Knowledge representation for diagnosis of care problems through an expert system: Model of the auto-care deficit situations

The caregiver role strain is today an increasing problem because of the population aging; moreover, its diagnosis is highly difficult. In this article, we summarize the design of an Expert System for the diagnosis of this health problem. The Expert System Knowledge Base is composed by a set of production rules written in classic bi-valued logic and by a set of potential facts. In order to build this Knowledge Base it has been necessary to design previously a Model of the problem treatment. The Expert System Inference Engine uses Gröbner Bases and Normal Form to obtain the diagnosis from the information stored in the Knowledge Base. Furthermore, a Graphic User’s Interface has been implemented to make easier the access to the System by all kinds of users.     

数码监控系统磁盘阵列的扩展及数据优化管理

近年来,数码相机监控系统以其高分辨率、方便、快捷、处理软件丰富以及高性能比等优点,愈来愈受到人们的关注。但是该系统仍然存在着一系列有待继续研究的问题,比如存储数据量大、不易管理等。针对以上不足,在介绍数码相机监控系统软、硬件的设计和开发过程同时,着重讨论数码相机监控系统磁盘阵列存储系统的扩展和图像采集数据的管理等问题。     

Evaluation of the safety and usability of touch gestures in operating in-vehicle information systems with visual occlusion

Nowadays, many automobile manufacturers are interested in applying the touch gestures that are used in smart phones to operate their in-vehicle information systems (IVISs). In this study, an experiment was performed to verify the applicability of touch gestures in the operation of IVISs from the viewpoints of both driving safety and usability. In the experiment, two devices were used: one was the Apple iPad, with which various touch gestures such as flicking, panning, and pinching were enabled; the other was the SK EnNavi, which only allowed tapping touch gestures. The participants performed the touch operations using the two devices under visually occluded situations, which is a well-known technique for estimating load of visual attention while driving.     

Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria

We describe an approach of automatic feature extraction for shape characterization of seven distinct species of Eimeria, a protozoan parasite of domestic fowl. We used digital images of oocysts, a round-shaped stage presenting inter-specific variability. Three groups of features were used: curvature characterization, size and symmetry, and internal structure quantification. Species discrimination was performed with a Bayesian classifier using Gaussian distribution. A database comprising 3891 micrographs was constructed and samples of each species were employed for the training process. The classifier presented an overall correct classification of 85.75%. Finally, we implemented a real-time diagnostic tool through a web interface, providing a remote diagnosis front-end.     

熵在随机系统故障诊断和容错控制中的应用

长期以来,关于随机动态系统的故障诊断和容错控制的研究一直是控制理论和应用的重要领域之一.随机控制系统故障诊断的目标是建立有效的故障估计算法以使残差信号方差最小这种方法仅适用于高斯型残差或者具有对称分布的概率密度函数的残差.然而,对非高斯残差而言,仅使用残差信号的方差不能够全面表示残差的不确定性.针对非高斯随机动态控制系...     

Using Signal Detection Theory and Time Window-based Human-In-The-Loop simulation as a tool for assessing the effectiveness of different qualitative shapes in continuous monitoring tasks

This paper provides a case study of Signal Detection Theory (SDT) as applied to a continuous monitoring dual-task environment. Specifically, SDT was used to evaluate the independent contributions of sensitivity and bias to different qualitative gauges used in process control. To assess detection performance in monitoring the gauges, we developed a Time Window-based Human-In-The-Loop (TWHITL) simulation bed. Through this test bed, we were able to generate a display similar to those monitored by console operators in oil and gas refinery plants. By using SDT and TWHITL, we evaluated the sensitivity, operator bias, and response time of flow, level, pressure, and temperature gauge shapes developed by Abnormal Situation Management^® (ASM^®) Consortium (www.asmconsortium.org). Our findings suggest that display density influences the effectiveness of participants in detecting abnormal shapes. Furthermore, results suggest that some shapes elicit better detection performance than others.     

Soft computing agents for e-Health in application to the research and control of unknown diseases

This paper presents an ontology-based holonic diagnostic system (OHDS) that combines the holonic paradigm with multi-agent system technology and ontology design, for the organization of unstructured biomedical research into structured disease information. We use ontologies as ‘brain’ for the holonic diagnostic system to enhance its ability to structure information in a meaningful way and share information fast. To integrate distributed heterogeneous knowledge available on the Web we use intelligent agents augmented by the mechanisms of the technology of fuzzy sets, which automatically structures the information in the adequate ontology template. Our vision of how this system implementation should be supported by a solid security shield that ensures the privacy and safety of medical information concludes the paper.     

Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery

A new object-oriented segmentation approach with special focus on shape analysis was developed for the extraction of large, man-made objects, especially agricultural fields, in high-resolution panchromatic satellite imagery. The approach, a combination of region- and edge-based techniques, includes new methods for the evaluation of straight edges, edge preserving degradation, and edge-guided region growing.     

Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors

This study aimed to evaluate the value of using 3-D breast MRI morphologic features to differentiate benign and malignant breast lesions. The 3-D morphological features extracted from breast MRI were used to analyze the malignant likelihood of tumor from ninety-five solid breast masses (44 benign and 51 malignant) of 82 patients. Each mass-like lesion was examined with regards to three categories of morphologic features, including texture-based gray-level co-occurrence matrix (GLCM) feature, shape, and ellipsoid fitting features. For obtaining a robust combination of features from different categories, the biserial correlation coefficient (|r_pb|) ≧ 0.4 was used as the feature selection criterion. Receiver operating characteristic (ROC) curve was used to evaluate performance and Student's t-test to verify the classification accuracy. The combination of the selected 3-D morphological features, including conventional compactness, radius, spiculation, surface ratio, volume covering ratio, number of inside angular regions, sum of number of inside and outside angular regions, showed an accuracy of 88.42% (84/95), sensitivity of 88.24% (45/51), and specificity of 88.64% (39/44), respectively. The A_Z value was 0.8926 for these seven combined morphological features. In conclusion, 3-D MR morphological features specified by GLCM, tumor shape and ellipsoid fitting were useful for differentiating benign and malignant breast masses.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

基于模糊神经网络和证据理论的列车智能控制系统多信息融合故障诊断研究

针对列车智能控制系统故障诊断中的多故障特征信息输入时的时变、冗余、不确定性和空间分布性,给出了一种列车智能控制系统多信息融合故障诊断的系统结构。讨论了采用模糊神经网络进行特征层融合和证据理论进行决策层融合相结合的列车智能控制系统多信息融合故障诊断方法。故障诊断实例的结果表明:该方法能够有效地提高诊断的可信度,减小诊断的不确定性。     

Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines

This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMMs, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.     

An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design

Evolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing optimisers that adapt to the characteristics of the problem being solved. In order to select appropriate operators for such optimisers it is necessary to first understand the interaction between operator and problem. This paper explores the concept of EA operator behaviour in real world applications through the empirical study of performance using water distribution networks (WDN) as a case study. Artificial networks are created to embody specific WDN features which are then used to evaluate the impact of network features on operator performance. The method extracts key attributes of the problem which are encapsulated in the natural features of a WDN, such as topologies and assets, on which different EA operators can be tested. The method is demonstrated using small exemplar networks designed specifically so that they isolate individual features. A set of operators are tested on these artificial networks and their behaviour characterised. This process provides a systematic and quantitative approach to establishing detailed information about an algorithm's suitability to optimise certain types of problem. The experiment is then repeated on real-world inspired networks and the results are shown to fit with the expected results.     

On Taylor series expansions for waiting times in tandem queues: an algorithm for calculating the coefficients and an investigation of the approximation error

Recently, a Taylor series expansion was developed for expected stationary waiting times in open (max,+)-linear stochastic systems with Poisson input process; these systems cover various instances of queueing networks.As an application, we present an algorithm for calculating the coefficients for infinite capacity tandem queueing networks with discrete service-time distributions. The algorithm works quite efficiently if the random vector of the service times of all servers is concentrated at a small number of atoms. We investigate the relative error of the Taylor approximation by simulation; in many cases, it follows very well a simple expression which holds exactly for independent, exponentially distributed servers.