Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

Case‐based decision support for fault diagnosis: Do salient ratings discourage situation analysis?

In the processing industry, frequent faults call for assistance in diagnosis, and case‐based reasoning (CBR) can provide solutions applied by other operators in the past. This study investigated whether salient case ratings promote an uncritical acceptance of solutions. In 2 experiments, subjects diagnosed faults with a simulated CBR system, and ratings were presented in graphical or verbal format. In most trials, the case with the highest rating provided the correct solution, while in catch‐trials, it did not. Graphical ratings were hypothesized to speed up solutions but discourage cross‐checking and lead to errors in catch‐trials. These hypotheses were not confirmed, even though Experiment 2 maximized the incentive of relying on case ratings. While graphical ratings led subjects to start with the most highly rated case, they did not impair situation analysis and accuracy. The results suggest that during fault diagnosis people are not easily misled into overtrusting a CBR system.     

推理建模中基于KDD和粗糙集的案例修改

在应用基于案例推理技术进行智能建模时,案例修改后的案例质量好坏直接影响所建模型的精度,但是由于案例修改对领域知识的依赖性很强,采用一般手工案例修改方法尤法保证案例修改的质量,即无法保证智能推理模型的精度。基于以上原因,该文提出了一种新的案例修改方法,利用KDD技术,通过有效的多值关联规则挖掘算法从运行数据库中挖掘出案例各属性间的依赖关系,得到案例修改的基本关联规则集,在此基础上利用粗糙集理论对基本关联规则集进行简约,然后根据简约后的关联规则进行案例修改。在线对比实验证明,应用本文方法进行案例修改,提商了修改后的案例质量,从而提高了整体智能推理模型的精度。     

Hybrid job offer recommender system in a social network

Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.     

基于粗糙集和CBR的救灾口粮需求预测

救灾口粮预测所采用的方法多以专家经验判断为主,具有较大的随机性。为此,从灾害案例的特点出发,针对案例推理时存在效率低下和权重确定差异性较大的问题,结合粗糙集处理不确定知识的优点和案例推理的特点,提出一种方法,实现灾害应急救灾口粮需求预测,并通过洪涝灾害实例进行分析。结果表明该方法有利于减少主观影响,提高需求预测的准确率和效率。     

基于案例的推理在农业专家系统中的应用

该文将基于案例的推理方法CBR应用到农业专家系统中。在对CBR方法作了简单介绍后,提出了在农业专家系统中面向对象的案例表示、案例库组织的两层结构,并以大豆专家系统中病虫害诊断模块为例阐述了CBR推理在农业专家系统中的具体实现过程。     

基于实例推理的企业动态联盟伙伴选择与优化模型

将基于案例的推理方法运用于动态联盟伙伴企业选择与优化系统中,建立了伙伴企业选择系统的模型。具体讨论了方案库和评价结果库的建立,提出了基于灰色关联理论和模糊集理论相结合的相似度计算方法,从而可以准确地检索到相近案例,提高了伙伴企业选择的效率和准确性。     

Hierarchical case-based reasoning integrating case-based anddecompositional problem-solving techniques for plant-control softwaredesign

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design     

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.     

Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems

Abstract: A new approach based on an adaptive neuro‐fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases.     

Case‐based curve behaviour prediction

Case‐based reasoning (CBR) is the area of artificial intelligence where problems are solved by adapting solutions that worked for similar problems from the past. This technique can be applied in different domains and with different problem representations. In this paper, a system curve base generator (CuBaGe) is presented. This framework is designed to be a domain‐independent prediction system for the analysis and prediction of curves and time‐series trends, based on the CBR technology. CuBaGe employs a novel curve representation method based on splines and a corresponding similarity function based on definite integrals. This combination of curve representation and similarity measure showed excellent results with sparse and non‐equidistant time series, which is demonstrated through a set of experiments. Copyright © 2008 John Wiley & Sons, Ltd.     

案例推理分类器的权重分配及案例库维护方法

由于特征权重分配以及案例库维护对案例推理（CBR）分类器的性能有重要影响,提出了用蚁狮（ALO）算法来分配权重且用高斯混合模型的期望最大化算法（GMMEM）进行案例库维护的案例推理算法模型——AGECBR（Ant Lion and Expectation Maximization of Gaussian Mixture Model Case-Based Reasoning）。首先采用蚁狮算法对特征权重进行分配,在这个过程中将案例推理分类准确率作为蚁狮算法对特征权重进行迭代寻优的适应度函数,以此实现特征权重的优化分配;然后,使用高斯混合模型的期望最大化算法对案例库中的各案例进行聚类分析,并删除其中的噪声案例和冗余案例,从而实现案例库的维护。在UCI标准数据集上进行了实验,所提模型AGECBR比反向传播（BP）、k-近邻（kNN）等分类算法平均分类准确率提升了3.83~5.44个百分点。实验结果表明,AGECBR能够使案例推理分类准确率得到有效改进。     

Mammographic case base applied for supporting image diagnosis of breast lesion

Mammography is an important screening tool for early detection of breast cancer. However, radiologists usually experience difficulties in image interpretation of grey zones. A computer system providing similar cases with known diagnostic results for decision support would be useful. Applying case-based reasoning (CBR) to a mammographic case base, constructed from prior cases with known diagnostic results, offers a solution to this problem. Serving as an inference tool, the CBR can retrieve similar cases to help radiologists interpret a new mammographic case. To evaluate the usability of this system, 34 licensed radiologists were invited as experts to assess the system. The results indicate that CBR applied to the mammographic case base is valuable for decision support in mammographic image interpretation.     

CFW的CBR与ART-KNN集成智能预测

结合基于案例推理（CBR）方法和ART-KNN网络，提出了一种黄瓜枯萎病（CFW）的集成智能预测方法。与传统的CBR相似案例检索任务不同的是，该方法用受训ART-KNN网络对新案例分类后根据提出的案例相似性测度来计算相似案例集。对ART-KNN网络的分类性能进行测试，确定了网络的最优相似参量ρ，得到最高平均分类正确率达94.4%。对CFW进行预测，确定了案例相异阈值R的最优范围，得到病株率、病叶率的最优平均预测误差率分别达7.4%、9.3%。综合分析结果表明，提出的CBR与ART-KNN集成预测方法可为CFW的防治提供较为可靠的预测数据以及辅助决策信息。     

基于相似粗糙集的案例特征权值确定新方法

针对现有案例特征权值确定方法客观性差、算法复杂等问题，首先介绍和完善了基于传统粗糙集的权值确定方法．其次，针对基于传统粗糙集的方法会造成案例相似度测量误差从而影响案例推理的准确性的问题，将传统粗糙集的不可分辨关系推广为相似关系，提出了一种基于相似粗糙集的案例特征权值确定方法．给出了相似粗糙集的基本定义，以及利用该方法基于差别矩阵进行特征权值计算的两个定理．最后，用实例表明了方法的有效性．     

基于案例与规则推理的故障诊断专家系统

设计并实现基于案例的推理(CBR)与基于规则的推理(RBR)的故障旋转机械诊断专家系统。采用CBR与RBR串行方式进行推理,优先通过案例匹配方式寻求诊断结果,在不适用情况下转入通用性规则推理,并将诊断结果反馈给知识库进行优化。应用结果表明,该系统诊断结果与实际相符合,且诊断速度快、针对性强。     

相似案例自适应选择算法及其应用

为提高相似案例选择的效率和准确性,将有向无环图支持向量机（DAGSVM）多类分类器应用到相似案例选择中．提出多类分类器有效分辨阈值的概念,在保证一定案例选择准确度的前提下．对自适应构造案例集进行相似案例选择．提高相似案例选择的效率．将该方法应用于光动力治疗（PDT）鲜红斑痣（PWS）案例推理专家系统．实验结果表明了该方法的有效性．     

Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning

Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today, clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor, they could easily be vulnerable to uncertain noises/interferences in such domain; multiple sensors could provide more robust and reliable decision. Therefore, this paper presents a classification approach i.e. Multivariate Multiscale Entropy Analysis–Case-Based Reasoning (MMSE–CBR) that classifies physiological parameters of wheel loader operators by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval (IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR). Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE–CBR has been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the proposed system that fuses information at data level could classify ‘stressed’ and ‘healthy’ subjects 83.33% correctly compare to an expert’s classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different conditions ‘adapt’ (training) and ‘sharp’ (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE–CBR could support in classification of operators and may be of interest to researchers developing systems based on information collected from different sensor sources.     

基于遥感案例推理的海岸带养殖信息提取

目前基于目视解释或光谱分类的养殖信息提取效率低,难以克服由于地物混杂带来的“椒盐”噪声现象且难以融合地学知识。针对养殖信息提取中存在的问题,首先在分析现有养殖信息提取方法和案例推理CBR(Case\|Based Reasoning)用于遥感图像处理的基础上,提出基于遥感案例推理的海岸带养殖信息提取的研究思路;其次,结合养殖区域的空间特征和属性特征,构建案例的表达模型以及CBR相似性推理模型;最后,对不属于案例构建区的粤西沙田镇进行养殖信息提取的CBR实验,精度达到84.56%。对比CBR方法和传统监督分类方法可知,CBR方法是实现海岸带养殖信息快速准确提取的一种有效手段。     

基于实例推理的电机模具设计

结合某企业电动机典型零件的模具设计,将实例设计的设计思想用于电机模具设计,同时通过对基于实例推理的分析,成功实现了基于相似实例的模具CAD系统,经实际应用,效果显著。