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Detecção e diagnóstico de falhas em robôs manipuladores via redes neurais artificiais.; Fault detection and diagnosis in robotic manipulators via artificial neural networks.

Tinós, Renato
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 11/02/1999 PT
Relevância na Pesquisa
45.82%
Neste trabalho, um novo enfoque para detecção e diagnóstico de falhas (DDF) em robôs manipuladores é apresentado. Um robô com falhas pode causar sérios danos e pode colocar em risco o pessoal presente no ambiente de trabalho. Geralmente, os pesquisadores têm proposto esquemas de DDF baseados no modelo matemático do sistema. Contudo, erros de modelagem podem ocultar os efeitos das falhas e podem ser uma fonte de alarmes falsos. Aqui, duas redes neurais artificiais são utilizadas em um sistema de DDF para robôs manipuladores. Um perceptron multicamadas treinado por retropropagação do erro é usado para reproduzir o comportamento dinâmico do manipulador. As saídas do perceptron são comparadas com as variáveis medidas, gerando o vetor de resíduos. Em seguida, uma rede com função de base radial é usada para classificar os resíduos, gerando a isolação das falhas. Quatro algoritmos diferentes são empregados para treinar esta rede. O primeiro utiliza regularização para reduzir a flexibilidade do modelo. O segundo emprega regularização também, mas ao invés de um único termo de penalidade, cada unidade radial tem um regularização individual. O terceiro algoritmo emprega seleção de subconjuntos para selecionar as unidades radiais a partir dos padrões de treinamento. O quarto emprega o mapa auto-organizável de Kohonen para fixar os centros das unidades radiais próximos aos centros dos aglomerados de padrões. Simulações usando um manipulador com dois graus de liberdade e um Puma 560 são apresentadas...

Análise e reconhecimento digital de formas biológicas para o diagnóstico automático de parasitas do gênero Eimeria; Biological shape analysis and digital recognition for the automatic diagnosis of parasites of the genus Eimeria

Castañon, Cesar Armando Beltran
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 16/01/2007 PT
Relevância na Pesquisa
45.92%
O gênero Eimeria compreende um grupo de protozoários da classe Coccidia que infecta uma grande variedade de hospedeiros. Um total de sete espécies distintas Eimeria podem infectar a galinha doméstica causando enterites com graves prejuízos econômicos. A identificação das espécies pode ser feita através da análise microscópica das diferentes características morfológicas dos oocistos, um dos estágios de desenvolvimento do parasita. Alternativamente, ensaios moleculares baseados na amplificação de alvos específicos de DNA também podem ser utilizados. Em ambos os casos, requer-se um laboratório especializado e, principalmente, pessoal altamente treinado. Neste trabalho é relatada uma abordagem computacional para a extração automática de características para a representação da forma das distintas espécies de Eimeria. Foram utilizadas imagens digitais do protozoário nas quais aplicou-se técnicas de processamento de imagens e visão computacional para sua representação morfológica, formando três grupos de características: medidas geométricas, caracterização da curvatura, e quantificação da estrutura interna. A morfologia dos protozoários foi representada por um vetor de características constituído por 14 dimensões...

A new adaptive analog test and diagnosis system

Cota, Erika Fernandes; Negreiros, Marcelo; Carro, Luigi; Lubaszewski, Marcelo Soares
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Artigo de Revista Científica Formato: application/pdf
ENG
Relevância na Pesquisa
55.86%
This paper presents a low-cost analog test system with diagnosis capabilities. The tester is able to detect faults in any linear circuit by learning a reference circuit behavior in a first step, and comparing this behavior against the output of the circuit under test in a second step. For a faulty circuit, a third step takes place to locate the fault. The diagnosis method consists on injecting probable faults in a mathematical model of the circuit, and later comparing its output with the output of the real faulty circuit. This system has been successfully applied to a case study, a biquad filter. Soft, large, and hard deviations on components, as well as faults in operational amplifiers, were considered. Experimental results have proven the feasibility and efficiency of the proposed test and diagnosis system.

An expert system for supporting Traditional Chinese Medicine diagnosis and treatment

Silva, Paulo; Gago, Pedro; Ribeiro, José Carlos Bregieiro; Santos, Manuel Filipe; Portela, Filipe; Abelha, António; Machado, José Manuel; Pinto, Filipe
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Publicado em //2014 ENG
Relevância na Pesquisa
45.72%
Portugal has recently become one the few European countries to fully acknowledge Traditional Chinese Medicine (TCM); this substantial paradigm shift calls for novel tools for TCM practitioners, students and patients alike. This paper describes an Expert System for supporting the TCM consultation process – both in terms of gathering and managing the patients’ personal and symptomatic data, and of obtaining accurate diagnoses and treatments under regulated and reviewed protocols. The proposed tool was designed and is being developed with the support of two TCM therapists, which act as experts and provide aid to the processes of building the knowledge base and the automatic diagnosis system. In terms of architecture, the current version of the framework includes a mobile client application for the Android platform, integrated with an online spreadsheet. A survey was conducted in order to assess some of the needs of the community of TCM practitioners, and allowed gathering information on their needs.

Breast US Computer-aided Diagnosis System: Robustness across Urban Populations in South Korea and the United States1

Gruszauskas, Nicholas P.; Drukker, Karen; Giger, Maryellen L.; Chang, Ruey-Feng; Sennett, Charlene A.; Moon, Woo Kyung; Pesce, Lorenzo L.
Fonte: Radiological Society of North America, Inc. Publicador: Radiological Society of North America, Inc.
Tipo: Artigo de Revista Científica
Publicado em /12/2009 EN
Relevância na Pesquisa
45.66%
In general, the breast US computer-aided diagnosis system appears to be effective across different patient populations, but further investigation is warranted.

Global product development : a framework for organizational diagnosis

Martínez, Víctor Takahiro Endo
Fonte: Massachusetts Institute of Technology Publicador: Massachusetts Institute of Technology
Tipo: Tese de Doutorado Formato: 128 leaves
ENG
Relevância na Pesquisa
45.63%
The main purpose of this thesis is to present an approach for analyzing product development organizations in a globalizing world. The fragmentation and distribution of several product development activities in the global market have generated a variety of strategies. In addition, an increasing visibility of the influence of cultural diversity in these strategies and an intensified sensitivity to sustainability issues motivate this research. Retaking the questions of which is the best strategy for product development organizations to succeed and, even further, which is the measure of success for these organizations are also part of the motivation behind the research. The methodology followed for constructing the socio-technical framework presented in this document mainly consisted of gathering, analyzing, and integrating existing literature and frameworks from systems engineering, social, and management studies. Utilizing a macro-framework with three spectra -space, time, and context- the framework allows the decomposition of the product development system into three levels, identifying the key stakeholders and roles within the system. The framework includes four different angles -structural, human resources, political, and symbolic- from which a product development organization can be diagnosed. Also...

Avaliação de classificadores na classificação de radiografias de tórax para o diagnóstico de pneumonia infantil; Classifiers evaluation in chest radiograph classification to childhood pneumonia diagnosis

Sousa, Rafael Teixeira
Fonte: Universidade Federal de Goiás; Brasil; UFG; Programa de Pós-graduação em Ciência da Computação (INF); Instituto de Informática - INF (RG) Publicador: Universidade Federal de Goiás; Brasil; UFG; Programa de Pós-graduação em Ciência da Computação (INF); Instituto de Informática - INF (RG)
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
45.65%
This work extends a Computer-Aided Diagnosis system called PneumoCAD for detecting pneumonia in infants using radiographic images, with the aim of improving the system’s accuracy, robustness and test the features previously extracted. We implement and compare five contemporary machine learning classifiers, namely: Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Decision Tree, combined with three dimensionality reduction algorithms: the feature selection wrapper Sequential Forward Elimination (SFE), and two feature filter algotithms: Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA). Current Results of demonstrate that the Naïve Bayes classifier combined with KPCA produces the best overall results. Also confirming the efficiency os features.; Avaliação de classificadores na classificação de radiografias de tórax para o diagnóstico de pneumonia infantil Este trabalho dá continuidade ao Sistema de Auxílio a Diagnóstico chamado de PneumoCAD para a detecção de pneumonia infantil por meio de imagens radiográficas, com o objetivo de aprimorar a acurácia, robustez e testar as características extraídas anteriormente. Nós implementamos cinco classificadores contemporâneos...

ODDIN: ontology-driven differential diagnosis based on logical inference and probabilistic refinements

García-Crespo, Ángel; Rodríguez, Alejandro; Mencke, Myriam; Gómez-Berbís, Juan Miguel; Colomo-Palacios, Ricardo
Fonte: Elsevier Publicador: Elsevier
Tipo: info:eu-repo/semantics/acceptedVersion; info:eu-repo/semantics/article Formato: application/pdf
Publicado em 15/03/2010 ENG
Relevância na Pesquisa
45.8%
Medical differential diagnosis (ddx) is based on the estimation of multiple distinct parameters in order to determine the most probable diagnosis. Building an intelligent medical differential diagnosis system implies using a number of knowledge based technologies which avoid ambiguity, such as ontologies rep resenting specific structured information, but also strategies such as computation of probabilities of var ious factors and logical inference, whose combination outperforms similar approaches. This paper presents ODDIN, an ontology driven medical diagnosis system which applies the aforementioned strat egies. The architecture and proof of concept implementation is described, and results of the evaluation are discussed.; This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI-020100-2008-564) and SONAR2 (TSI-020100-2008-665), under the PIBES project of the Spanish Committee of Education & Science (TEC2006-12365-C02-01) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140-C03-02).

The effects of cognitive rehabilitation on Alzheimer’s dementia patients’ cognitive assessment reference diagnosis system performance based on level of cognitive functioning

Hwang, Jung-Ha; Cha, Hyun-Gyu; Cho, Hyuk-Shin
Fonte: The Society of Physical Therapy Science Publicador: The Society of Physical Therapy Science
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.66%
[Purpose] The purpose of this study is to apply cognitive rehabilitation according to Alzheimer’s disease (AD) patients’ level of cognitive functioning to compare changes in Cognitive Assessment Reference Diagnosis System performance and present standards for effective intervention. [Subjects] Subjects were 30 inpatients diagnosed with AD. Subjects were grouped by Clinical Dementia Rating (CDR) class (CDR-0.5, CDR-1, or CDR-2, n = 10 per group), which is based on level of cognitive functioning, and cognitive rehabilitation was applied for 50 minutes per day, five days per week, for four weeks. [Methods] After cognitive rehabilitation intervention, CARDS tests were conducted to evaluate memory. [Results] Bonferroni tests comparing the three groups revealed that the CDR-0.5 and CDR-1 groups showed significant increases in Delayed 10 word-list, Delayed 10 object-list, Recognition 10 object, and Recent memory performance compared to the CDR-2 group. In addition, the CDR-0.5 group showed significant decreases in Recognition 10 word performance compared to the CDR-1 group. [Conclusion] Cognitive rehabilitation, CDR-0.5 or CDR-1 subjects showed significantly greater memory improvements than CDR-2 subjects. Moreover, was not effective for CDR-2 subjects.

Preliminary Evaluation of MDX—A Medical Diagnosis System*

Tatman, John L.; Smith, Jack W.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 02/11/1982 EN
Relevância na Pesquisa
45.72%
MDX, a prototype medical diagnosis system in the domain of cholestasis, was evaluated to see how it performed on cases of jaundice which contained incomplete data for making an etiologic diagnosis and to examine its current knowledge base. After briefly describing MDX and evaluation issues, the performance of several functions of MDX, including the control process, CHOLESTASIS specialist and suggestion rules, are discussed. At this stage of development, these functions appear to perform adequately. The evaluation provided additional information on MDX's performance and suggests modifications to be incorporated into future versions of MDX.

Availability of Tongue Diagnosis System for Assessing Tongue Coating Thickness in Patients with Functional Dyspepsia

Kim, Juyeon; Son, Jiyoung; Jang, Seungwon; Nam, Dong-Hyun; Han, Gajin; Yeo, Inkwon; Ko, Seok-Jae; Park, Jae-Woo; Ryu, Bongha; Kim, Jinsung
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.84%
Tongue diagnosis is an important procedure in traditional Korean medicine (TKM). In particular, tongue coating thickness (TCT) is deemed to show the progression of the disease. However, conventional tongue diagnosis has limitations because of various external factors. Therefore, it is necessary to investigate the availability of tongue diagnosis system (TDS) in the assessment of TCT. This study has been designed as a prospective clinical trial involving 60 patients with functional dyspepsia. Tongue images will be obtained by TDS twice with a 30 min interval. The system will measure the percentage of TCT and classify it as either no coating, thin coating, or thick coating according to the existing diagnostic criteria. After finishing the collection of 60 patients' tongue images, TCT on the images will be simultaneously evaluated by the conventional method to establish the gold standard for assessing TCT by 5 well-trained clinicians. The evaluation will be repeated by the same clinicians after 2 weeks, but the order of the images will be changed. This trial is expected to provide clinical evidence for the availability of TDS as a diagnostic tool and to contribute to the standardization of the diagnosis system used in TKM. This trial is registered with ClinicalTrials.gov NCT01864837.

An Expert Fitness Diagnosis System Based on Elastic Cloud Computing

Tseng, Kevin C.; Wu, Chia-Chuan
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Publicado em 02/03/2014 EN
Relevância na Pesquisa
45.7%
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.

The Study on the Agreement between Automatic Tongue Diagnosis System and Traditional Chinese Medicine Practitioners

Lo, Lun-chien; Chen, Yung-Fu; Chen, Wen-Jiuan; Cheng, Tsung-Lin; Chiang, John Y.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.76%
Tongue diagnosis is an important practice in traditional Chinese medicine (TCM) for diagnosing diseases before determining proper means of treatments. Traditionally, it depends solely on personal knowledge and experience of the practitioner, thereby being criticized as lacking of objectivity. Currently, no research regarding intra- and inter-agreements of automatic tongue diagnosis system (ATDS) and TCM doctors has been conducted. In this study, the ATDS is developed to extract a variety of tongue features and provide practitioners with objective information to assist diagnoses. To evaluate the ATDS clinical stability, 2 sets of tongue images taken 1 hour apart from 20 patients with possible variations in lighting and extruding tongue, are employed to investigate intra-agreement of the ATDS, intra-agreement of the TCM doctors, and the inter-agreement between the ATDS and TCM doctors. The ATDS is shown to be more consistent with significantly higher intra-agreement than the TCM doctors (kappa value: 0.93 ± 0.06 versus 0.64 ± 0.13) with P < 0.001 (Student's t-test). Inter-agreements between the ATDS and TCM doctors, as well as among the TCM doctors are both moderate. The high agreement of the ATDS can provide objective and reliable tongue features to facilitate doctor in making effective observation and diagnosis of specific diseases.

A generalized model for distributed comparison-based system-level diagnosis

Albini,Luiz Carlos Pessoa; Duarte Jr,Elias Procópio; Ziwich,Roverli Pereira
Fonte: Sociedade Brasileira de Computação Publicador: Sociedade Brasileira de Computação
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2005 EN
Relevância na Pesquisa
45.92%
This work introduces a new system-level diagnosis model and an algorithm based on this model: Hi-Comp (Hierarchical Comparison-based Adaptive Distributed System-Level Diagnosis algorithm). This algorithm allows the diagnosis of systems that can be represented by a complete graph. Hi-Comp is the first diagnosis algorithm that is, at the same time, hierarchical, distributed and comparison-based. The algorithm is not limited to crash fault diagnosis, because its tests are based on comparisons. To perform a test, a processor sends a task to two processors of the system that, after executing the task, send their outputs back to the tester. The tester compares the two outputs; if the comparison produces a match, the tester considers the tested processors fault-free; on the other hand, if the comparison produces a mismatch, the tester considers that at least one of the two tested processors is faulty, but can not determine which one. Considering a system of N nodes, it is proved that the algorithm's diagnosability is (N-1) and the latency is log2N testing rounds. Furthermore, a formal proof of the maximum number of tests required per testing round is presented, which can be O(N³). Simulation results are also presented.

Optimization of Fault-Insertion Test and Diagnosis of Functional Failures

Zhang, Zhaobo
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação
Publicado em //2011
Relevância na Pesquisa
35.95%

Advances in semiconductor technology and design automation methods have introduced a new era for electronic products. With design sizes in millions of logic gates and operating frequencies in GHz, defects-per-million rates continue to increase, and defects are manifesting themselves in subtle ways. Traditional test methods are not sufficient to guarantee product quality and diagnostic programs cannot rapidly locate the root cause of failure in large systems. Therefore, there is a need for efficient fault diagnosis methods that can provide quality assurance, accelerate new product release, reduce manufacturing cost, and increase product yield.

This thesis research is focused on fault-insertion test (FIT) and fault diagnosis at the board and system levels. FIT is a promising technique to evaluate system reliability and facilitate fault diagnosis. The error-handling mechanism and system reliability can be assessed in the presence of intentionally inserted faults, and artificial faulty scenarios can be used as references for fault diagnosis. However, FIT needs to be deployed under constraints of silicon area, design effort, availability of equipment, and what is actually possible to test from one design to the next. In this research...

Efficient Board-Level Functional-Fault Diagnosis with Missing Syndromes

Jin, Shi; Ye, Fangming; Zhang, Zhaobo; Chakrabarty, Krishnendu; Gu, Xinli
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Relatório
Publicado em 01/07/2015 EN_US
Relevância na Pesquisa
45.83%
Functional fault diagnosis is widely used in board manufacturing to ensure product quality and improve product yield. Advanced machine-learning techniques have recently been advocated for reasoning-based diagnosis; these techniques are based on the historical record of successfully repaired boards. However, traditional diagnosis systems fail to provide appropriate repair suggestions when the diagnostic logs are fragmented and some error outcomes, or syndromes, are not available during diagnosis. We describe the design of a diagnosis system that can handle missing syndromes and can be applied to four widely used machine-learning techniques. Several imputation methods are discussed and compared in terms of their effectiveness for addressing missing syndromes. Moreover, a syndrome-selection technique based on the minimumredundancy- maximum-relevance (mRMR) criteria is also incorporated to further improve the efficiency of the proposed methods. Two large-scale synthetic data sets generated from the log information of complex industrial boards in volume production are used to validate the proposed diagnosis system in terms of diagnosis accuracy and training time.; This research was supported by a grant from Huawei Technologies Co. Ltd.

Knowledge-Driven Board-Level Functional Fault Diagnosis

Ye, Fangming
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação
Publicado em //2014
Relevância na Pesquisa
45.99%

The semiconductor industry continues to relentlessly advance silicon technology scaling into the deep-submicron (DSM) era. High integration levels and structured design methods enable complex systems that can be manufactured in high volume. However, due to increasing integration densities and high operating speeds, subtle manifestation of defects leads to functional failures at the board level. Functional fault diagnosis is, therefore, necessary for board-level product qualification. However, ambiguous diagnosis results can lead to long debug times and wrong repair actions, which significantly increase repair cost and adversely impact yield.

A state-of-the-art diagnosis system involves several key components: (1) design of functional test programs, (2) collection of functional-failure syndromes, (3) building of the diagnosis engine, (4) isolation of root causes, and (5) evaluation of the diagnosis engine. Advances in each of these components can pave the way for a more effective diagnosis system, thus improving diagnosis accuracy and reducing diagnosis time. Machine-learning techniques offer an unprecedented opportunity to develop an automated and adaptive diagnosis system to increase diagnosis accuracy and speed. This dissertation targets all the above components of an advanced diagnosis system by leveraging various machine-learning techniques.

This thesis first describes a diagnosis system based on support-vector machines (SVMs)...

Diagnosis expert system with automated query to a process control system

Winslow, Richard
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
45.83%
This thesis describes the development of a diagnosis expert system for an automated process control facility. To reduce the number of user responses to the expert system, a network interface was created between the expert system and the process control computer. This document focuses on the unique concerns associated with the development, validation, and implementation of an expert system that is directly interfaced to a process control system.

Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization

Sahin, Ferat; Yavuz, M. Cetin; Arnavut, Ziya; Uluyol, Onder
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
EN_US
Relevância na Pesquisa
45.71%
This paper presents a fault diagnosis system for airplane engines using Bayesian networks (BN) and distributed particle swarm optimization (PSO). The PSO is inherently parallel, works for large domains and does not trap into local maxima. We implemented the algorithm on a computer cluster with 48 processors using message passing interface (MPI) in Linux. Our implementation has the advantages of being general, robust, and scalable. Unlike existing BN-based fault diagnosis methods, neither expert knowledge nor node ordering is necessary prior to the Bayesian Network discovery. The raw datasets obtained from airplane engines during actual flights are preprocessed using equal frequency binning histogram and used to generate Bayesian networks fault diagnosis for the engines. We studied the performance of the distributed PSO algorithm and generated a BN that can detect faults in the test data successfully.; RIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/

Diagnosis of clear sky ultraviolet radiation for Mexico

LEMUS-DESCHAMPS,L.; GALINDO,I.; SOLANO,R.; ELIZALDE,A. T.; FONSECA,J.
Fonte: Centro de Ciencias de la Atmósfera, UNAM Publicador: Centro de Ciencias de la Atmósfera, UNAM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2002 EN
Relevância na Pesquisa
45.65%
A discrete-ordinate radiative transfer model is employed to develop a regional clear sky ultraviolet (UV) diagnosis system. The clear sky UV radiation, weighted by the spectral sensitivity of human skin is calculated using the Total Ozone Mapping Spectrometer (TOMS) data sets. Examples of the geographical clear sky UV Index distributions are presented and the model results are compared with surface UV measurements from the University of Colima for 1999.