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Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over...
An improved batch process fault identification approach with kernel exponential discriminant analysis (KEDA) is proposed, in which performance index based on difference degree is given to identify fault classification. This method takes the advantages of both the kernel technology and the exponential discriminant analysis technique. The proposed KEDA method shows powerful ability in dealing with nonlinear,...
The amount of data is exploding with the development of Internet and multimedia technology. Rapid retrieval of mass data is becoming more and more important. To meet the demand of the rapid retrieval, many approximate nearest neighobor methods have been proposed to accelerate the exhaustive search process. Hashing is such an example with great balance of time and accuracy. Hashing methods achieve...
In order to overcome the low accuracy defect of the traditional theoretical line losses calculation method in distribution system, an intelligent calculation method based on improved minimum enclosing ball vector machine (MEBVM) is proposed. In this intelligent calculation method, the theoretical line losses calculation is abstracted into multiple regression analysis. All kinds of line losses impact...
Personal credit assessment is carried out by setting up a mathematical model to count, calculate and analyze the personal credit data. At present personal credit assessment has already became a kind of worldwide industry. In this paper we combine kernel principal component analysis and support vector machine to propose a new mathematical model based on KPCA and SVM. We extract personal credit data...
Face recognition plays a important role in computer vision. Recent researches show that high dimensional face images lie on or close to a low dimensional manifold. LPP is a widely used manifold reduced dimensionality technique. But it suffers two problem: (1) Small Sample Size problem; (2)the performance is sensitive to the neighborhood size k. In order to address the problems, this paper proposed...
Traditional classification algorithms used in remote sensing images have many problems, such as the low operation speed, low accuracy and difficult convergence. Support Vector Machine (SVM) is a new machine learning method of statistical learning theory based on small samples of machine learning rules. This paper deals with the remote sensing image classification by the support vector machine, using...
In this paper, a new nonlinear system prediction control algorithm is proposed according to the process requirements of Roller-hearth Heat Treatment Furnace. The new control algorithm uses particle swarm optimization (PSO) and support vector machine (SVM) to establish the predictive model. This model is established and simulated using lots of data acquired from the site. The result indicates that...
In apple harvesting robot stereo vision system, fruit recognition based on least squares support vector machine (LS-SVM) and calibration based on binocular vision are proposed, in order to gain the location information of apples including depth. Firstly, vector median filtering, opening and closing operations are employed, then feature vectors, H and S components in HIS color model and shape features,...
According to the non-stationary feature of ECG signal, a new classification method of arrhythmia is introduced. This method combines empirical mode decomposition (EMD) with singular value decomposition (SVD), using support vector machines (SVM) for classifying. First, ECG signal is decomposed into a set of intrinsic mode function (IMF) using empirical mode decomposition method. The initial feature...
In an increasingly competitive market, the management of client relationship is becoming a key point for a enterprise to get a success in the competition, client subdivision is a foundation for the enterprise to make a precise marketing strategy and a successful management of client group, based on the development of data mining technology, a fuzzy-C-means(FCM) algorithm model is founded to do the...
In the robot vision system of the apple harvesting robot, the key is to recognize and locate the apple. To solve recognition questions such as high error rate, too much calculation and time consuming, a new recognizing method, support vector machine (SVM) is applied to improve recognition accuracy and efficiency. At first, vector median filter is used to remove the color images noise of apple fruit...
Security, simplicity, efficiency, are three important aspects of system architecture design, especially for dedicated security systems. The legacy horizontal-layered architectures (e.g., microkernel model) suffer from lacking of many desired features, such as flexibility, security and deployability. In this paper, we propose a new kernel model with vertical architecture, called ultra-kernel model,...
This paper proposes an effective method for constructing and pruning support vector machine ensembles for improved classification performance. Firstly we propose a novel method for constructing SVM ensembles. Traditionally an SVM ensemble is constructed by the data sampling method; In our method, however,each individual SVM classifier is trained by using the same original training set, but with different...
Decision tree is one common method used in data mining to extract predicted information. Based on Statistical Learning Theory (SLT), support vector machine(SVM) is a new kind of machine learning method that is used for classification and regression, it realizes the trade-off between empirical risk minimization(ERM) and generalization capability. SVM and decision tree have combined into one multi-class...
Structural similarity computation plays a crucial role in many applications such as in searching similar documents, in comparing chemical compounds, in finding genetic similarities, etc. We propose in this paper to use structural information content (SIC) for measuring structural information, considering both the nodes and edges of trees. We utilize a binary encoding approach for assigning the weights...
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