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排序方式: 共有4034条查询结果,搜索用时 281 毫秒
91.
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93.
O. PostolacheAuthor Vitae H. Geirinhas Ramos Author VitaeA. Lopes Ribeiro Author Vitae 《Computer Standards & Interfaces》2011,33(2):191-200
This work presents an eddy-current testing system based on a giant magnetoresistive (GMR) sensing device. Non-destructive tests in aluminum plates are applied in order to extract information about possible defects: cracks, holes and other mechanical damages. Eddy-current testing (ECT) presents major benefits such as low cost, high checking speed, robustness and high sensitivity to large classes of defects. Coil based architecture probes or coil-magnetoresistive probes are usually used in ECT. In our application the GMR sensor is used to detect a magnetic field component parallel to a plate surface, when an excitation field perpendicular to the plate is imposed. A neural network processing architecture, including a multilayer perceptron and a competitive neural network, is used to classify defects using the output amplitude of the eddy-current probe (ECP) and its operation frequency. The crack detection, classification and estimation of the geometrical characteristics, for different classes of defects, are described in the paper. 相似文献
94.
K.C. Sindhu Thampatty M.P. NandakumarElizabeth P. Cheriyan 《Engineering Applications of Artificial Intelligence》2011,24(1):60-76
Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions. 相似文献
95.
A distributed approach to enabling privacy-preserving model-based classifier training 总被引:4,自引:4,他引:0
Hangzai Luo Jianping Fan Xiaodong Lin Aoying Zhou Elisa Bertino 《Knowledge and Information Systems》2009,20(2):157-185
This paper proposes a novel approach for privacy-preserving distributed model-based classifier training. Our approach is an
important step towards supporting customizable privacy modeling and protection. It consists of three major steps. First, each
data site independently learns a weak concept model (i.e., local classifier) for a given data pattern or concept by using
its own training samples. An adaptive EM algorithm is proposed to select the model structure and estimate the model parameters simultaneously. The second step deals with combined classifier training by integrating the weak concept models that are shared from multiple data sites. To reduce the data transmission costs and
the potential privacy breaches, only the weak concept models are sent to the central site and synthetic samples are directly generated from these shared weak concept models at the central site. Both the shared weak concept models and
the synthetic samples are then incorporated to learn a reliable and complete global concept model. A computational approach is developed to automatically achieve a good trade off between the privacy disclosure risk, the
sharing benefit and the data utility. The third step deals with validating the combined classifier by distributing the global
concept model to all these data sites in the collaboration network while at the same time limiting the potential privacy breaches.
Our approach has been validated through extensive experiments carried out on four UCI machine learning data sets and two image
data sets.
相似文献
Jianping FanEmail: |
96.
Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases 总被引:1,自引:1,他引:0
Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases. 相似文献
97.
We present in this work a two-step sparse classifier called IP-LSSVM which is based on Least Squares Support Vector Machine (LS-SVM). The formulation of LS-SVM aims at solving the learning problem with a system of linear equations. Although this solution is simpler, there is a loss of sparseness in the feature vectors. Many works on LS-SVM are focused on improving support vectors representation in the least squares approach, since they correspond to the only vectors that must be stored for further usage of the machine, which can also be directly used as a reduced subset that represents the initial one. The proposed classifier incorporates the advantages of either SVM and LS-SVM: automatic detection of support vectors and a solution obtained simply by the solution of systems of linear equations. IP-LSSVM was compared with other sparse LS-SVM classifiers from literature, and RRS+LS-SVM. The experiments were performed on four important benchmark databases in Machine Learning and on two artificial databases created to show visually the support vectors detected. The results show that IP-LSSVM represents a viable alternative to SVMs, since both have similar features, supported by literature results and yet IP-LSSVM has a simpler and more understandable formulation. 相似文献
98.
In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different
notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in
three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology. We propose a multi-classifier that combines
a classifier that approaches the problem as a two-class pattern recognition problem and a method based on a one-class classifier.
Several classifiers combined with the “sum rule” enables us to obtain an improvement performance over the best results previously
published in the literature.
相似文献
Loris NanniEmail: |
99.
Rule-based intrusion detection systems generally rely on hand crafted signatures developed by domain experts. This could lead to a delay in updating the signature bases and potentially compromising the security of protected systems. In this paper, we present a biologically-inspired computational approach to dynamically and adaptively learn signatures for network intrusion detection using a supervised learning classifier system. The classifier is an online and incremental parallel production rule-based system.A signature extraction system is developed that adaptively extracts signatures to the knowledge base as they are discovered by the classifier. The signature extraction algorithm is augmented by introducing new generalisation operators that minimise overlap and conflict between signatures. Mechanisms are provided to adapt main algorithm parameters to deal with online noisy and imbalanced class data. Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt.The performance of the developed systems is evaluated with a publicly available intrusion detection dataset and results are presented that show the effectiveness of the proposed system. 相似文献
100.