Papers by Keyword: Information Fusion

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Authors: Li Yong Wang, Le Li, Yang Long Li
Abstract: On the basis of complicated structure, various movement forms and high difficulty of fault detection for reciprocating engine, we have implemented the research on fault diagnostic approach by multi-sensor information, carried out the multi-source information fusion based on crankshaft phase signal, vibration signal, ultrasonic signal, pressure signal, ignition voltage signal and oil analysis information, created distributed structure model with multi-source information fusion, explained the parallel, distributed, serial and tree topology structure models, and established total model for multi-source information fusion of reciprocating engine fault diagnosis.
Authors: Jin Ming Yao, Jun Jie Yang, Zhi Bin Lou
Abstract: Due to considerations limited for the current monitoring techniques and computational models and other reasons,the accuracy rate of line icing condition assessment is not high. Transmission line icing are affected by many factors, having greater relevance with micro-meteorological parameters. To improve the assessment accuracy of transmission line icing condition,multi-sensor information fusion method are put forward for a comprehensive assessment to Line icing state, based on online monitoring system,considering the equivalent ice thickness of monitoring system, micro-meteorological parameters and duration of ice cover.BP neural network convergence line icing membership value, the output state is cing probability. Then ,the probability of the state of uncertainty output line integrated assessmen through Fuzzy Reasoning
Authors: Xian Bao Wang, Shi Hai Zhao, Guo Wei
Abstract: According to the theory of multi-sensor information fusion technology, based on D - S evidence theory to fuse of multiple sensors feedback information from different angles for detecting solution concentration, and achieving the same judgment; This system uses of D - S evidence theory of multi-sensor data fusion method, not only make up the disadvantages of using a single sensor, but also largely reduce the uncertainty of the judgment. Additionally this system improves the rapidity and accuracy of the solution concentration detection, and broadens the application field of multi-sensor information fusion technology.
Authors: Dong Yan Cui, Zai Xing Xie
Abstract: In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.
Authors: Xue Jun Li, D.L. Yang, Ling Li Jiang
Abstract: This paper proposed a fault diagnosis based on multi-sensor information fusion for rolling bearing. This method used the energy value of multiple sensors is used as feature vector and a binary tree support vector machine (Binary Tree Support Vector Machine, BT-SVM) is used for pattern recognition and fault diagnosis. By analyzing the training samples, penalty factor and the kernel function parameters have effects on the recognition rate of bearing fault, then a approximate method to determine optimum value are proposed, Compared with the traditional single sensor by using the components energy of EMD as feature, the results show that the proposed method in this paper significantly reduce feature extraction time, and improve diagnostic accuracy, which is up to99.82%. This method is simple, effective and fast in feature extraction and meets the bearing diagnosis requirement of real-time fault diagnosis.
Authors: De Qiang Han, Chong Zhao Han, Yong Deng, Yi Yang
Abstract: Multiple classifier fusion is an effective way to improve the classification performance. In this paper, member classifiers are designed based on the training dataset’s different feature spaces. By utilizing ISODATA technique, various clustering results can be obtained in different feature spaces. For each member classifier (corresponding to one feature space), the given test sample is assigned to the cluster according to the distance between each cluster centroid and the test sample itself. The mass functions and the classification decision of each member classifier for the given test sample can be implemented based on the corresponding cluster’s inner-cluster class distribution. Then according to Dempster’s rule of combination, the multiple classifier fusion can be implemented. Experimental results show the rationality and efficacy of the proposed approach.
Authors: A.C. Xu, J.B. Chen, Pei Ming Zhang, Song Lin Zhuang
Abstract: A combined method measuring human eye aberrations is presented in our study, since both objective and subjective method are needed for the measurement of human eye’s wavefront aberration. The optical setup can carry out the two methods in one system, i.e. it can measure the human eye’s wavefront aberration with 4.8-mm artificial pupil when the natural pupil is dilated and then when the subject is watching visual testing chart. The result datum from both objective measurement and subjective measurement is fused on feature level by information fusion method with the weighting factors, which is helpful to combine the advantages of both subjective and objective measurement and emphasize the influence of neural system on human eye. Finally, we have done a series of experiments to demonstrate this combined fusion method and give some discussions.
Authors: Feng Shan Wang, Quan Bing Rong, Hong Jun Zhang
Abstract: To account for the conflict sensitivity, one model is presented to fuse the high conflict risk evidences about earthquake-damaged underground structure. Following the nature ideology and model rule of Evidence Theory, the earthquake-damaged origin risk evidence is revised with Similarity Coefficients, and the identical intensity and conflict intensity is calculated for each risk evidence; the difference and conflict character is comparatively analyzed about the fusion rules respectively on Similarity Coefficient and Conflict Intensity; Under Standard DS Fusion Mode and Conflict Intensity Fusion Method, the four combination fusion model is presented as Model-AO, Model-RO, Model-AC and Model-RC, and the improved risk fusion operator is given for such earthquake-damaged underground structure evidences. Finally, case demonstrates the validity of the integrated model, which could overcome the high conflict lack in the risk fusion standard DS model.
Authors: Hong Zheng, Kai Zhang
Abstract: To distinguish people’s identities, the information is normally included in one gait periodic sequence image. First, the gait energy image for feature extraction of wavelet moments was constructed. After boundary unwrapping, the gait silhouette boundary was extracted and principal component analysis (PCA) was use to obtain its compressed contour features. Then nearest neighbor classifier and support vector machines were applied for classification of these two features. Finally, support vector machine (SVM) on Bayesian rule were used to complete gait recognition with information fusion of different features. The method is evaluated on the National Laboratory of Pattern Recognition (NLPR) gait database and the correct recognition rate is relatively high. The experimental results show that the proposed method has good recognition performance.
Authors: Feng Tian, Xiao Lei Lu
Abstract: This document explains a system of open pit slope deformation monitoring. After calculating data by software of GPS Solution and the system which bases on the signal of GPS receiver obtain information of every GPS receiver. So it can get GIS information of whole coal mine. If there is any abnormal data, it will report to the police timely, and directly control the running of coal miningmachines through PLC. Compared with the technology before, the system has higher automation integration, higher positioning accuracy, gets more responsive and provides more reliable data. It’s the best choice for long-term spatial structure monitoring and condition assessment.
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