Task-based image annotation and retrieval

In order to address problems of information overload in digital imagery task domains we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. The approach allows us to gauge a measure of a user's intentions as they complete goal-directed image tasks. As users analyze retrieved imagery their interactions are captured and an expert task context is dynamically constructed. This human expertise, proficiency, and knowledge can then be leveraged to support other users in carrying out similar domain tasks. We have applied our techniques to two multimedia retrieval applications for two different image domains, namely the geo-spatial and medical imagery domains. © Springer-Verlag Berlin Heidelberg 2007.

fuzzySim: Fuzzy similarity in species distributions

fuzzySim is an R package for calculating fuzzy similarity in species occurrence patterns. It includes functions for data preparation, such as converting species lists (long format) to presence-absence tables (wide format), obtaining unique abbreviations of species names, or transposing (parts of) complex data frames; and sample data sets for providing practical examples. It can convert binary presence-absence to fuzzy occurrence data, using e.g. trend surface analysis, inverse distance interpolation or prevalence-independent environmental favourability modelling, for multiple species simultaneously. It then calculates fuzzy similarity among (fuzzy) species distributions and/or among (fuzzy) regional species compositions. Currently available similarity indices are Jaccard, Sørensen, Simpson, and Baroni-Urbani & Buser.

水泥回转窑热工控制模型研究及其仿真

水泥回转窑是建材工业发展的方向，我国是水泥生产大国，而国内回转窑与发达国家相差甚大，尤其在热工控制方面。由于水泥回转窑具有时变、分布参数和非线性特性，是一个典型的复杂过程，因而水泥回转窑控制系统是一个很有意义且困难的研究方向，本论文在借鉴国内外同类研究的基础上，提出了模糊专家系统控制模型，进行了深入地研究，并且对该模型进行了计算机仿真，希望通过这项研究，提高我国在水泥回转窑先进智能技术的控制水平。主要研究内容有：对水泥回转窑的热工过程进行了详细分析，对其不同控制方法进行全面的综述，对水泥回转窑实现控制的人工智能方法进行了全面的综述，并介绍了国内外的研究现状；研究了对水泥回转窑控制的模糊控制模型、专家系统设计方法，以及利用模糊控制与专家系统相结合的方法对水泥回转窑进行安全而有效控制的方法：研究了专家系统的实时性问题，提出了静态排列专家系统的推理时间模型、优化排列专家系统的时间估计模型与排列准则；利用计算机仿真方法，实现对水泥回转窑这种复杂而昂贵系统控制进行实验研究，以较低的代价实现对其分析。本论文的主研究成果如下：1. 详细研究了水泥回转窑的技术发展与结构演化过程，分析了水泥回转窑的热工过程以及影响水泥生产的各种因素，总结了影响水泥生产质量的主要因素与次要因素，确定了控制水泥回转窑的主要并且可测量的过程参数。2. 用推理全成方法研究模糊控制模型，实现从模糊的角度研究水泥回转窑的控制：从专家 系统角度研究水泥回转窑的控制问题，并提取了有关的专家系统控制规则；在模糊控制与专家系统的基础上，将水泥回转窑的模糊控制与专家系统相结合，利用层次化的控制器结构，底层为模糊控制器，顶层为专家系统，实现了水泥回转窑的安全与有效控制。3. 从定量的角度研究了专家系统的推理时间问题，给出了三种相应的时间估计模型，这不仅可以分析水泥回转窑系统中的专家系统的实时性，而且也可以分析一般专家系统的推理时间和问题。4. 本文提出的计算机仿真工具，为三组数据分别进行计算机仿真，以此研究水泥回转窑控制策略的性能以及对其动态过程进行分析，为水泥回转窑这样的复杂且昂贵的控制系统研究提供有效的手段。

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

Performing Feature Selection with ACO

R. Jensen, 'Performing Feature Selection with ACO. Swarm Intelligence and Data Mining,' A. Abraham, C. Grosan and V. Ramos (eds.), Studies in Computational Intelligence, vol. 34, pp. 45-73. 2006.

Finding Rough Set Reducts with SAT.

R. Jensen, Q. Shen and A. Tuson, 'Finding Rough Set Reducts with SAT,' Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, LNAI 3641, pp. 194-203, 2005.

Utilidad de los signos físicos en el diagnóstico de neumonía. ¿es necesaria la placa de tórax?

Objetivos : evaluar las características operativas del examen físico en el diagnóstico de neumonía y evaluar su acuerdo inter-observador. Marco de referencia : los estudios que avaluaron al examen físico como prueba diagnóstica en neumonía son metodológicamente deficientes. Diseño : estudio ciego de corte transversal para evaluación de prueba diagnóstica. Pacientes : adultos quienes consultan al servicio de urgencias y hospitalización de la FCI por síntomas respiratorios agudos o exacerbación de los mismos. Mediciones : examen físico por dos observadores independientes, toma de radiografía de tórax y lectura por radiólogo experto. Se tomaron los datos que permitieron calcular el índice de severidad de neumonía (PSI). Resultados : de 198 pacientes, 85(42%) tenían neumonía radiográficamente. Las características operativas del examinador1 fueron: Sensibilidad:63.2%, Especificidad:54,1%, LR(+)=1,36, LR(-)=0,68; para el examinador2: Sensibilidad:34,3%, Especificidad:71,7%, LR(+)=1,17, LR(-)=0,92. La correlación entre diagnóstico clínico para derrame pleural fue k=-0,052, no significativa (p=0,445); y para neumonía k=0.25 significativa (p=0.022). Al medirse la severidad de neumonía por PSI, la sensibilidad aumento estratificada a severidad (II:Sensibilidad:40%; III:Sensibilidad: 57%; IV:Sensibilidad;75%; V:Sensibilidad:80%). Conclusiones : el examen físico no es sensible ni especifico en el diagnóstico de neumonía. Existe un índice de acuerdo débil en el examen físico de tórax para el diagnóstico de derrame pleural y neumonía Es más probable el diagnóstico clínico de neumonía al aumentar la severidad por PSI.

Previsão em tempo atual de cheias com uso de sistema especialista difuso

Sistemas de previsão de cheias podem ser adequadamente utilizados quando o alcance é suficiente, em comparação com o tempo necessário para ações preventivas ou corretivas. Além disso, são fundamentalmente importantes a confiabilidade e a precisão das previsões. Previsões de níveis de inundação são sempre aproximações, e intervalos de confiança não são sempre aplicáveis, especialmente com graus de incerteza altos, o que produz intervalos de confiança muito grandes. Estes intervalos são problemáticos, em presença de níveis fluviais muito altos ou muito baixos. Neste estudo, previsões de níveis de cheia são efetuadas, tanto na forma numérica tradicional quanto na forma de categorias, para as quais utiliza-se um sistema especialista baseado em regras e inferências difusas. Metodologias e procedimentos computacionais para aprendizado, simulação e consulta são idealizados, e então desenvolvidos sob forma de um aplicativo (SELF – Sistema Especialista com uso de Lógica “Fuzzy”), com objetivo de pesquisa e operação. As comparações, com base nos aspectos de utilização para a previsão, de sistemas especialistas difusos e modelos empíricos lineares, revelam forte analogia, apesar das diferenças teóricas fundamentais existentes. As metodologias são aplicadas para previsão na bacia do rio Camaquã (15543 km2), para alcances entre 10 e 48 horas. Dificuldades práticas à aplicação são identificadas, resultando em soluções as quais constituem-se em avanços do conhecimento e da técnica. Previsões, tanto na forma numérica quanto categorizada são executadas com sucesso, com uso dos novos recursos. As avaliações e comparações das previsões são feitas utilizandose um novo grupo de estatísticas, derivadas das freqüências simultâneas de ocorrência de valores observados e preditos na mesma categoria, durante a simulação. Os efeitos da variação da densidade da rede são analisados, verificando-se que sistemas de previsão pluvio-hidrométrica em tempo atual são possíveis, mesmo com pequeno número de postos de aquisição de dados de chuva, para previsões sob forma de categorias difusas.

Data-driven fuzzy rule generation and its application for student academic performance evaluation

K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Applied Intelligence, 25(3):305-319, 2006.

Fuzzy data envelopment analysis:a discrete approach

Data envelopment analysis (DEA) as introduced by Charnes, Cooper, and Rhodes (1978) is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input-output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on a-cut. One drawback of the a-cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the a-cut approach. We introduce the concept of "local a-level" to develop a multi-objective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.