UM MODELO FUZZY-DEA-GAME PARA ESTRATÉGIAS DE PRODUÇÃO SOB INCERTEZA

Este trabalho desenvolve um novo modelo Fuzzy-DEA-Game (FDG) para apoiar o estabelecimento de estratégias de produção. Esse modelo combina a Análise Envoltória de Dados (DEA) com conceitos da Teoria dos Conjuntos Fuzzy e do Jogo da Barganha de Nash. O modelo permite uma avaliação da eficiência produtiva e econômica dos produtos, o que pode resultar num portfólio de produtos mais rentáveis e de interesse do mercado consumidor. O modelo foi aplicado em uma empresa do segmento de energia. Os resultados obtidos com a aplicação do modelo FDG mostraram-se aderentes à realidade da empresa estudada e forneceram metas para a redução dos níveis de recursos (entradas) necessários para a fabricação dos produtos e para aumento dos níveis de resultados (saídas) oriundos da comercialização desses produtos. Como resultado adicional importante, o modelo FDG permitiu a identificação dos produtos do portfólio que são mais sensíveis à ocorrência de incerteza.

Compression Efficiency Analysis of Wyner-Ziv Video Coding with Motion Compensated Side Information Interpolation

The Wyner-Ziv video coding (WZVC) rate distortion performance is highly dependent on the quality of the side information, an estimation of the original frame, created at the decoder. This paper, characterizes the WZVC efficiency when motion compensated frame interpolation (MCFI) techniques are used to generate the side information, a difficult problem in WZVC especially because the decoder only has available some reference decoded frames. The proposed WZVC compression efficiency rate model relates the power spectral of the estimation error to the accuracy of the MCFI motion field. Then, some interesting conclusions may be derived related to the impact of the motion field smoothness and the correlation to the true motion trajectories on the compression performance.

Fuzzy linguistic model for evaluating the risk of neonatal death

OBJECTIVE: To introduce a fuzzy linguistic model for evaluating the risk of neonatal death. METHODS: The study is based on the fuzziness of the variables newborn birth weight and gestational age at delivery. The inference used was Mamdani's method. Neonatologists were interviewed to estimate the risk of neonatal death under certain conditions and to allow comparing their opinions and the model values. RESULTS: The results were compared with experts' opinions and the Fuzzy model was able to capture the expert knowledge with a strong correlation (r=0.96). CONCLUSIONS: The linguistic model was able to estimate the risk of neonatal death when compared to experts' performance.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Decision support system with incomplete and domain incoherent information management

Many of the most common human functions such as temporal and non-monotonic reasoning have not yet been fully mapped in developed systems, even though some theoretical breakthroughs have already been accomplished. This is mainly due to the inherent computational complexity of the theoretical approaches. In the particular area of fault diagnosis in power systems however, some systems which tried to solve the problem, have been deployed using methodologies such as production rule based expert systems, neural networks, recognition of chronicles, fuzzy expert systems, etc. SPARSE (from the Portuguese acronym, which means expert system for incident analysis and restoration support) was one of the developed systems and, in the sequence of its development, came the need to cope with incomplete and/or incorrect information as well as the traditional problems for power systems fault diagnosis based on SCADA (supervisory control and data acquisition) information retrieval, namely real-time operation, huge amounts of information, etc. This paper presents an architecture for a decision support system, which can solve the presented problems, using a symbiosis of the event calculus and the default reasoning rule based system paradigms, insuring soft real-time operation with incomplete, incorrect or domain incoherent information handling ability. A prototype implementation of this system is already at work in the control centre of the Portuguese Transmission Network.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outages

This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Research on evaluation system of emergency disposal plan for power plant incidents

This paper starts with the analysis of the unusual inherence mechanism, from two aspects: accumulating and human error. We put forward twelve factors affected the decision of the emergency treatment plan in practice and summarized the evaluation index system combining with literature data. Then we screened out eighteen representative indicators by used the FDM expert questionnaire in the first phase. Hereafter, we calculated the weight of evaluation index and sorted them by the FAHP expert questionnaire, and came up with the frame of the evaluation rule by combined with the experience. In the end, the evaluation principles are concluded.

Lógica fuzzy e regressão logística na decisão para prática de cintilografia das paratiróides

OBJETIVO: Desenvolver e comparar dois modelos matemáticos, um deles baseado em regressão logística e o outro em teoria de conjuntos fuzzy, para definir a indicação para a realização do exame cintilográfico a partir de resultados dos exames laboratoriais. MÉTODOS: Foram identificados 194 pacientes que tiveram cálcio e paratormônio séricos medidos a partir da base de registros de cintilografia de paratiróides realizadas em laboratório de diagnóstico de São Paulo, no período de janeiro de 2000 a dezembro de 2004. O modelo de regressão logística foi desenvolvido utilizando-se o software SPSS e o modelo fuzzy, o Matlab. A performance dos modelos foi comparada utilizando-se curvas ROC. RESULTADOS: Os modelos apresentaram diferenças estatisticamente significantes (p=0,026) nos seus desempenhos. A área sob a curva ROC do modelo de regressão logística foi de 0,862 (IC 95%: 0,811-0,913) e do modelo de lógica fuzzy foi 0,887 (IC 95%: 0,840-0,933). Este último destacou-se como particularmente útil porque, ao contrário do modelo logístico, mostrou capacidade de utilizar informações de paratormônio em intervalo em que os valores de cálcio mostraram-se pouco discriminantes. CONCLUSÕES: O modelo matemático baseado em teoria de conjuntos fuzzy pareceu ser mais adequado do que o baseado em regressão logística como método para decisão da realização de cintilografia das paratiróides. Todavia, sendo resultado de um exercício metodológico, inferências sobre o comportamento do objeto podem ser impróprias, dada a não representatividade populacional dos dados.

Image upsampling algorithms applied on Nuclear Medicine: comparision of hqnx and nxSaI algorithms families with two interpolation algorithms - nearest neighbour and bicubic interpolation

Introduction: Image resizing is a normal feature incorporated into the Nuclear Medicine digital imaging. Upsampling is done by manufacturers to adequately fit more the acquired images on the display screen and it is applied when there is a need to increase - or decrease - the total number of pixels. This paper pretends to compare the “hqnx” and the “nxSaI” magnification algorithms with two interpolation algorithms – “nearest neighbor” and “bicubic interpolation” – in the image upsampling operations. Material and Methods: Three distinct Nuclear Medicine images were enlarged 2 and 4 times with the different digital image resizing algorithms (nearest neighbor, bicubic interpolation nxSaI and hqnx). To evaluate the pixel’s changes between the different output images, 3D whole image plot profiles and surface plots were used as an addition to the visual approach in the 4x upsampled images. Results: In the 2x enlarged images the visual differences were not so noteworthy. Although, it was clearly noticed that bicubic interpolation presented the best results. In the 4x enlarged images the differences were significant, with the bicubic interpolated images presenting the best results. Hqnx resized images presented better quality than 4xSaI and nearest neighbor interpolated images, however, its intense “halo effect” affects greatly the definition and boundaries of the image contents. Conclusion: The hqnx and the nxSaI algorithms were designed for images with clear edges and so its use in Nuclear Medicine images is obviously inadequate. Bicubic interpolation seems, from the algorithms studied, the most suitable and its each day wider applications seem to show it, being assumed as a multi-image type efficient algorithm.