of Open Access Journals (Sweden)

    S. Zimeras

    2012-01-01

    Full Text Available Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor’s opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools, manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

  11. Tuberculosis diagnosis support analysis for precarious health information systems.

    Science.gov (United States)

    Orjuela-Cañón, Alvaro David; Camargo Mendoza, Jorge Eliécer; Awad García, Carlos Enrique; Vergara Vela, Erika Paola

    2018-04-01

    Pulmonary tuberculosis is a world emergency for the World Health Organization. Techniques and new diagnosis tools are important to battle this bacterial infection. There have been many advances in all those fields, but in developing countries such as Colombia, where the resources and infrastructure are limited, new fast and less expensive strategies are increasingly needed. Artificial neural networks are computational intelligence techniques that can be used in this kind of problems and offer additional support in the tuberculosis diagnosis process, providing a tool to medical staff to make decisions about management of subjects under suspicious of tuberculosis. A database extracted from 105 subjects with precarious information of people under suspect of pulmonary tuberculosis was used in this study. Data extracted from sex, age, diabetes, homeless, AIDS status and a variable with clinical knowledge from the medical personnel were used. Models based on artificial neural networks were used, exploring supervised learning to detect the disease. Unsupervised learning was used to create three risk groups based on available information. Obtained results are comparable with traditional techniques for detection of tuberculosis, showing advantages such as fast and low implementation costs. Sensitivity of 97% and specificity of 71% where achieved. Used techniques allowed to obtain valuable information that can be useful for physicians who treat the disease in decision making processes, especially under limited infrastructure and data. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

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

  15. A new remote-imaging diagnosis system at Komazawa University

    International Nuclear Information System (INIS)

    Shimada, Morio; Kohda, Eiichi; Yoshikawa, Kohki

    2007-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Zhonghai MA

    2018-02-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

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

    Science.gov (United States)

    Szmyrka, Magdalena

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

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

    Directory of Open Access Journals (Sweden)

    Yasuo Yamashita

    2009-07-01

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

  20. A new expert system for diagnosis of lung cancer: GDA-LS_SVM.

    Science.gov (United States)

    Avci, Engin

    2012-06-01

    In nowadays, there are many various diseases, whose diagnosis is very hardly. Lung cancer is one of this type diseases. It begins in the lungs and spreads to other organs of human body. In this paper, an expert diagnostic system based on General Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM) Classifier for diagnosis of lung cancer. This expert diagnosis system is called as GDA-LS-SVM in rest of this paper. The GDA-LS-SVM expert diagnosis system has two stages. These are 1. Feature extraction and feature reduction stage and 2. Classification stage. In feature extraction and feature reduction stage, lung cancer dataset is obtained and dimension of this lung cancer dataset, which has 57 features, is reduced to eight features using Generalized Discriminant Analysis (GDA) method. Then, in classification stage, these reduced features are given to Least Squares Support Vector Machine (LS-SVM) classifier. The lung cancer dataset used in this study was taken from the UCI machine learning database. The classification accuracy of this GDA-LS-SVM expert system was obtained about 96.875% from results of these experimental studies.

  1. Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system.

    Science.gov (United States)

    Li, Lin; Zhang, Qizhi; Ding, Yihua; Jiang, Huabei; Thiers, Bruce H; Wang, James Z

    2014-10-13

    Early and accurate diagnosis of melanoma, the deadliest type of skin cancer, has the potential to reduce morbidity and mortality rate. However, early diagnosis of melanoma is not trivial even for experienced dermatologists, as it needs sampling and laboratory tests which can be extremely complex and subjective. The accuracy of clinical diagnosis of melanoma is also an issue especially in distinguishing between melanoma and mole. To solve these problems, this paper presents an approach that makes non-subjective judgements based on quantitative measures for automatic diagnosis of melanoma. Our approach involves image acquisition, image processing, feature extraction, and classification. 187 images (19 malignant melanoma and 168 benign lesions) were collected in a clinic by a spectroscopic device that combines single-scattered, polarized light spectroscopy with multiple-scattered, un-polarized light spectroscopy. After noise reduction and image normalization, features were extracted based on statistical measurements (i.e. mean, standard deviation, mean absolute deviation, L1 norm, and L2 norm) of image pixel intensities to characterize the pattern of melanoma. Finally, these features were fed into certain classifiers to train learning models for classification. We adopted three classifiers - artificial neural network, naïve bayes, and k-nearest neighbour to evaluate our approach separately. The naive bayes classifier achieved the best performance - 89% accuracy, 89% sensitivity and 89% specificity, which was integrated with our approach in a desktop application running on the spectroscopic system for diagnosis of melanoma. Our work has two strengths. (1) We have used single scattered polarized light spectroscopy and multiple scattered unpolarized light spectroscopy to decipher the multilayered characteristics of human skin. (2) Our approach does not need image segmentation, as we directly probe tiny spots in the lesion skin and the image scans do not involve

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

    Science.gov (United States)

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

    2010-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Fohona S. Coulibaly

    2017-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

    Parkash, Om; Hanim Shueb, Rafidah

    2015-01-01

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

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

    Science.gov (United States)

    Parkash, Om; Shueb, Rafidah Hanim

    2015-10-19

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

  7. Fault diagnosis system for tapped power transmission lines

    Energy Technology Data Exchange (ETDEWEB)

    Mohamed, E.A.; Talaat, H.A. [Elect. Power and Machines Dept., Ain Shams Univ., Cairo (Egypt); Khamis, E.A. [EEA, Nasr City, Cairo (Egypt)

    2010-05-15

    This paper presents a design for a fault diagnosis system (FDS) for tapped HV/EHV power transmission lines. These lines have two different protection zones. The proposed approach reduces the cost and the complexity of the FDS for these types of lines. The FDS consists basically of fifteen artificial neural networks (ANNs). The FDS basic objectives are mainly: (1) the detection of the system fault; (2) the localization of the faulted zone; (3) the classification of the fault type; and finally (4) the identification of the faulted phase. This FDS is structured in a three hierarchical levels. In the first level, a preprocessing unit to the input data is performed. An ANN, in the second level, is designed in order to detect and zone localize the line faults. In the third level, two zone diagnosis systems (ZDS) are designed. Each ZDS is dedicated to one zone and consists of seven parallel-cascaded ANN's. Four-parallel ANN's are designed in order to achieve the fault type classification. While, the other three cascaded ANN's are designed mainly for the selection of the faulted phase. A smoothing unit is also configured to smooth out the output response of the proposed FDS. The proposed FDS is designed and evaluated using the local measurements of the three-phase voltage and current samples acquired at only one side. The sampling rate was taken 16 samples per cycle of the power frequency. Data window of 4 samples was utilized. These samples were generated using the EMTP simulation program, applied to the High-Dam/Cairo 500 kV tapped transmission line. All possible shunt fault types were considered. The effect of fault location and fault incipience time were also included. Moreover, the effect of load and capacitor switchings on the FDS performance was investigated. Testing results have proved the capability as well as the effectiveness of the proposed FDS. (author)

  8. Diagnosis - Using automatic test equipment and artificial intelligence expert systems

    Science.gov (United States)

    Ramsey, J. E., Jr.

    Three expert systems (ATEOPS, ATEFEXPERS, and ATEFATLAS), which were created to direct automatic test equipment (ATE), are reviewed. The purpose of the project was to develop an expert system to troubleshoot the converter-programmer power supply card for the F-15 aircraft and have that expert system direct the automatic test equipment. Each expert system uses a different knowledge base or inference engine, basing the testing on the circuit schematic, test requirements document, or ATLAS code. Implementing generalized modules allows the expert systems to be used for any different unit under test. Using converted ATLAS to LISP code allows the expert system to direct any ATE using ATLAS. The constraint propagated frame system allows for the expansion of control by creating the ATLAS code, checking the code for good software engineering techniques, directing the ATE, and changing the test sequence as needed (planning).

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2018-02-05

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

  12. Saliency-Based Bleeding Localization for Wireless Capsule Endoscopy Diagnosis

    Directory of Open Access Journals (Sweden)

    Hongda Chen

    2017-01-01

    Full Text Available Stomach bleeding is a kind of gastrointestinal disease which can be diagnosed noninvasively by wireless capsule endoscopy (WCE. However, it requires much time for physicians to scan large amount of WCE images. Alternatively, computer-assisted bleeding localization systems are developed where color, edge, and intensity features are defined to distinguish lesions from normal tissues. This paper proposes a saliency-based localization system where three saliency maps are computed: phase congruency-based edge saliency map derived from Log-Gabor filter bands, intensity histogram-guided intensity saliency map, and red proportion-based saliency map. Fusing the three maps together, the proposed system can detect bleeding regions by thresholding the fused saliency map. Results demonstrate the accuracy of 98.97% for our system to mark bleeding regions.

  13. Fault Diagnosis for Electrical Distribution Systems using Structural Analysis

    DEFF Research Database (Denmark)

    Knüppel, Thyge; Blanke, Mogens; Østergaard, Jacob

    2014-01-01

    Fault-tolerance in electrical distribution relies on the ability to diagnose possible faults and determine which components or units cause a problem or are close to doing so. Faults include defects in instrumentation, power generation, transformation and transmission. The focus of this paper...... is the design of efficient diagnostic algorithms, which is a prerequisite for fault-tolerant control of power distribution. Diagnosis in a grid depend on available analytic redundancies, and hence on network topology. When topology changes, due to earlier fault(s) or caused by maintenance, analytic redundancy...... analysis of power systems, it demonstrates detection and isolation of failures in a network, and shows how typical faults are diagnosed. Nonlinear fault simulations illustrate the results....

  14. Dawn of the digital diagnosis assisting system, can it open a new age for pathology?

    Science.gov (United States)

    Saito, Akira; Cosatto, Eric; Kiyuna, Tomoharu; Sakamoto, Michiie

    2013-03-01

    Digital pathology is developing based on the improvement and popularization of WSI (whole slide imaging) scanners. WSI scanners are widely expected to be used as the next generation microscope for diagnosis; however, their usage is currently mostly limited to education and archiving. Indeed, there are still many hindrances in using WSI scanners for diagnosis (not research purpose), two of the main reasons being the perceived high cost and small gain in productivity obtained by switching from the microscope to a WSI system and the lack of WSI standardization. We believe that a key factor for advancing digital pathology is the creation of computer assisted diagnosis systems (CAD). Such systems require high-resolution digitization of slides and provide a clear added value to the often costly conversion to WSI. We (NEC Corporation) are creating a CAD system, named e-Pathologist ®. This system is currently used at independent pathology labs for quality control (QC/QA), double-checking pathologists diagnosis and preventing missed cancers. At the end of 2012, about 80,000 slides, 200,000 tissues of gastric and colorectal samples will have been analyzed by e-Pathologist ®. Through the development of e-Pathologist ®, it has become clear that a computer program should be inspired by the pathologist diagnosis process, yet it should not be a mere copy or simulation of it. Indeed pathologists often approach the diagnosis of slides in a "holistic" manner, examining them at various magnifications, panning and zooming in a seemingly haphazard way that they often have a hard time to precisely describe. Hence there has been no clear recipe emerging from numerous interviews with pathologists on how to exactly computer code a diagnosis expert system. Instead, we focused on extracting a small set of histopathological features that were consistently indicated as important by the pathologists and then let the computer figure out how to interpret in a quantitative way the presence or

  15. A belief network approach for development of a nuclear power plant diagnosis system

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, I. K.; Kim, J. T.; Lee, D. Y.; Jung, C. H.; Kim, J. Y.; Lee, J. S.; Ham, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

  16. A Self-Health Monitoring System for a Wireless Sensor Network Used in Bridge Diagnosis

    Science.gov (United States)

    Xiao, Haitao; Li, Tansheng; Ogai, Harutoshi

    For bridge diagnosis, the authors developed a wireless sensor network (WSN) to measure and gather the vibration data of bridges. In previous bridge diagnosis experiments, node failure and data packet loss occurred in the WSN, which caused some corruption in the collected data and hence the WSN could not be used to analyze the health status of the bridge. Furthermore, it was always difficult to determine the location of the nodes in order to ensure the link quality, when all the nodes of the WSN deployed for the first time. In this paper, a self-health monitoring system called distributed localized decision monitoring system (DLDMS) is presented to monitor the health of the WSN. Key features of the system include high detection accuracy, high responsiveness, and low energy consumption. Experimental data is given based on experiments at Kitakyushu in Japan.

  17. Skin-deep diagnosis: affective bias and zebra retreat complicating the diagnosis of systemic sclerosis.

    Science.gov (United States)

    Miller, Chad S

    2013-01-01

    Nearly half of medical errors can be attributed to an error of clinical reasoning or decision making. It is estimated that the correct diagnosis is missed or delayed in between 5% and 14% of acute hospital admissions. Through understanding why and how physicians make these errors, it is hoped that strategies can be developed to decrease the number of these errors. In the present case, a patient presented with dyspnea, gastrointestinal symptoms and weight loss; the diagnosis was initially missed when the treating physicians took mental short cuts and used heuristics as in this case. Heuristics have an inherent bias that can lead to faulty reasoning or conclusions, especially in complex or difficult cases. Affective bias, which is the overinvolvement of emotion in clinical decision making, limited the available information for diagnosis because of the hesitancy to acquire a full history and perform a complete physical examination in this patient. Zebra retreat, another type of bias, is when a rare diagnosis figures prominently on the differential diagnosis but the physician retreats for various reasons. Zebra retreat also factored in the delayed diagnosis. Through the description of these clinical reasoning errors in an actual case, it is hoped that future errors can be prevented or inspiration for additional research in this area will develop.

  18. Genetic Programming for the Generation of Crisp and Fuzzy Rule Bases in Classification and Diagnosis of Medical Data

    DEFF Research Database (Denmark)

    Dounias, George; Tsakonas, Athanasios; Jantzen, Jan

    2002-01-01

    programming system for the generation of fuzzy rule-based systems. Two different medical domains are used to evaluate the models. The first field is the diagnosis of subtypes of Aphasia. Two models for crisp rule-bases are presented. The first one discriminates between four major types and the second attempts...... systems. Comparisons on the system's comprehensibility and the transparency are included. These comparisons include for the Aphasia domain, previous work consisted of two neural network models....

  19. Diagnosis and Remediation in the Context of Intelligent Tutoring Systems.

    Science.gov (United States)

    Sleeman, D.; And Others

    This paper provides an overview of the four major aspects of the PIXIE Intelligent Tutoring System: the field work undertaken to determine how teachers diagnose and remediate in introductory algebra; the set of experiments run to determine the relative effectiveness of Model-Based-Remediation (MBR) and Reteaching; systems work carried out to…

  20. Embarked diagnosis applied to a mechanical system "diesel engine ...

    African Journals Online (AJOL)

    The implementation of OBD (on-board diagnostic) systems for diesel engines has become an unavoidable necessity. From the models described in the literature, the latest generation diesel engine models as well as defects affecting it were established. A board diagnostic system based on the use of fuzzy pattern ...

  1. A Systematic Review of Clinical Diagnostic Systems Used in the Diagnosis of Tuberculosis in Children

    Directory of Open Access Journals (Sweden)

    Emily C. Pearce

    2012-01-01

    Full Text Available Background. Tuberculosis (TB is difficult to diagnose in children due to lack of a gold standard, especially in resource-limited settings. Scoring systems and diagnostic criteria are often used to assist in diagnosis; however their validity, especially in areas with high HIV prevalence, remains unclear. Methods. We searched online bibliographic databases, including MEDLINE and EMBASE. We selected all studies involving scoring systems or diagnostic criteria used to aid in the diagnosis of tuberculosis in children and extracted data from these studies. Results. The search yielded 2261 titles, of which 40 met selection criteria. Eighteen studies used point-based scoring systems. Eighteen studies used diagnostic criteria. Validation of these scoring systems yielded varying sensitivities as gold standards used ranged widely. Four studies evaluated and compared multiple scoring criteria. Ten studies selected for pulmonary tuberculosis. Five studies specifically evaluated the use of scoring systems in HIV-positive children, generally finding the specificity to be lower. Conclusions. Though scoring systems and diagnostic criteria remain widely used in the diagnosis of tuberculosis in children, validation has been difficult due to lack of an established and accessible gold standard. Estimates of sensitivity and specificity vary widely, especially in populations with high HIV co-infection.

  2. Blood-based protein biomarkers for diagnosis of Alzheimer disease.

    Science.gov (United States)

    Doecke, James D; Laws, Simon M; Faux, Noel G; Wilson, William; Burnham, Samantha C; Lam, Chiou-Peng; Mondal, Alinda; Bedo, Justin; Bush, Ashley I; Brown, Belinda; De Ruyck, Karl; Ellis, Kathryn A; Fowler, Christopher; Gupta, Veer B; Head, Richard; Macaulay, S Lance; Pertile, Kelly; Rowe, Christopher C; Rembach, Alan; Rodrigues, Mark; Rumble, Rebecca; Szoeke, Cassandra; Taddei, Kevin; Taddei, Tania; Trounson, Brett; Ames, David; Masters, Colin L; Martins, Ralph N

    2012-10-01

    To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD). Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data. General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve. A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve. This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis.

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

    Science.gov (United States)

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

    2018-03-01

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

  4. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    Science.gov (United States)

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging

    Directory of Open Access Journals (Sweden)

    Hongyoon Choi

    2017-01-01

    Full Text Available Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD, which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD, an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies.

  6. Expert System For Diagnosis Pest And Disease In Fruit Plants

    Science.gov (United States)

    Dewanto, Satrio; Lukas, Jonathan

    2014-03-01

    This paper discussed the development of an expert system to diagnose pests and diseases on fruit plants. Rule base method was used to store the knowledge from experts and literatures. Control technique using backward chain and started from the symptoms to get conclusions about the pests and diseases that occur. Development of the system has been performed using software Corvid Exsys developed by Exsys company. Results showed that the development of this expert system can be used to assist users in identifying the type of pests and diseases on fruit plants. Further development and possibility of using internet for this system are proposed.

  7. Fault trees for diagnosis of system fault conditions

    International Nuclear Information System (INIS)

    Lambert, H.E.; Yadigaroglu, G.

    1977-01-01

    Methods for generating repair checklists on the basis of fault tree logic and probabilistic importance are presented. A one-step-ahead optimization procedure, based on the concept of component criticality, minimizing the expected time to diagnose system failure is outlined. Options available to the operator of a nuclear power plant when system fault conditions occur are addressed. A low-pressure emergency core cooling injection system, a standby safeguard system of a pressurized water reactor power plant, is chosen as an example illustrating the methods presented

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

    International Nuclear Information System (INIS)

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

    2005-01-01

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

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

    Science.gov (United States)

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

    2005-09-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-09-07

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

  11. Diagnosis and treatment of melanoma. European consensus-based interdisciplinary guideline - Update 2016

    DEFF Research Database (Denmark)

    Garbe, Claus; Peris, Ketty; Hauschild, Axel

    2016-01-01

    and Treatment of Cancer was formed to make recommendations on CM diagnosis and treatment, based on systematic literature reviews and the experts' experience. Diagnosis is made clinically using dermoscopy and staging is based upon the AJCC system. CMs are excised with 1-2 cm safety margins. Sentinel lymph node...... dissection is routinely offered as a staging procedure in patients with tumours >1 mm in thickness, although there is as yet no clear survival benefit for this approach. Interferon-α treatment may be offered to patients with stage II and III melanoma as an adjuvant therapy, as this treatment increases....... For first-line treatment particularly in BRAF wild-type patients, immunotherapy with PD-1 antibodies alone or in combination with CTLA-4 antibodies should be considered. BRAF inhibitors like dabrafenib and vemurafenib in combination with the MEK inhibitors trametinib and cobimetinib for BRAF mutated...

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

    Science.gov (United States)

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

    2014-12-01

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

  13. NATO Symposium on Human Detection and Diagnosis of System Failures

    CERN Document Server

    Rouse, William

    1981-01-01

    This book includes all of the papers presented at the NATO Symposium on Human Detection and Diagnosis of System Failures held at Roskilde, Denmark on August 4-8, 1980. The Symposium was sponsored by the Scientific Affairs Division of NATO and the Rise National Laboratory of Denmark. The goal of the Symposium was to continue the tradition initiated by the NATO Symposium on Monitoring Behavior and Supervisory Control held in Berchtesgaden, F .R. Germany in 1976 and the NATO Symposium on Theory and Measurement of Mental Workload held in Mati, Greece in 1977. To this end, a group of 85 psychologists and engineers coming from industry, government, and academia convened to discuss, and to generate a "state-of-the-art" consensus of the problems and solutions associated with the human IS ability to cope with the increasing scale of consequences of failures within complex technical systems. The Introduction of this volume reviews their findings. The Symposium was organized to include brief formal presentations of pape...

  14. The lung in systemic vasculitis: radiological patterns and differential diagnosis

    Science.gov (United States)

    Mantini, Cesare; Sperandeo, Marco; Galluzzo, Michele; Belcaro, Giovanni; Tartaro, Armando; Cotroneo, Antonio R

    2016-01-01

    The respiratory system may be involved in all systemic vasculitides, although with a variable frequency. The aim of our review is to describe radiographic and high-resolution CT (HRCT) findings of pulmonary vasculitides and to correlate radiological findings with pathological results. Lung disease is a common feature of antineutrophil cytoplasmic autoantibody-associated small-vessel vasculitides, including granulomatosis with polyangiitis (Wegener's), eosinophilic granulomatosis with polyangiitis (Churg–Strauss) and microscopic polyangiitis. Pulmonary involvement is less frequent in immune-complex-mediated small-vessel vasculitides, such as Behçet's disease and Goodpasture's syndrome. Pulmonary involvement associated to large-vessel (gigantocellular arteritis and Takayasu's disease) or medium-vessel (nodose polyarteritis and Kawasaki's disease) vasculitides is extremely rare. The present review describes the main clinical and radiological features of pulmonary vasculitides with major purpose to correlate HRCT findings (solitary or multiple nodules, cavitary lesions, micronodules with centrilobular or peribronchial distribution, airspace consolidations, “crazy paving”, tracheobronchial involvement, interstitial disease) with pathological results paying particular attention to the description of acute life-threatening manifestations. A thorough medical history, careful clinical examination and the knowledge of radiological patterns are mandatory for a correct and early diagnosis. PMID:26876879

  15. Reasoning based in cases applied to diagnosis of electric generators; Razonamiento basado en casos aplicado al diagnostico de generadores electricos

    Energy Technology Data Exchange (ETDEWEB)

    De la Torre Vega, H. Octavio; Garcia Tevillo, Arturo; Campuzano Martinez, Roberto [Instituto de Investigaciones Electricas, Temixco, Morelos (Mexico); Lopez Azamar, Ernesto [Comision Federal de Electricidad (Mexico)

    2000-07-01

    The development of a system for the diagnosis of electrical generators that apply techniques of artificial intelligence, is presented, as it is the reasoning based on cases, to support the work of the diagnosis engineer. This system is part of a system called CADIS, dedicated to the diagnosis of electrical generators out of line and reason of previous articles. In this occasion the characteristics of the reasoning module based on experiences (SirBE) are emphasized, indicating how to make a diagnosis using similar cases and how to edit the system base of experience, using the interactive editor of cases. It is included, in addition, a summarized example which represents a case for SirBE and how the system helps to make a diagnosis. [Spanish] Se presenta el desarrollo de un sistema de diagnostico de generadores electricos que aplica tecnicas de inteligencia artificial, como es el razonamiento basado en casos, para apoyar la labor del ingeniero de diagnostico. Este sistema es parte de un sistema denominado CADIS, dedicado al diagnostico de generadores electricos fuera de linea y motivo de articulos anteriores. En esta ocasion se resaltan las caracteristicas del modulo de razonamiento basado en experiencias (SirBE), indicando como realizar un diagnostico utilizando casos similares y como editar la base de experiencia del sistema utilizando el editor interactivo de casos. Se incluye, ademas, un ejemplo resumido de lo que representa un caso para SiRBE y como el sistema ayuda a realizar un diagnostico.

  16. Reasoning about fault diagnosis for the space station common module thermal control system

    Science.gov (United States)

    Vachtsevanos, G.; Hexmoor, H.; Purves, B.

    1988-01-01

    The proposed common module thermal control system for the Space Station is designed to integrate thermal distribution and thermal control functions in order to transport heat and provide environmental temperature control through the common module. When the thermal system is operating in an off-normal state, due to component faults, an intelligent controller is called upon to diagnose the fault type, identify the fault location and determine the appropriate control action required to isolate the faulty component. A methodology is introduced for fault diagnosis based upon a combination of signal redundancy techniques and fuzzy logic. An expert system utilizes parity space representation and analytic redundancy to derive fault symptoms, the aggregate of which is assessed by a multivalued rule based system. A subscale laboratory model of the thermal control system designed is used as the testbed for the study.

  17. Demonstration of a Client/Server System for Remote Diagnosis of Cardiac Arrhythmias

    Science.gov (United States)

    Tong, David A.; Gajjala, Vijay; Widman, Lawrence E.

    1995-01-01

    Health care practitioners are often faced with the task of interpreting complex heart rhythms from electrocardiograms (ECGs) produced by 12-lead ECG machines, ambulatory (Holter) monitoring systems, and intensive-care unit monitors. Usually, the practitioner caring for the patient does not have specialized training in cardiology or in ECG interpretation; and commercial programs that interpret 12-lead ECGs have been well-documented in the medical literature to perform poorly at analyzing cardiac rhythm. We believe that a system capable of providing comprehensive ECG interpretation as well as access to online consultations will be beneficial to the health care system. We present a client-server based telemedicine system capable of providing access to (1) an on-line knowledge-based system for remote diagnosis of cardiac arrhythmias and (2) an on-line cardiologist for real-time interactive consultation using readily available resources on the Internet.

  18. Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

    Science.gov (United States)

    Rodríguez-González, Alejandro; Torres-Niño, Javier; Mayer, Miguel A.; Alor-Hernandez, Giner; Wilkinson, Mark D.

    2012-01-01

    Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts. PMID:23320043

  19. Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

    Directory of Open Access Journals (Sweden)

    Alejandro Rodríguez-González

    2012-01-01

    Full Text Available Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs or medical diagnosis systems (MDSs. An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC. Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.

  20. Expert system for the diagnosis of the condition and performance of centrifugal pumps

    Energy Technology Data Exchange (ETDEWEB)

    Jantunen, E.; Vaehae-Pietilae, K.; Pesonen, K. [Technical Research Centre of Finland, Manufacturing Technology, Espoo (Finland)

    1997-12-31

    A brief description of the results of a study concerning the maintenance and downtime costs in Finnish pumping is given. The leakage of seals was found to be the fault that causes the highest downtime and maintenance costs. A small laboratory arrangement has been used to test the effectiveness of various condition monitoring methods. This information has been used in the development of a diagnostic expert system called CEPDIA, which can be used for diagnosing the condition of a pump and its components. The diagnosis is based on measuring results obtained from sensors and on information about maintenance actions carried out with the pump and its components. The principles of the CEPDIA expert system are described. A database is included in the system for handling and saving the measurement results, technical information on the pumps and maintenance actions carried out with the pumps. The diagnosis can also be based on vibration signature analysis, which is quite effective in determining which fault is the actual cause of malfunction of the pump or its components. CEPDIA can also be used to calculate of the efficiency of the electrical motor and the pump. CEPDIA has been tested in the diagnosis of 63 pumps. The average efficiency in pumping was less than 40 %, and more than 10 % of the pumps were pumping with less than 10 % efficiency. (orig.) 11 refs.

  1. Recent advances in biosensor based diagnosis of urinary tract infection.

    Science.gov (United States)

    Kumar, M S; Ghosh, S; Nayak, S; Das, A P

    2016-06-15

    Urinary tract infections (UTIs) are potentially life threatening infections that are associated with high rates of incidence, recurrence and mortality. UTIs are characterized by several chronic infections which may lead to lethal consequences if left undiagnosed and untreated. The uropathogens are consistent across the globe. The most prevalent uropathogenic gram negative bacteria are Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa, Klebsiella pneumonia. Early detection and precise diagnosis of these infections will play a pivotal role in health care, pharmacological and biomedical sectors. A number of detection methods are available but their performances are not upto the mark. Therefore a more rapid, selective and highly sensitive technique for the detection and quantification of uropathogen levels in extremely minute concentrations need of the time. This review brings all the major concerns of UTI at one's doorstep such as clinical costs and incidence rate, several diagnostic approaches along with their advantages and disadvantages. Paying attention to detection approaches with emphasizing biosensor based recent developments in the quest for new diagnostics for UTI and the need for more sophisticated techniques in terms of selectivity and sensitivity is discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Laboratory diagnosis of toluene-based inhalants abuse.

    Science.gov (United States)

    Thiesen, Flavia Valladão; Noto, Ana Regina; Barros, Helena M T

    2007-01-01

    Toluene is the main substance contained in products used as inhalants. The frequent abuse of toluene-based inhalants requires the definition of a simple laboratory parameter that allows acute exposure assessment. This study aimed at defining urinary hippuric acid (UHA) levels related to intentional exposure to toluene, and to correlate them to blood toluene concentration (BT). BT and UHA levels were measured in 65 homeless adolescents of Porto Alegre, Brazil. Toluene was detected in 91.9% of the investigated population, who presented BT levels from 0.5 to 83.7 microg/mL. There was good correlation between UHA and BT concentrations (r = 0.78), and in homeless adolescents, UHA levels higher than 3.0 g/g creatinine indicate intentional exposure to toluene. The determination of UHA concentrations can be used as a screening method for the detection of intentional exposure to toluene, but its diagnosis must include BT toluene dosage, as well as circumstantial and clinical evidence.

  3. Medical Diagnosis Using Distance-Based Similarity Measures of Single Valued Neutrosophic Multisets

    Directory of Open Access Journals (Sweden)

    Shan Ye

    2015-01-01

    Full Text Available This paper proposes a generalized distance measure and its similarity measures between single valued neutrosophic multisets (SVNMs. Then, the similarity measures are applied to a medical diagnosis problem with incomplete, indeterminate and inconsistent information. This diagnosis method can deal with the diagnosis problem with indeterminate and inconsistent information which cannot be handled by the diagnosis method based on intuitionistic fuzzy multisets (IFMs.

  4. Diseño basado en diagnóstico de fallos y sistemas híbridos aplicado en un equipo de desfibrilación ventricular Design based on fault diagnosis and hybrid systems applied to a ventricular defibrillator device

    Directory of Open Access Journals (Sweden)

    Alberto Prieto Moreno

    2012-04-01

    Full Text Available En este artículo se presenta una propuesta de procedimiento que incorpora el diagnóstico de fallos desde la fase de diseño de un equipo de desfibrilación ventricular. Lo anterior permite resolver un grupo de limitaciones que están presentes actualmente en el diseño de sistemas electrónicos. El procedimiento propuesto utiliza el concepto de diseño basado en diagnóstico, la técnica de composición de autómatas híbridos para el modelado y diagnóstico basado en el conocimiento de los expertos. Finalmente se diseña el sistema con el diagnosticador ya incorporado. El procedimiento utilizado puede ser extendido a otros tipos de sistemas.This article presents a proposal of procedure to incorporate the fault diagnosis from the design phase of ventricular defibrillation equipment. This solves a set of constraints that are currently present in the design of electronic systems. The proposed procedure uses the concept of diagnosis-based design, the technique of composition of hybrid automata modeling and diagnosis based on expert knowledge. Finally the system is designed with the fault diagnostic system incorporated. The procedure can be extended to other types of systems.

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

    Science.gov (United States)

    Nguchu, Benedictor A.; Li, Li

    2017-07-01

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

  6. Evaluating a decision making system for cardiovascular dysautonomias diagnosis.

    Science.gov (United States)

    Idri, Ali; Kadi, Ilham

    2016-01-01

    Autonomic nervous system (ANS) is the part of the nervous system that is involved in homeostasis of the whole body functions. A malfunction in this system can lead to a cardiovascular dysautonomias. Hence, a set of dynamic tests are adopted in ANS units to diagnose and treat patients with cardiovascular dysautonomias. The purpose of this study is to develop and evaluate a decision tree based cardiovascular dysautonomias prediction system on a dataset collected from the ANS unit of the Moroccan university hospital Avicenne. We collected a dataset of 263 records from the ANS unit of the Avicenne hospital. This dataset was split into three subsets: training set (123 records), test set (55 records) and validation set (85 records). C4.5 decision tree algorithm was used in this study to develop the prediction system. Moreover, Java Enterprise Edition platform was used to implement a prototype of the developed system which was deployed in the Avicenne ANS unit so as to be clinically validated. The performance of the decision tree-based prediction system was evaluated by means of the error rate criterion. The error rates were measured for each classifier and have achieved an average value of 1.46, 2.24 and 0.89 % in training, test, and validation sets respectively. The results obtained were encouraging but further replicated studies are still needed to be performed in order to confirm the findings of this study.

  7. An intelligent tutoring system for space shuttle diagnosis

    Science.gov (United States)

    Johnson, William B.; Norton, Jeffrey E.; Duncan, Phillip C.

    1988-01-01

    An Intelligent Tutoring System (ITS) transcends conventional computer-based instruction. An ITS is capable of monitoring and understanding student performance thereby providing feedback, explanation, and remediation. This is accomplished by including models of the student, the instructor, and the expert technician or operator in the domain of interest. The space shuttle fuel cell is the technical domain for the project described below. One system, Microcomputer Intelligence for Technical Training (MITT), demonstrates that ITS's can be developed and delivered, with a reasonable amount of effort and in a short period of time, on a microcomputer. The MITT system capitalizes on the diagnostic training approach called Framework for Aiding the Understanding of Logical Troubleshooting (FAULT) (Johnson, 1987). The system's embedded procedural expert was developed with NASA's C-Language Integrated Production (CLIP) expert system shell (Cubert, 1987).

  8. Review of Diagnosis-Related Group-Based Financing of Hospital Care

    Directory of Open Access Journals (Sweden)

    Natasa Mihailovic

    2016-05-01

    Full Text Available Since the 1990s, diagnosis-related group (DRG-based payment systems were gradually introduced in many countries. The main design characteristics of a DRG-based payment system are an exhaustive patient case classification system (ie, the system of diagnosis-related groupings and the payment formula, which is based on the base rate multiplied by a relative cost weight specific for each DRG. Cases within the same DRG code group are expected to undergo similar clinical evolution. Consecutively, they should incur the costs of diagnostics and treatment within a predefined scale. Such predictability was proven in a number of cost-of-illness studies conducted on major prosperity diseases alongside clinical trials on efficiency. This was the case with risky pregnancies, chronic obstructive pulmonary disease, diabetes, depression, alcohol addiction, hepatitis, and cancer. This article presents experience of introduced DRG-based payments in countries of western and eastern Europe, Scandinavia, United States, Canada, and Australia. This article presents the results of few selected reviews and systematic reviews of the following evidence: published reports on health system reforms by World Health Organization, World Bank, Organization for Economic Co-operation and Development, Canadian Institute for Health Information, Canadian Health Services Research Foundation, and Centre for Health Economics University of York. Diverse payment systems have different strengths and weaknesses in relation to the various objectives. The advantages of the DRG payment system are reflected in the increased efficiency and transparency and reduced average length of stay. The disadvantage of DRG is creating financial incentives toward earlier hospital discharges. Occasionally, such polices are not in full accordance with the clinical benefit priorities.

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

    Directory of Open Access Journals (Sweden)

    Shu-zhi Gao

    2013-01-01

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

  10. Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

    Directory of Open Access Journals (Sweden)

    Changwen Zheng

    2017-11-01

    Full Text Available The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries.

  11. Numerical model for thermoeconomic diagnosis in commercial transcritical/subcritical booster refrigeration systems

    DEFF Research Database (Denmark)

    Ommen, Torben; Elmegaard, Brian

    2012-01-01

    cycle supplying refrigerant for evaporators in both chilled and frozen display cases. In the paper, thermoeconomic theory is used to establish the cost of cooling at each individual temperature level based on operating costs.With a high amount of operating systems, faulty operation becomes an economic......, and environmental, interest. A general solution for evaluation of these systems is considered, with the objective to reduce cost and power consumption of malfunctioning equipment in operation. An analysis of the use of thermoeconomic diagnosis methods is required, as these methods may prove applicable...

  12. A novel model-based diagnosis engine: theory and applications

    Science.gov (United States)

    Fijany, A.; Vatan, F.; Barrett, A.; James, M.; Williams, C.; Mackey, R.

    2003-01-01

    Systematic methods of general diagnosis exist in literature, but they all suffer from two major drawbacks that severely limit their practical applications. In this paper, we propose a two-fold approach to overcome these limitations.

  13. An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique

    International Nuclear Information System (INIS)

    Taheri-Garavand, Amin; Ahmadi, Hojjat; Omid, Mahmoud; Mohtasebi, Seyed Saeid; Mollazade, Kaveh; Russell Smith, Alan John; Carlomagno, Giovanni Maria

    2015-01-01

    This research presents a new intelligent fault diagnosis and condition monitoring system for classification of different conditions of cooling radiator using infrared thermal images. The system was adopted to classify six types of cooling radiator faults; radiator tubes blockage, radiator fins blockage, loose connection between fins and tubes, radiator door failure, coolant leakage, and normal conditions. The proposed system consists of several distinct procedures including thermal image acquisition, image pre-processing, image processing, two-dimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection using a genetic algorithm (GA), and finally classification by artificial neural networks (ANNs). The 2D-DWT is implemented to decompose the thermal images. Subsequently, statistical texture features are extracted from the original images and are decomposed into thermal images. The significant selected features are used to enhance the performance of the designed ANN classifier for the 6 types of cooling radiator conditions (output layer) in the next stage. For the tested system, the input layer consisted of 16 neurons based on the feature selection operation. The best performance of ANN was obtained with a 16-6-6 topology. The classification results demonstrated that this system can be employed satisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator. - Highlights: • Intelligent fault diagnosis of cooling radiator using thermal image processing. • Thermal image processing in a multiscale representation structure by 2D-DWT. • Selection features based on a hybrid system that uses both GA and ANN. • Application of ANN as classifier. • Classification accuracy of fault detection up to 93.83%

  14. Computer decision support system for the stomach cancer diagnosis

    Science.gov (United States)

    Polyakov, E. V.; Sukhova, O. G.; Korenevskaya, P. Y.; Ovcharova, V. S.; Kudryavtseva, I. O.; Vlasova, S. V.; Grebennikova, O. P.; Burov, D. A.; Yemelyanova, G. S.; Selchuk, V. Y.

    2017-01-01

    The paper considers the creation of the computer knowledge base containing the data of histological, cytologic, and clinical researches. The system is focused on improvement of diagnostics quality of stomach cancer - one of the most frequent death causes among oncologic patients.

  15. Neuro-fuzzy system for prostate cancer diagnosis.

    Science.gov (United States)

    Benecchi, Luigi

    2006-08-01

    To develop a neuro-fuzzy system to predict the presence of prostate cancer. Neuro-fuzzy systems harness the power of two paradigms: fuzzy logic and artificial neural networks. We compared the predictive accuracy of our neuro-fuzzy system with that obtained by total prostate-specific antigen (tPSA) and percent free PSA (%fPSA). The data from 1030 men (both outpatients and hospitalized patients) were used. All men had a tPSA level of less than 20 ng/mL. Of the 1030 men, 195 (18.9%) had prostate cancer. A neuro-fuzzy system was developed using the coactive neuro-fuzzy inference system model. The mean area under the receiver operating characteristic curve for the neuro-fuzzy system output was 0.799 +/- 0.029 (95% confidence interval 0.760 to 0.835), for tPSA, it was 0.724 +/- 0.032 (95% confidence interval 0.681 to 0.765), and for %fPSA, 0.766 +/- 0.024 (95% confidence interval 0.725 to 0.804). Furthermore, pairwise comparison of the area under the curves evidenced differences among %fPSA, tPSA, and neuro-fuzzy system's output (tPSA versus neuro-fuzzy system's output, P = 0.008; %fPSA versus neuro-fuzzy system's output, P = 0.032). The comparison at 95% sensitivity showed that the neuro-fuzzy system had the best specificity (31.9%). This study presented a neuro-fuzzy system based on both serum data (tPSA and %fPSA) and clinical data (age) to enhance the performance of tPSA to discriminate prostate cancer. The predictive accuracy of the neuro-fuzzy system was superior to that of tPSA and %fPSA.

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

    Science.gov (United States)

    Schermer, Tjard R; Robberts, Bas; Crockett, Alan J; Thoonen, Bart P; Lucas, Annelies; Grootens, Joke; Smeele, Ivo J; Thamrin, Cindy; Reddel, Helen K

    2016-09-29

    Clinical guidelines indicate that a chronic obstructive pulmonary disease (COPD) diagnosis is made from a single spirometry test. However, long-term stability of diagnosis based on forced expiratory volume in 1 s over forced vital capacity (FEV 1 /FVC) ratio has not been reported. In primary care subjects at risk for COPD, we investigated shifts in diagnostic category (obstructed/non-obstructed). The data were from symptomatic 40+ years (ex-)smokers referred for diagnostic spirometry, with three spirometry tests, each 12±2 months apart. The obstruction was based on post-bronchodilator FEV 1 /FVC smokers or SABA users at year 1. Change from non-obstructed to obstructed was more likely for males, older subjects, current smokers and patients with lower baseline FEV 1 % predicted, and less likely for those with higher baseline BMI. Up to one-third of symptomatic (ex-)smokers with baseline obstruction on diagnostic spirometry had shifted to non-obstructed when routinely re-tested after 1 or 2 years. Given the implications for patients and health systems of a diagnosis of COPD, it should not be based on a single spirometry test.

  17. Study of expert system of fault diagnosis for nuclear power plant

    International Nuclear Information System (INIS)

    Chen Zhihui; Xia Hong; Liu Miao

    2005-01-01

    Based on the fault features of Nuclear Power Plant, the ES (expert system) of fault diagnosis has been programmed. The knowledge in the ES adopts the production systems, which can express the certain and uncertain knowledge. For certain knowledge, the simple reasoning mechanism of prepositional logic is adopted. For the uncertain knowledge, CF (certain factor) is used to express the uncertain, thus to set up the reasoning mechanism. In order to solve the 'bottleneck' problem for knowledge acquisition, rough set theory is incorporated into the fault diagnose system and the reduction algorithm based on the discernibility matrix is improved. In the improved algorithm, the measure of attribute importance first calculate the attribute which have the same value in the same decision-sort, then calculate the degrees of attribute in the discernibility matrix. Several different faults have been diagnosed on some emulator with this expert system. (authors)

  18. Intelliface - Intelligent Assistant for Interfacing Diagnosis and Planning Systems, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — To integrate automated diagnosis and automated planning functions, one must translate diagnosed system faults to corresponding changes in resource availabilities....

  19. Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles

    Directory of Open Access Journals (Sweden)

    Yu-shan Sun

    2016-05-01

    Full Text Available Autonomous Underwater Vehicles (AUVs generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

  20. Fuzzy clustering methods application for Alzheimer’s diseases diagnosis based on PET images

    OpenAIRE

    Krashenyi, Ihor Eduardovych; Popov, Anton Oleksandrovych; Ramirez, Haver; Gorriz, Huan Manuel

    2016-01-01

    This work was dedicated to clustering methods application in fuzzy inference system for Alzheimer’s disease diagnosis using PET-images. Three methods (Subtractive Clustering, C-means and Fuzzy Grid Partition) of clustering were discussed and their performance in Alzheimer’s disease diagnosis were measured. Recommendation of the future use of Subtractive Clustering algorithm in the computeraided diagnosis system for Alzheimer’s disease are given. The performance of this algorithm is AUC=0,8791...

  1. A client/server system for remote diagnosis of cardiac arrhythmias.

    Science.gov (United States)

    Tong, D A; Gajjala, V; Widman, L E

    1995-01-01

    Health care practitioners are often faced with the task of interpreting complex heart rhythms from electrocardiograms (ECGs) produced by 12-lead ECG machines, ambulatory (Holter) monitoring systems, and intensive-care unit monitors. Usually, the practitioner caring for the patient does not have specialized training in cardiology or in ECG interpretation; and commercial programs that interpret 12-lead ECGs have been well-documented in the medical literature to perform poorly at analyzing cardiac rhythm. We believe that a system capable of providing comprehensive ECG interpretation as well as access to online consultations will be beneficial to the health care system. We hypothesized that we could develop a client-server based telemedicine system capable of providing access to (1) an on-line knowledge-based system for remote diagnosis of cardiac arrhythmias and (2) an on-line cardiologist for real-time interactive consultation using readily available resources on the Internet. Furthermore, we hypothesized that Macintosh and Microsoft Windows-based personal computers running an X server could function as the delivery platform for the developed system. Although we were successful in developing such a system that will run efficiently on a UNIX-based work-station, current personal computer X server software are not capable of running the system efficiently.

  2. Acoustic Emission Monitoring of Incipient Failure in Journal Bearings( III ) - Development of AE Diagnosis System for Journal Bearings -

    International Nuclear Information System (INIS)

    Chung, Min Hwa; Cho, Yong Sang; Yoon, Dong Jin; Kwon, Oh Yang

    1996-01-01

    For the condition monitoring of the journal bearing in rotating machinery, a system for their diagnosis by acoustic emission(AE) was developed. AE has been used to detect abnormal conditions in the bearing system. It was found from the field application study as well as the laboratory experiment using a simulated journal bearing system that AE RMS voltage was the most efficient parameter for the purpose of current study. Based on the above results, algorithms and judgement criteria for the diagnosis system was established. The system is composed of four parts as follows: the sensing part including AE sensor and preamplifier, the signal processing part for RMS-to-DC conversion to measure AE ms voltage, the interface part for transferring RMS voltage data into PC using A/D converter, and the software part including the graphic display of bearing conditions and the diagnosis program

  3. Intelligent Adaptation Process for Case Based Systems

    International Nuclear Information System (INIS)

    Nassar, A.M.; Mohamed, A.H.; Mohamed, A.H.

    2014-01-01

    Case Based Reasoning (CBR) Systems is one of the important decision making systems applied in many fields all over the world. The effectiveness of any CBR system based on the quality of the storage cases in the case library. Similar cases can be retrieved and adapted to produce the solution for the new problem. One of the main issues faced the CBR systems is the difficulties of achieving the useful cases. The proposed system introduces a new approach that uses the genetic algorithm (GA) technique to automate constructing the cases into the case library. Also, it can optimize the best one to be stored in the library for the future uses. However, the proposed system can avoid the problems of the uncertain and noisy cases. Besides, it can simply the retrieving and adaptation processes. So, it can improve the performance of the CBR system. The suggested system can be applied for many real-time problems. It has been applied for diagnosis the faults of the wireless network, diagnosis of the cancer diseases, diagnosis of the debugging of a software as cases of study. The proposed system has proved its performance in this field

  4. Aid system in the attention direction for accidents diagnosis at nuclear power plants based on artificial intelligence; Sistema de auxilio para o direcionamento da atencao no diagnostico de acidentes em usinas nucleares baseado em inteligencia artificial

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Rafael Gomes da

    2009-07-01

    Transient identification in Nuclear Power Plant (NPP) is often a very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event Several systems based on specialist systems, neural-networks, and fuzzy logic have been developed for transient identification. In the work, we investigate the possibility of using a Neuro Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A preliminary evaluation of the developed system was made at the Human-System Interface Laboratory (LABIHS). The obtained results show that the system can help the operators to take decisions during transients/accidents in the plant (author)

  5. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    International Nuclear Information System (INIS)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-01-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis

  6. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    Energy Technology Data Exchange (ETDEWEB)

    Stoitsis, John [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)]. E-mail: stoitsis@biosim.ntua.gr; Valavanis, Ioannis [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Mougiakakou, Stavroula G. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Golemati, Spyretta [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Nikita, Alexandra [University of Athens, Medical School 152 28 Athens (Greece); Nikita, Konstantina S. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)

    2006-12-20

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  7. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.

    Science.gov (United States)

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-08-22

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  8. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

    Science.gov (United States)

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-01-01

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472

  9. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

    Directory of Open Access Journals (Sweden)

    Paola Costamagna

    2016-08-01

    Full Text Available The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  10. Sclerosing cholangitis: Clinicopathologic features, imaging spectrum, and systemic approach to differential diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Ni Eun [Dept. of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Kim, So Yeon; Lee, Seung Soo; Byun, Jae Ho; Kim, Hyoung Jung; Kim, Jin Hee; Lee, Moon Gyu [Dept. of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul (Korea, Republic of)

    2016-02-15

    Sclerosing cholangitis is a spectrum of chronic progressive cholestatic liver disease characterized by inflammation, fibrosis, and stricture of the bile ducts, which can be classified as primary and secondary sclerosing cholangitis. Primary sclerosing cholangitis is a chronic progressive liver disease of unknown cause. On the other hand, secondary sclerosing cholangitis has identifiable causes that include immunoglobulin G4-related sclerosing disease, recurrent pyogenic cholangitis, ischemic cholangitis, acquired immunodeficiency syndrome-related cholangitis, and eosinophilic cholangitis. In this review, we suggest a systemic approach to the differential diagnosis of sclerosing cholangitis based on the clinical and laboratory findings, as well as the typical imaging features on computed tomography and magnetic resonance (MR) imaging with MR cholangiography. Familiarity with various etiologies of sclerosing cholangitis and awareness of their typical clinical and imaging findings are essential for an accurate diagnosis and appropriate management.

  11. Management Index Systems and Energy Efficiency Diagnosis Model for Power Plant: Cases in China

    Directory of Open Access Journals (Sweden)

    Jing-Min Wang

    2016-01-01

    Full Text Available In recent years, the energy efficiency of thermal power plant largely contributes to that of the industry. A thorough understanding of influencing factors, as well as the establishment of scientific and comprehensive diagnosis model, plays a key role in the operational efficiency and competitiveness for the thermal power plant. Referring to domestic and abroad researches towards energy efficiency management, based on Cloud model and data envelopment analysis (DEA model, a qualitative and quantitative index system and a comprehensive diagnostic model (CDM are construed. To testify rationality and usability of CDM, case studies of large-scaled Chinese thermal power plants have been conducted. In this case, CDM excavates such qualitative factors as technology, management, and so forth. The results shows that, compared with conventional model, which only considered production running parameters, the CDM bears better adaption to reality. It can provide entities with efficient instruments for energy efficiency diagnosis.

  12. A Wireless Emergency Telemedicine System for Patients Monitoring and Diagnosis

    Directory of Open Access Journals (Sweden)

    M. Abo-Zahhad

    2014-01-01

    Full Text Available Recently, remote healthcare systems have received increasing attention in the last decade, explaining why intelligent systems with physiology signal monitoring for e-health care are an emerging area of development. Therefore, this study adopts a system which includes continuous collection and evaluation of multiple vital signs, long-term healthcare, and a cellular connection to a medical center in emergency case and it transfers all acquired raw data by the internet in normal case. The proposed system can continuously acquire four different physiological signs, for example, ECG, SpO2, temperature, and blood pressure and further relayed them to an intelligent data analysis scheme to diagnose abnormal pulses for exploring potential chronic diseases. The proposed system also has a friendly web-based interface for medical staff to observe immediate pulse signals for remote treatment. Once abnormal event happened or the request to real-time display vital signs is confirmed, all physiological signs will be immediately transmitted to remote medical server through both cellular networks and internet. Also data can be transmitted to a family member’s mobile phone or doctor’s phone through GPRS. A prototype of such system has been successfully developed and implemented, which will offer high standard of healthcare with a major reduction in cost for our society.

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

    Science.gov (United States)

    Khawaja, Taimoor Saleem

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

  14. Diagnosis of common hemoglobinopathies among South East Asian population using capillary isoelectric focusing system.

    Science.gov (United States)

    Srivorakun, H; Fucharoen, G; Sanchaisuriya, K; Fucharoen, S

    2017-02-01

    We have evaluated an automated capillary isoelectric focusing (cIEF)-based Hb analyzer in diagnosis of hemoglobinopathies commonly found among South East Asian population. Study was performed on a cohort of 665 adult Thai subjects and 13 fetal blood specimens obtained at routine thalassemia diagnostic laboratory. Hb analysis was performed using the cIEF system. Thalassemia genotypes were defined by DNA analysis. The system revealed satisfactorily within-run and between-run precision for quantitation of Hb A 2 and Hb E (CV: 0.02-0.09%). The reference ranges of Hb A 2 and Hb E were 2.6-4.0% and 25.7-33.1%, respectively. The system identified the cases of β-thalassemia and Hb E disorders correctly. Several thalassemia genotypes and Hb variants were identifiable. However, Hb Constant Spring was separated closely to Hb A 2 and Hbs Bart's and H were relatively difficult to be reported due to interfering peaks separating at the same regions. Prenatal diagnosis by fetal blood analysis was found to be accurate for Hb Bart's hydrops fetalis and Hb E-β 0 -thalassemia disease. The cIEF system could accurately diagnose β-thalassemia and Hb E carriers and demonstrate many Hb variants found in the region. The system cannot report Hb A 2 in the presence of Hb E whereas Hbs Lepore and F are comigrated. Diagnosis of α-thalassemia disease based on Hb H and Hb Bart's might be difficult. © 2016 John Wiley & Sons Ltd.

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

    CERN Document Server

    2017-01-01

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

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

    African Journals Online (AJOL)

    Abstract. This cross-sectional study evaluated knowledge and acceptability of prenatal diagnosis among 500 pregnant women at the University College Hospital, Ibadan. Most participants were aged 25-34 years , self-employed, Muslim, monogamy, secondary school leavers, on income of < ₦10,000.00 (US$ 67.00)/month.

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

    African Journals Online (AJOL)

    Two of the many factors which may affect the accuracy of pregnancy diagnosis using milk progesterone levels are day of sampling and number of samples taken per cow. These two aspects were analysed using information obtained from progesterone profiles encompassing 359 pregnancy tests. Where a single sample was ...

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

    African Journals Online (AJOL)

    AJRH Managing Editor

    Determinants of acceptability were age, educational attainment, marital status and religion. Being married significantly affected knowledge scores, while tertiary education, being divorced, unskilled and self-employed positively influenced attitude towards prenatal diagnosis. (Afr J Reprod Health 2014; 18[1]: 127-. 132).

  19. Analyzing reliability of seizure diagnosis based on semiology.

    Science.gov (United States)

    Jin, Bo; Wu, Han; Xu, Jiahui; Yan, Jianwei; Ding, Yao; Wang, Z Irene; Guo, Yi; Wang, Zhongjin; Shen, Chunhong; Chen, Zhong; Ding, Meiping; Wang, Shuang

    2014-12-01

    This study aimed to determine the accuracy of seizure diagnosis by semiological analysis and to assess the factors that affect diagnostic reliability. A total of 150 video clips of seizures from 50 patients (each with three seizures of the same type) were observed by eight epileptologists, 12 neurologists, and 20 physicians (internists). The videos included 37 series of epileptic seizures, eight series of physiologic nonepileptic events (PNEEs), and five series of psychogenic nonepileptic seizures (PNESs). After observing each video, the doctors chose the diagnosis of epileptic seizures or nonepileptic events for the patient; if the latter was chosen, they further chose the diagnosis of PNESs or PNEEs. The overall diagnostic accuracy rate for epileptic seizures and nonepileptic events increased from 0.614 to 0.660 after observations of all three seizures (p semiological diagnosis of seizures is greatly affected by the seizure type as well as the doctor's experience. Although the overall reliability is limited, it can be improved by observing more seizures. Copyright © 2014 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2016-01-01

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

  1. Fault diagnosis in spur gears based on genetic algorithm and random forest

    Science.gov (United States)

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  2. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  3. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Science.gov (United States)

    Flores, Agustín; Morant, Francisco

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

  4. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  5. Hybrid approach for fault diagnosis based on multilevel flow model and information fusion of nuclear power plant

    International Nuclear Information System (INIS)

    Ma Jie; Guo Lifeng; Zhang Yusheng; Peng Qiao; Ruan Minzhi

    2011-01-01

    In order to improve the ability of condition monitoring and fault diagnostic system, a hybrid intelligent diagnostic system based on multilevel flow model (MFM) and information fusion was proposed. This method utilized information fusion technique to improve the rapidness and veracity of fault diagnosis, and made use of MFM to explain the alarm propagation path, which could enhance the comprehension of diagnostic result. The emulation test proves that the hybrid intelligent diagnostic system can identify fault and propose the alarm analysis quickly. (authors)

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

    Science.gov (United States)

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

    2017-10-01

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

  7. Knowledge Acquisition for Diagnosis of Skin Diseases as an Initial Platform for an Expert System

    Directory of Open Access Journals (Sweden)

    Fatemeh Rangraz Jeddi

    2018-04-01

    Full Text Available Background: The diagnosis of skin diseases, especially in patients suffering from more than one disease or having similar symptoms, is very complex and access to the knowledge of skin diseases makes the design of an expert system easier. This research aimed to design a knowledge base used for diagnosis of complex skin diseases, selected by experts. Methods: This applied developmental research was conducted in 2015. The study population included 10 dermatologists of Razi Hospital, affiliated to Tehran University of Medical Sciences. Data collection was conducted through a questionnaire and a checklist. The questionnaire had face and content validity and was based on Likert scale according to the twelfth chapter of the International Classification of Diseases (Tenth revision. The questionnaires were administered to participants and collected after completion. A checklist of knowledge acquisition was designed for each disease based on the semiology book of skin diseases with “agree-disagree” options and completed by interviews. Signs and symptoms had an agreement with at least 70% of the experts, and symptoms that were added according to the experts’ proposal entered the checklist and was given to experts for consensus in future evaluations. The software used in this research was Clementine and its statistical method used was Stata. The data were analyzed using SPSS, 16. Results: The diseases including pemphigus vulgaris, lichen planus, basal cell carcinoma, melanoma, and scabies were selected to design the expert system. Confirmed signs and symptoms of the diseases selected by the experts included 106 causes. Conclusion: The choice of the selected diseases needed by specialists in the knowledge system is a very vital component needed in designing the expert knowledge base system to meet international standards based on international classification and according to the needs of specialists.

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

    Science.gov (United States)

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

    2017-11-01

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

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

    DEFF Research Database (Denmark)

    Huang, Sijia; Chong, Nicole; Lewis, Nathan

    2016-01-01

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

  10. Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

    Science.gov (United States)

    Luo, Yuan; Tang, Xiaoying

    2017-03-01

    Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

  11. The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer’s disease diagnosis

    Directory of Open Access Journals (Sweden)

    Raymundo eCassani

    2014-03-01

    Full Text Available Over the last decade, electroencephalography (EEG has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD. EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system ``semi-automated. Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (disadvantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR algorithms (both alone and in combination with each other on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR, blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA, and wavelet enhanced independent component analysis (wICA. Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate, early detection (control vs. mild, and disease progression (mild vs. moderate, thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.

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

    Science.gov (United States)

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

    2018-04-01

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

  13. An improved wrapper-based feature selection method for machinery fault diagnosis.

    Science.gov (United States)

    Hui, Kar Hoou; Ooi, Ching Sheng; Lim, Meng Hee; Leong, Mohd Salman; Al-Obaidi, Salah Mahdi

    2017-01-01

    A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.

  14. Star polymer-based unimolecular micelles and their application in bio-imaging and diagnosis.

    Science.gov (United States)

    Jin, Xin; Sun, Pei; Tong, Gangsheng; Zhu, Xinyuan

    2018-02-03

    As a novel kind of polymer with covalently linked core-shell structure, star polymers behave in nanostructure in aqueous medium at all concentration range, as unimolecular micelles at high dilution condition and multi-micelle aggregates in other situations. The unique morphologies endow star polymers with excellent stability and functions, making them a promising platform for bio-application. A variety of functions including imaging and therapeutics can be achieved through rational structure design of star polymers, and the existence of plentiful end-groups on shell offers the opportunity for further modification. In the last decades, star polymers have become an attracting platform on fabrication of novel nano-systems for bio-imaging and diagnosis. Focusing on the specific topology and physicochemical properties of star polymers, we have reviewed recent development of star polymer-based unimolecular micelles and their bio-application in imaging and diagnosis. The main content of this review summarizes the synthesis of integrated architecture of star polymers and their self-assembly behavior in aqueous medium, focusing especially on the recent advances on their bio-imaging application and diagnosis use. Finally, we conclude with remarks and give some outlooks for further exploration in this field. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

    International Nuclear Information System (INIS)

    Banica, Constantin; Dobrea, Dumitru

    1999-01-01

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

  16. Development of an Image Processing System for Automatic Melanoma Diagnosis from Dermoscopic Images: Preliminary Sudy - Original Article

    Directory of Open Access Journals (Sweden)

    M. Emin Yüksel

    2008-12-01

    Full Text Available Objective: Design and implementation of a medical image processing system that will provide decision support to the clinician in the diagnosis of melanoma type skin cancers by performing the analysis of dermoscopic images.Methods: Visual features of pigmented lesions are converted into measurable numerical quantities by employing digital image processing methods and a classification regarding melanoma diagnosis is performed based on these quantitative data.Results: We achieved numerical results showing asymmetry, border and color features of the pigmentary lesions by using segmentation, image histogram, thresholding, convex hull, color clustering, color quantization and distribution methods. Conclusion: The system under development speeds up the decision process of the clinician. In addition, it allows the diagnosis to be based on more objective data.

  17. Numerical model for thermoeconomic diagnosis in commercial transcritical/subcritical booster refrigeration systems

    International Nuclear Information System (INIS)

    Ommen, Torben; Elmegaard, Brian

    2012-01-01

    Highlights: ► A transcritical booster refrigeration plant is modelled. ► We examine changes in cost flow at different operation parameters. ► The use of characteristic curves for diagnosis is studied. - Abstract: Transcritical/subcritical booster refrigeration systems are increasingly installed and used in Danish supermarkets. The systems operate in both transcritical and subcritical conditions dependent on the heat rejection performance and the ambient conditions. The plant consists of one refrigerant cycle supplying refrigerant for evaporators in both chilled and frozen display cases. In the paper, thermoeconomic theory is used to establish the cost of cooling at each individual temperature level based on operating costs. With a high amount of operating systems, faulty operation becomes an economic, and environmental, interest. A general solution for evaluation of these systems is considered, with the objective to reduce cost and power consumption of malfunctioning equipment in operation. An analysis of the use of thermoeconomic diagnosis methods is required, as these methods may prove applicable. To accommodate the analysis, a numerical model of a transcritical booster refrigeration plant is considered in this paper. Additionally the characteristic curves method is applied to the high pressure compressor unit of the refrigeration plant. The approach successfully determine whether an anomaly is intrinsic or induced in the component when no uncertainties are introduced in the steady state model.

  18. A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis.

    Science.gov (United States)

    El-Sappagh, Shaker; Elmogy, Mohammed; Riad, A M

    2015-11-01

    Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. Evaluation of telemedicine systems for impacted third molars diagnosis

    Directory of Open Access Journals (Sweden)

    Duka Miloš

    2009-01-01

    Full Text Available Background/Aim. In the last twenty years significant advances have been made in the fields of information and telecommunication technology in health care applications, with a positive impact on the health care practice. The need for remote diagnosis and planning of interventions is of special importance in military health care, and health management of immobile persons, and those with special needs. In cases such as these, availability of specialist health care is mainly limited by geographic and financial factors. The aim of this study was to investigate practical usability of telemedicine approaches in everyday management of oral surgery patients in terms of reliability of established diagnosis and indications for oral surgery treatment of the third molars. Methods. Our experimental randomized study enrolled 432 randomly selected patients of both genders, aged 20 to 87 years, undergoing panoramic radiography for some reason in the Centre for Dental Radiography in Belgrade. In addition to radiography, photographs of the face and mouth cavity were taken. These images were uploaded to the web server specially dedicated to the study purposes, and then transmitted to teledentists, i.e. oral surgeons, who made remote diagnoses. Diagnostic agreement was determined by way of the Cohen's kappa coefficient, and diagnostic sensitivity (SE, specificity (SP, and effectiveness (EFF were also established. Statistical significance was determined and comparisons performed by using the z-test, and testing of non-parametric characteristics by using the McNemar's χ2 test for p = 0.05 significance cut-off. Results. The results obtained by analyzed images and diagnostic assessment of the clinical diagnosis (kappa = 0.99, SE = 99%, SP = 99%, EFF = 99%, for 95% CI indicate an almost complete diagnostic agreement. The differences in diagnosis were not statistically significant. Conclusion. Diagnostic assessment of the clinical diagnosis of impacted or semi

  20. Comparison of Physician-Based and Patient-Based Criteria for the Diagnosis of Fibromyalgia.

    Science.gov (United States)

    Wolfe, Frederick; Fitzcharles, Mary-Ann; Goldenberg, Don L; Häuser, Winfried; Katz, Robert L; Mease, Philip J; Russell, Anthony S; Jon Russell, I; Walitt, Brian

    2016-05-01

    The American College of Rheumatology (ACR) 2010 preliminary fibromyalgia diagnostic criteria require symptom ascertainment by physicians. The 2011 survey or research modified ACR criteria use only patient self-report. We compared physician-based (MD) (2010) and patient-based (PT) (2011) criteria and criteria components to determine the degree of agreement between criteria methodology. We studied prospectively collected, previously unreported rheumatology practice data from 514 patients and 30 physicians in the ACR 2010 study. We evaluated the widespread pain index, polysymptomatic distress (PSD) scale, tender point count (TPC), and fibromyalgia diagnosis using 2010 and 2011 rules. Bland-Altman 95% limits of agreement (LOA), kappa statistic, Lin's concordance coefficient, and the area under the receiver operating curve (ROC) were used to measure agreement and discrimination. MD and PT diagnostic agreement was substantial (83.4%, κ = 0.67). PSD scores differed slightly (12.3 MD, 12.8 PT; P = 0.213). LOA for PSD were -8.5 and 7.7, with bias of -0.42. The TPC was strongly associated with both the MD (r = 0.779) and PT PSD scales (r = 0.702). There was good agreement in MD and PT fibromyalgia diagnosis and other measures among rheumatology patients. Low bias scores indicate consistent results for physician and patient measures, but large values for LOA indicate many widely discordant pairs. There is acceptable agreement in diagnosis and PSD for research, but insufficient agreement for clinical decisions and diagnosis. We suggest adjudication of symptom data by patients and physicians, as recommended by the 2010 ACR criteria. © 2016, American College of Rheumatology.

  1. Diagnosis of Bearing System using Minimum Variance Cepstrum

    International Nuclear Information System (INIS)

    Lee, Jeong Han; Choi, Young Chul; Park, Jin Ho; Lee, Won Hyung; Kim, Chan Joong

    2005-01-01

    Various bearings are commonly used in rotating machines. The noise and vibration signals that can be obtained from the machines often convey the information of faults and these locations. Monitoring conditions for bearings have received considerable attention for many years, because the majority of problems in rotating machines are caused by faulty bearings. Thus failure alarm for the bearing system is often based on the detection of the onset of localized faults. Many methods are available for detecting faults in the bearing system. The majority of these methods assume that faults in bearings produce impulses. Impulse events can be attributed to bearing faults in the system. McFadden and Smith used the bandpass filter to filter the noise signal and then obtained the envelope by using the envelope detector. D. Ho and R. B Randall also tried envelope spectrum to detect faults in the bearing system, but it is very difficult to find resonant frequency in the noisy environments. S. -K. Lee and P. R. White used improved ANC (adaptive noise cancellation) to find faults. The basic idea of this technique is to remove the noise from the measured vibration signal, but they are not able to show the theoretical foundation of the proposed algorithms. Y.-H. Kim et al. used a moving window. This algorithm is quite powerful in the early detection of faults in a ball bearing system, but it is difficult to decide initial time and step size of the moving window. The early fault signal that is caused by microscopic cracks is commonly embedded in noise. Therefore, the success of detecting fault signal is completely determined by a method's ability to distinguish signal and noise. In 1969, Capon coined maximum likelihood (ML) spectra which estimate a mixed spectrum consisting of line spectrum, corresponding to a deterministic random process, plus arbitrary unknown continuous spectrum. The unique feature of these spectra is that it can detect sinusoidal signal from noise. Our idea

  2. Experiences with an expert system technology for real-time monitoring and diagnosis of industrial processes

    International Nuclear Information System (INIS)

    Chou, Q.B.; Mylopoulos, J.; Opala, J.

    1996-01-01

    The complexity of modern industrial processes and the large amount of data available to their operators make it difficult to monitor their status and diagnose potential failures. Although there have been many attempts to apply knowledge-based technologies to this problem, there have not been any convincing success. This paper describes recent experiences with a technology that combines artificial intelligence and simulation techniques for building real-time monitoring and diagnosis systems. A prototype system for monitoring and diagnosing the feedwater system of a nuclear power plant built using this technology is described. The paper then describes several interesting classes of failures that the prototype is capable of diagnosing. (author). 19 refs, 6 figs

  3. Ultrasonographic scoring system: an auxiliary to differential diagnosis of gastric pathologies.

    Science.gov (United States)

    Boyacioğlu, S; Dolar, E; Acar, Y; Dalay, R; Temuçin, G

    1993-02-01

    In a prospective clinical study, 64 patients with gastric pathologies (27 malignant and 37 benign) were examined ultrasonographically. Gastric wall layer changes, gastric wall thickness, lesion length, and protrusion into the lumen were evaluated. A scoring system was defined based on the distribution of these parameters and the score of each patient was calculated retrospectively. Six of the malignant cases had scores in the benign range and 3 of the benign cases had scores in the malignant range. Sensitivity of this scoring system in terms of detecting malignancy was 78% and specificity 92%. The positive predictive value was 88%, the negative predictive value was 85%, and overall diagnostic accuracy was 86%. This scoring system was considered to be a useful aid in the differential diagnosis of gastric pathologies.

  4. Sequential fault diagnosis for mechatronics system using diagnostic hybrid bond graph and composite harmony search

    Directory of Open Access Journals (Sweden)

    Ming Yu

    2015-12-01

    Full Text Available This article proposes a sequential fault diagnosis method to handle asynchronous distinct faults using diagnostic hybrid bond graph and composite harmony search. The faults under consideration include fault mode, abrupt fault, and intermittent fault. The faults can occur in different time instances, which add to the difficulty of decision making for fault diagnosis. This is because the earlier occurred fault can exhibit fault symptom which masks the fault symptom of latter occurred fault. In order to solve this problem, a sequential identification algorithm is developed in which the identification task is reactivated based on two conditions. The first condition is that the latter occurred fault has at least one inconsistent coherence vector element which is consistent in coherence vector of the earlier occurred fault, and the second condition is that the existing fault coherence vector has the ability to hide other faults and the second-level residual exceeds the threshold. A new composite harmony search which is capable of handling continuous variables and binary variables simultaneously is proposed for identification purpose. Experiments on a mobile robot system are conducted to assess the proposed sequential fault diagnosis algorithm.

  5. [Overcoming the limitations of the descriptive and categorical approaches in psychiatric diagnosis: a proposal based on Bayesian networks].

    Science.gov (United States)

    Sorias, Soli

    2015-01-01

    Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.

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

    Science.gov (United States)

    Zhao, Yue; Wang, Nian; Cui, Xiaoyu

    2017-08-01

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

  7. Assisted Diagnosis Research Based on Improved Deep Autoencoder

    Directory of Open Access Journals (Sweden)

    Ke Zhang-Han

    2017-01-01

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

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

    Science.gov (United States)

    Daigle, Matthew; Roychoudhury, Indranil

    2010-01-01

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

  9. Disposable Morpho menelaus Based Flexible Microfluidic and Electronic Sensor for the Diagnosis of Neurodegenerative Disease.

    Science.gov (United States)

    He, Zhenzhu; Elbaz, Abdelrahman; Gao, Bingbing; Zhang, Junning; Su, Enben; Gu, Zhongze

    2018-03-01

    Rapid early disease prevention or precise diagnosis is almost impossible in low-resource settings. Natural ordered structures in nature have great potential for the development of ultrasensitive biosensors. Here, motivated by the unique structures and extraordinary functionalities of ordered structures in nature, a biosensor based on butterfly wings is presented. In this study, a flexible Morpho menelaus (M. menelaus) based wearable sensor is integrated with a microfluidic system and electronic networks to facilitate the diagnosis of neurodegenerative disease (ND). In the microfluidic section, the structural characteristics of the M. menelaus wings up layer are combined with SiO 2 nanoparticles to form a heterostructure. The fluorescent enhancement property of the heterostructure is used to increase the fluorescent intensity for multiplex detection of two proteins: IgG and AD7c-NTP. For the electronic section, conductive ink is blade-coated on the under layer of wings for measuring resistance change rate to obtain the frequency of static tremors of ND patients. The disposable M. menelaus based flexible microfluidic and electronic sensor enables biochemical-physiological hybrid monitoring of ND. The sensor is also amenable to a variety of applications, such as comprehensive personal healthcare and human-machine interaction. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Clinical decision support system for the diagnosis of adolescence health.

    Science.gov (United States)

    Moutsouri, Irene; Nikou, Amalia; Pampalou, Machi; Lentza, Maria; Spyridakis, Paulos; Mathiopoulou, Natassa; Konsoulas, Dimitris; Lampou, Marianna; Alexiou, Athanasios

    2015-01-01

    It is common that children confront psychological problems when they reach puberty. These problems could easily be overcome, but in many cases they could be severe, leading to social estrangement or worse in madness or death. According to information collected we designed a questionnaire about the psychology of adolescents in order to help people in that age or their elders find out if they have health issues. We used already published researches and material concerning all the psychological problems a child can confront in order to make a reliable questionnaire and to develop the clinical decision support system. Our main objective is to publish and administrate a web-based free tool for sharing medical knowledge about any psychological disease a child can already have or develop during puberty.

  11. A new fault diagnosis algorithm for AUV cooperative localization system

    Science.gov (United States)

    Shi, Hongyang; Miao, Zhiyong; Zhang, Yi

    2017-10-01

    Multiple AUVs cooperative localization as a new kind of underwater positioning technology, not only can improve the positioning accuracy, but also has many advantages the single AUV does not have. It is necessary to detect and isolate the fault to increase the reliability and availability of the AUVs cooperative localization system. In this paper, the Extended Multiple Model Adaptive Cubature Kalmam Filter (EMMACKF) method is presented to detect the fault. The sensor failures are simulated based on the off-line experimental data. Experimental results have shown that the faulty apparatus can be diagnosed effectively using the proposed method. Compared with Multiple Model Adaptive Extended Kalman Filter and Multi-Model Adaptive Unscented Kalman Filter, both accuracy and timelines have been improved to some extent.

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

    NARCIS (Netherlands)

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

    2016-01-01

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

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  14. 2D nanomaterials based electrochemical biosensors for cancer diagnosis.

    Science.gov (United States)

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

    2017-03-15

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

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

    Science.gov (United States)

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

    2018-04-14

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

  16. Optical diagnosis system for intense electron beam diode plasma

    International Nuclear Information System (INIS)

    Yang Jie; Shu Ting; Zhang Jun; Fan Yuwei; Yang Jianhua; Liu Lie; Yin Yi; Luo Ling

    2012-01-01

    A nanosecond time-resolved imaging platform for diode plasmas diagnostics has been constructed based on the pulsed electron beam accelerator and high speed framing camera (HSFC). The accelerator can provide an electrical pulse with voltages of 200-500 kV, rise-time (from 10% to 90% amplitude) of 25 ns and duration of 110 ns. The diode currents up to kA level can be extracted. The trigger signal for camera was picked up by a water-resistor voltage divider after the main switch of the accelerator, which could avoid the disadvantageous influence of the time jitter caused by the breakdown of the gas gaps. Then the sampled negative electrical pulse was converted into a transistor-transistor logic (TTL) signal (5 V) with rise time of about 1.5 ns and time jitter less than 1 ns via a processor. And this signal was taken as the synchronization time base. According to the working characteristics of the camera, the synchronization scheme relying mainly on electrical pulse delay method supplemented by light signal delay method was determined to make sure that the camera can work synchronously with the light production and transportation from the diode plasma within the time scale of nanosecond. Moreover, shielding and filtering methods were used to restrain the interference on the measurement system from the accelerator. Finally, time resolved 2-D framing images of the diode plasma were acquired. (authors)

  17. Fault diagnosis

    Science.gov (United States)

    Abbott, Kathy

    1990-01-01

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

  18. Implementasi Hybrid Intelligent Information System Untuk Diagnosis Keperawatan

    OpenAIRE

    Alfionita, Ratih; Delima, Rosa; C., Antonius Rachmat

    2013-01-01

    By increasing amounts of data, a system gets more complex in accessing and processing, especially if the system is used to process information and makes a decision. Hybrid Intelligent Information System is a combination of information systems and expert systems. The system is better able to process the data and information, that generated be some knowledge to support decision making. Some application needs to process complex and large data, such as Medical Information Systems. Determination o...

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

    Science.gov (United States)

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

    2016-09-01

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

  20. Design of a modified adaptive neuro fuzzy inference system classifier for medical diagnosis of Pima Indians Diabetes

    Science.gov (United States)

    Sagir, Abdu Masanawa; Sathasivam, Saratha

    2017-08-01

    Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most of the diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system with Modified Levenberg-Marquardt algorithm using analytical derivation scheme for computation of Jacobian matrix. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about presence or absence of a disease. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent system was tested with Pima Indian Diabetes dataset obtained from the University of California at Irvine's (UCI) machine learning repository. The proposed method's performance was evaluated based on training and test datasets. In addition, an attempt was done to specify the effectiveness of the performance measuring total accuracy, sensitivity and specificity. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

  1. Research on the Method of Big Data Collecting, Storing and Analyzing of Tongue Diagnosis System

    Science.gov (United States)

    Chen, Xiaowei; Wu, Qingfeng

    2018-03-01

    This paper analyzes the contents of the clinical data of tongue diagnosis of TCM (Traditional Chinese Medicine), and puts forward a method to collect, store and analyze the clinical data of tongue diagnosis. Under the guidance of TCM theory of syndrome differentiation and treatment, this method combines with Hadoop, which is a distributed computing system with strong expansibility, and integrates the functions of analysis and conversion of big data of clinic tongue diagnosis. At the same time, the consistency, scalability and security of big data in tongue diagnosis are realized.

  2. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Shi-wang Hou

    2016-01-01

    Full Text Available Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

  3. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic.

    Science.gov (United States)

    Hou, Shi-Wang; Feng, Shunxiao; Wang, Hui

    2016-01-01

    Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

  4. Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI.

    Directory of Open Access Journals (Sweden)

    Kevin Li-Chun Hsieh

    Full Text Available The effects of a computer-aided diagnosis (CAD system based on quantitative intensity features with magnetic resonance (MR imaging (MRI were evaluated by examining radiologists' performance in grading gliomas. The acquired MRI database included 71 lower-grade gliomas and 34 glioblastomas. Quantitative image features were extracted from the tumor area and combined in a CAD system to generate a prediction model. The effect of the CAD system was evaluated in a two-stage procedure. First, a radiologist performed a conventional reading. A sequential second reading was determined with a malignancy estimation by the CAD system. Each MR image was regularly read by one radiologist out of a group of three radiologists. The CAD system achieved an accuracy of 87% (91/105, a sensitivity of 79% (27/34, a specificity of 90% (64/71, and an area under the receiver operating characteristic curve (Az of 0.89. In the evaluation, the radiologists' Az values significantly improved from 0.81, 0.87, and 0.84 to 0.90, 0.90, and 0.88 with p = 0.0011, 0.0076, and 0.0167, respectively. Based on the MR image features, the proposed CAD system not only performed well in distinguishing glioblastomas from lower-grade gliomas but also provided suggestions about glioma grading to reinforce radiologists' confidence rating.

  5. Flow cytometry-based diagnosis of primary immunodeficiency diseases

    Directory of Open Access Journals (Sweden)

    Hirokazu Kanegane

    2018-01-01

    Flow cytometry can evaluate specific cell populations and subpopulations, cell surface, intracellular and intranuclear proteins, biologic effects associated with specific immune defects, and certain functional immune characteristics, each being useful for the diagnosis and evaluation of PIDs. Flow cytometry effectively identifies major forms of PIDs, including severe combined immunodeficiency, X-linked agammaglobulinemia, hyper IgM syndromes, Wiskott-Aldrich syndrome, X-linked lymphoproliferative syndrome, familial hemophagocytic lymphohistiocytosis, autoimmune lymphoproliferative syndrome, IPEX syndrome, CTLA 4 haploinsufficiency and LRBA deficiency, IRAK4 and MyD88 deficiencies, Mendelian susceptibility to mycobacterial disease, chronic mucocuneous candidiasis, and chronic granulomatous disease. While genetic analysis is the definitive approach to establish specific diagnoses of PIDs, flow cytometry provides a tool to effectively evaluate patients with PIDs at relatively low cost.

  6. Truck circuits diagnosis for railway lines equipped with an automatic block signalling system

    Science.gov (United States)

    Spunei, E.; Piroi, I.; Muscai, C.; Răduca, E.; Piroi, F.

    2018-01-01

    This work presents a diagnosis method for detecting track circuits failures on a railway traffic line equipped with an Automatic Block Signalling installation. The diagnosis method uses the installation’s electrical schemas, based on which a series of diagnosis charts have been created. Further, the diagnosis charts were used to develop a software package, CDCBla, which substantially contributes to reducing the diagnosis time and human error during failure remedies. The proposed method can also be used as a training package for the maintenance staff. Since the diagnosis method here does not need signal or measurement inputs, using it does not necessitate additional IT knowledge and can be deployed on a mobile computing device (tablet, smart phone).

  7. iPhone-based teleradiology for the diagnosis of acute cervico-dorsal spine trauma.

    Science.gov (United States)

    Modi, Jayesh; Sharma, Pranshu; Earl, Alex; Simpson, Mark; Mitchell, J Ross; Goyal, Mayank

    2010-11-01

    To assess the feasibility of iPhone-based teleradiology as a potential solution for the diagnosis of acute cervico-dorsal spine trauma. We have developed a solution that allows visualization of images on the iPhone. Our system allows rapid, remote, secure, visualization of medical images without storing patient data on the iPhone. This retrospective study is comprised of cervico-dorsal computed tomogram (CT) scan examination of 75 consecutive patients having clinically suspected cervico-dorsal spine fracture. Two radiologists reviewed CT scan images on the iPhone. Computed tomogram spine scans were analyzed for vertebral body fracture and posterior elements fractures, any associated subluxation-dislocation and cord lesion. The total time taken from the launch of viewing application on the iPhone until interpretation was recorded. The results were compared with that of a diagnostic workstation monitor. Inter-rater agreement was assessed. The sensitivity and accuracy of detecting vertebral body fractures was 80% and 97% by both readers using the iPhone system with a perfect inter-rater agreement (kappa:1). The sensitivity and accuracy of detecting posterior elements fracture was 75% and 98% for Reader 1 and 50% and 97% for Reader 2 using the iPhone. There was good inter-rater agreement (kappa: 0.66) between both readers. No statistically significant difference was noted between time on the workstation and the iPhone system. iPhone-based teleradiology system is accurate in the diagnosis of acute cervicodorsal spinal trauma. It allows rapid, remote, secure, visualization of medical images without storing patient data on the iPhone.

  8. FDSAC-SPICE: fault diagnosis software for analog circuit based on SPICE simulation

    Science.gov (United States)

    Cao, Yiqin; Cen, Zhao-Hui; Wei, Jiao-Long

    2009-12-01

    This paper presents a novel fault diagnosis software (called FDSAC-SPICE) based on SPICE simulator for analog circuits. Four important techniques in AFDS-SPICE, including visual user-interface(VUI), component modeling and fault modeling (CMFM), fault injection and fault simulation (FIFS), fault dictionary and fault diagnosis (FDFD), greatly increase design-for-test and diagnosis efficiency of analog circuit by building a fault modeling-injection-simulationdiagnosis environment to get prior fault knowledge of target circuit. AFDS-SPICE also generates accurate fault coverage statistics that are tied to the circuit specifications. With employing a dictionary diagnosis method based on node-signalcharacters and regular BPNN algorithm, more accurate and effective diagnosis results are available for analog circuit with tolerance.

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

    KAUST Repository

    Busbait, Monther I.

    2014-05-01

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

  10. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection.

    Science.gov (United States)

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-07-17

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

  11. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Karthikeyan Elangovan

    2017-10-01

    Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

  12. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

    Science.gov (United States)

    Sun, Wenqing; Zheng, Bin; Qian, Wei

    2017-10-01

    This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

  13. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal.

    Science.gov (United States)

    Cerrada, Mariela; Vinicio Sánchez, René; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-09-18

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

  14. ALA-based fluorescent diagnosis of malignant oral lesions in the presence of bacterial porphyrin formation

    Science.gov (United States)

    Schleier, P.; Berndt, A.; Zinner, K.; Zenk, W.; Dietel, W.; Pfister, W.

    2006-02-01

    The aminolevulinic acid (5-ALA) -based fluorescence diagnosis has been found to be promising for an early detection and demarcation of superficial oral squamous cell carcinomas (OSCC). This method has previously demonstrated high sensitivity, however this clinical trial showed a specificity of approximately 62 %. This specificity was mainly restricted by tumor detection in the oral cavity in the presence of bacteria. After topical ALA application in the mouth of patients with previously diagnosed OSSC, red fluorescent areas were observed which did not correlate to confirm histological findings. Swabs and plaque samples were taken from 44 patients and cultivated microbiologically. Fluorescence was investigated (OMA-system) from 32 different bacteria strains found naturally in the oral cavity. After ALA incubation, 30 of 32 strains were found to synthesize fluorescent porphyrins, mainly Protoporphyrin IX. Also multiple fluorescent spectra were obtained having peak wavelengths of 636 nm and around 618 nm - 620 nm indicating synthesis of different porphyrins, such as the lipophylic Protoporphyrin IX (PpIX) and hydrophylic porphyrins (water soluble porphyrins, wsp). Of the 32 fluorescent bacterial strains, 18 produced wsp, often in combination with PpIX, and 5 produced solely wsp. These results clarify that ALA-based fluorescence diagnosis without consideration or suppression of bacteria fluorescence may lead to false-positive findings. It is necessary to suppress bacteria fluorescence with suitable antiseptics before starting the procedure. In this study, when specific antiseptic pre-treatment was performed bacterial associated fluorescence was significantly reduced.

  15. Fault detection and diagnosis in nonlinear systems a differential and algebraic viewpoint

    CERN Document Server

    Martinez-Guerra, Rafael

    2014-01-01

    The high reliability required in industrial processes has created the necessity of detecting abnormal conditions, called faults, while processes are operating. The term fault generically refers to any type of process degradation, or degradation in equipment performance because of changes in the process's physical characteristics, process inputs or environmental conditions. This book is about the fundamentals of fault detection and diagnosis in a variety of nonlinear systems which are represented by ordinary differential equations. The fault detection problem is approached from a differential algebraic viewpoint, using residual generators based upon high-gain nonlinear auxiliary systems (‘observers’). A prominent role is played by the type of mathematical tools that will be used, requiring knowledge of differential algebra and differential equations. Specific theorems tailored to the needs of the problem-solving procedures are developed and proved. Applications to real-world problems, both with constant an...

  16. Qualitative Event-based Diagnosis with Possible Conflicts: Case Study on the Third International Diagnostic Competition

    Data.gov (United States)

    National Aeronautics and Space Administration — We describe two model-based diagnosis algo- rithms entered into the Third International Diag- nostic Competition. We focus on the first diag- nostic problem of the...

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

    Directory of Open Access Journals (Sweden)

    Wentao Huang

    2017-06-01

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

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

    Science.gov (United States)

    Huang, Wentao; Sun, Hongjian; Wang, Weijie

    2017-06-03

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

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

    Science.gov (United States)

    Cao, Yan; Sun, Fengru

    2018-01-01

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

  20. A broadband multimedia collaborative system for advanced teleradiology and medical imaging diagnosis.

    Science.gov (United States)

    Gómez, E J; del Pozo, F; Ortiz, E J; Malpica, N; Rahms, H

    1998-09-01

    This paper presents a new telemedicine system currently in routine clinical usage, developed within the European Union (EU) ACTS BONAPARTE project (1). The telemedicine system is developed on an asynchronous transfer mode (ATM) multimedia hardware/software platform comprising the following set of telemedicine services: synchronous cooperative work, high-quality video conference, multimedia mail, medical image digitizing, processing, storing and printing, and local and remote transparent database access. The medical information handled by the platform conforms to the Digital Imaging and Communications in Medicine (DICOM) 3.0 medical imaging standard. The telemedicine system has been installed for clinical routines in three Spanish hospitals since November 1997 and has been used in an average of one/two clinical sessions per week. At each clinical session, a usability and clinical evaluation of the system was carried out. Evaluation is carried out through direct observation of interactions and questionnaire-based subjective data. The usability evaluation methodology and the results of the system usability study are also presented in this article. The experience gained from the design, development, and evaluation of the telemedicine system is providing an indepth knowledge of the benefits and difficulties involved in the installation and clinical usage of this type of high-usability and advanced multimedia telemedicine system in the field of teleradiology and collaborative medical imaging diagnosis.