Support vector machine-based classification scheme for myoelectric control applied to upper limb

This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations.     

Support vector machine-based inspection of solder joints using circular illumination

A method for inspecting solder joints using support vector machines (SVMs) and a tiered circular illumination technique is proposed. The illumination technique provides visual information that allows the 3D shape of the solder joint surface to be determined. The extracted features are used to classify the solder joint using an SVM classifier. Experimental results show the effectiveness of the proposed method.     

模式识别的特征优化专家系统研究

首先对模式识别中常用的几种特征分析方法的特点与应用范围进行了分析,基于此,开发了应用于特征优化、求取较优特征集的专家系统,对特征优化专家系统的关键问题——规则库内容作了初步确定,并给出具体的推理过程。试验证明,该专家系统能根据前端数据的特点与具体特征分析方法的特点,选择最佳特征优化方法,给出表征能力较好的特征集,应用于目标识别时能大大提高识别准确率。     

Support vector method for robust ARMA system identification

This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals and SVM-ARMA coefficients allows the linking of the fundamentals of SVM with several classical system identification methods. Additionally, the effect of outliers can be cancelled. Application examples show the performance of SVM-ARMA algorithm when it is compared with other system identification methods.     

基于专家系统的压力容器胶片焊缝缺陷识别技术研究

针对压力容器无损检测中胶片焊缝的缺陷识别,根据缺陷图像的特征,采用图像预处理、图像分割算法,专家系统等方法,提出了一种胶片焊缝缺陷的识别方法,并在MATLAB下实现。介绍了专家系统的基本原理、结构、建立方法和识别策略,根据焊缝缺陷特征,从不同层次进行识别。通过不断的试验来完善胶片缺陷专家系统库,进一步提高缺陷识别率。     

Complex common vector for multimodal biometric recognition

A novel multimodal biometric recognition algorithm based on a complex common vector (CCV) is proposed. The CCV generalises the common vector method for the complex field to perform feature fusion and classification. Theoretical analysis proves that the CCV could produce a unique common vector for every fusion feature in a given class. The iris and the face are used as two distinct biometric modals to test the algorithm. Experimental results show that the proposed algorithm achieves much better performance than other conventional multimodal biometric algorithms.     

Support vector machine networks for friction modeling

This paper presents a novel model-free approach for modeling friction for servo-motion systems. The proposed approach uses the support vector machine networks to parameterize the static friction mapping. The procedure of constructing such networks from a finite amount of training (sampling) data is developed based on support vector machine regression (SVMR). The validity of the proposed approach has been experimentally verified.     

Support vector machines for automated gait classification

Ageing influences gait patterns causing constant threats to control of locomotor balance. Automated recognition of gait changes has many advantages including, early identification of at-risk gait and monitoring the progress of treatment outcomes. In this paper, we apply an artificial intelligence technique [support vector machines (SVM)] for the automatic recognition of young-old gait types from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly participants were analyzed using a PEAK-2D motion analysis system during a 20-min continuous walk on a treadmill at self-selected walking speed. Gait features extracted from individual MFC histogram-plot and Poincaré-plot images were used to train the SVM. Cross-validation test results indicate that the generalization performance of the SVM was on average 83.3% (+/-2.9) to recognize young and elderly gait patterns, compared to a neural network's accuracy of 75.0+/-5.0%. A "hill-climbing" feature selection algorithm demonstrated that a small subset (3-5) of gait features extracted from MFC plots could differentiate the gait patterns with 90% accuracy. Performance of the gait classifier was evaluated using areas under the receiver operating characteristic plots. Improved performance of the classifier was evident when trained with reduced number of selected good features and with radial basis function kernel. These results suggest that SVMs can function as an efficient gait classifier for recognition of young and elderly gait patterns, and has the potential for wider applications in gait identification for falls-risk minimization in the elderly.     

Support vector machine techniques for nonlinear equalization

The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can be identified in a manner that does not require the extent of prior information or heuristic assumptions that some previous techniques require. Furthermore, the optimization method of a support vector machine is quadratic programming, which is a well-studied and understood mathematical programming technique. Support vector machine simulations are carried out on nonlinear problems previously studied by other researchers using neural networks. This allows initial comparison against other techniques to determine the feasibility of using the proposed method for nonlinear detection. Results show that support vector machines perform as well as neural networks on the nonlinear problems investigated. A method is then proposed to introduce decision feedback processing to support vector machines to address the fact that intersymbol interference (ISI) data generates input vectors having temporal correlation, whereas a standard support vector machine assumes independent input vectors. Presenting the problem from the viewpoint of the pattern space illustrates the utility of a bank of support vector machines. This approach yields a nonlinear processing method that is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equalizer is replaced by a Volterra filter. A simulation using a linear system shows that the proposed method performs equally to a conventional decision feedback equalizer for this problem     

An expert system approach for cellular CDMA

The demand for cordless communication has forced suppliers to exploit digital technology to satisfy the increasing demand for service. This demand has led to severe capacity limitations in many areas and spread spectrum techniques are being considered as one means to support these services. It is generally accepted that system control in the future will make use of intelligent processors. In this study we delineate an expert system to be used as an intelligent controller for a cell site working in a CDMA (Code Division Multiple Access) mobile cellular telecommunications system. An existing AI shell is used for program development and simulation and the results are discussed in terms of the benefits obtained by using an expert system approach in the highly dynamic environment of cellular mobile communications     

专家系统在水情预报中的应用

提出了水情预测专家系统的体系结构,并以该体系结构为基础给出了此类专家系统的知识表示及其推理机的实现方法,用SQL SERVER知识库存储基本数据库及产生式规则库,用VB实现推理机及人机界面,水情信息进行实时监控和预测.由于知识库和推理机分别独立实现,使得整个系统结构合理,具有良好的可维护性.     

An expert system for land cover classification

A framework to represent a broad class of problems in the analysis of remote sensing imagery is proposed, and an inference engine to tackle such problems is derived. A simple model for spectral knowledge representation is used along with a method for quantification of knowledge through an evidential approach. An automatic knowledge extraction technique is also proposed to gather knowledge from training samples. The techniques of knowledge extraction, representation and inferencing have been used to do a land cover analysis on two data sets, and the results are compared with contemporary digital techniques. It is found that the proposed approach has the advantages of avoiding commission errors, and can incorporate non-spectral and collateral knowledge, while its accuracy using only spectral knowledge is comparable with standard digital methods     

Autonomous power expert system

The Autonomous Power Expert (APEX) system has been designed to monitor and diagnose fault conditions that occur within the Space Station Freedom Electrical Power System (SSF/EPS) Testbed. The APEX system is being developed at the NASA Lewis Research Center by the Space Electronics Division (SED) in conjunction with the Space Station Directorate and Power Technology Division (PTD). APEX is designed to interface with SSF/EPS testbed power management controllers to provide enhanced autonomous operation and control capability.

The APEX architecture consists of three components: (1) a rule-based expert system, (2) a testbed data acquisition interface, and (3) a power scheduler interface. Fault detection, fault isolation, justification of probable causes, recommended actions, and incipient fault analysis are the main functions of the expert system component. The data acquisition component requests and receives pertinent parametric values from the EPS testbed and asserts the values into a knowledge base. Power load profile information is obtained from a remote scheduler through the power scheduler interface component.

This paper will discuss the current APEX design and development work. Operation and use of APEX by way of the user interface screens will also be covered.     

飞机电源系统故障诊断专家系统研究

针对飞机电源系统故障诊断的现状,为提高故障诊断的效率提出利用专家系统开发工具CLIPS建立飞机电源系统故障诊断专家系统。以西锐SR20飞机的电源系统为诊断对象,采用CLIPS为开发工具建立知识库、推理机。利用Eclipse开发环境编写专家系统人机界面程序,通过嵌入CLIPS JNI插件实现Java程序对CLIPS程序的调用,最终实现包括知识库、推理机、人机界面等模块在内的专家系统整体性能。最后阐述了专家系统在故障诊断领域的广阔应用前景。     

基于支持向量机的语种识别

支持向量机是统计学习理论的一个重要分支,也是解决模式识别问题的一个有力工具.现简要介绍支持向量机理论,构建基于径向基函数的支持向量机,对分别来自两个不同的电话语音数据库中的汉语普通话、英语、日语、白族语和纳西语等5种语言进行识别研究.实验结果表明,支持向量机, 对不同数据库语言的语种识别依然能达到比较高的识别率.     

Foetos: an expert system for fetal assessment

Assessment of the fetus in a high-risk pregnancy uses a variety of tests for screening and continued detection of in utero compromise. This paper describes Foetos, an expert system designed to help clinical personnel to interpret several fetal assessment tests: fetal biophysical profile, contraction stress test, and nonstress tests. Foetos has been built using the knowledge engineering tool Genie, which adopts a mixed frame- and rule-based approach to represent the clinical knowledge in the field. Foetos includes diagnostic, prognostic, and therapeutic structures, based on heuristic interpretation of such tests and contextual structures which relate interpretation to the overall clinical picture. The results of initial retrospective and prospective program validation are included in the report. These results show a substantial level of agreement between Foetos' recommendations and clinical management. Interpretation of the results indicates that the obstetrical field could be an area of interest for the application of AI techniques.     

Support vector channel selection in BCI

Designing a brain computer interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying electroencephalogram (EEG) signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization which are based on the training of support vector machines (SVM). These algorithms can provide more accurate solutions than standard filter methods for feature selection. We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.     

ESCFE: expert system for customer facing environments

ServiceCentres are purpose-built units where British Telecom (BT) staff provide a single point of contact to BT's largest customers. They provide information on all queries relating to product availability, ordering, job progression, and fault finding. ESCFE is a prototype expert system which supports ServiceCentre staff in the three-way dialog between BT staff, the customer, and BT computer systems (both local and remote). It is designed to be user-friendly to both BT staff and customers, and to provide quality customer service by promoting a professional image and efficient customer handling. It is essentially an intelligent interface on top of existing system functionality, which allows the user to receive structured dialog support in dealing with a customer request     

An expert system architecture for computer-aided control engineering

We propose the development of a rule-based expert system to create a third-generation man/machine environment for computer-aided control engineering (CACE). The breadth of the CACE problem is of particular concern, and provides a major motivation for the use of artificial intelligence. This approach promises to provide a high-level design environment that is powerful, supportive, flexible, broad in scope, and readily accessible to nonexpert users. We focus primarily on the high-level requirements for an improved CACE environment, and on the expert system concepts and structures that we have conceived to fulfill these needs. Our chief goal is to determine what artificial intelligence has to contribute to such an environment, and to provide as definite and credible a vision of an expert system for CACE as possible. The main product of this effort is an expert system architecture for CACE.     

I-Test: integrated testing expert system for trunks

A prototype trunk-testing expert system developed by Bell Communications Research (Bellcore) is described. The integrated-testing expert-system for trunks (I-TEST) mechanizes and automates many of the conventional manual testing procedures in a switching control center (SCC) of a Bellcore client company (BCC). I-TEST demonstrates the usefulness of integrating artificial intelligence (AI) techniques into the network operations environment. I-TEST was developed in the INTELEWINDOWS environment. This environment includes a windowing system, graphical kernel system (GKS), and a set of high-performance portable C-based AI tools. I-TEST combines traditional structured procedural knowledge and a typical rule-based production system into one inference mechanism to manage a complex trunk-testing environmental in the SCC. I-TEST is an effort to use one intelligent system to integrate all of the future network testing needs, and provide an automated testing environment. The modular design and generic nature of I-TEST allow it to be easily reconfigured to include test access to additional operations systems or network elements