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With the rapid development of the Internet, the P2P (Peer-to-Peer) technology which is characterized by no utilization of any servers with centralized functions has kept advancing apace. However, how to improve the accuracy of the P2P traffic identification efficiently is still a challenging problem. In this paper, we propose a new approach for P2P traffic identification, which uses a novel Support...
The widespread applications of chlorofluorocarbon (CFC) in industry including refrigeration, foam blowing agent and cleaning agent cause severe damage to the ozone layer and might contributed to global warming. Various methods were developed to find CFC alternatives or its environmentpsilas destruction. Among them catalytic conversion of CFCs into valuable compounds like hydrofluorocarbon (HFC) is...
Ship pitching influences mostly ship motion, it's important to study ship pitching modeling and prediction in order to improve ship's seaworthiness. Based on the random character of ship movement, this paper put forward a method for prediction of ship pitching movement with SVM. Based on the phase-space reconstruction theory, the method, the characteristic, and the selecting of the key parameters...
In order to recognize stratums, a new support vector machine model (SVMM) is built on the basis of well-logging data and with RBF as its kernel function. Through the optimization of penalty parameter C and the introduction of a discriminant function, the classification accuracy of SVMM is greatly enhanced. Experiments show that the SVM classifier can be applied effectively to the recognition of stratums,...
A novel model was proposed for short-term electricity price forecasting based on rough set approach and improved support vector machines (SVM). Firstly, we can get reduced information table with no information loss by rough set approach. And then, this reduced information is used to develop classification rules and train SVM, at the same time, we make use of the particle swarm optimization to optimize...
The principal role of embedded software is the transformation of data and the interaction with the physical world stimulus. The main concern in developing embedded software for network application is the lack of published best practice software architecture for optimizing performance by means of reducing protocol processing overhead, memory usage, and power consumption. This paper presents the implementation,...
With the increasingly widespread application of the network, a distributed operating system based on Linux cluster has been developing rapidly. This paper presents a kernel level distributed interprocess communication system model with support for distributed process synchronization and communication. This system model uses the System V interprocess communication programming interface and enhances...
Intruders will normally install some tools when he gains access to a computer system, in order to regain the root privilege when he come back onto the system at a later time. Installing a rookit on the compromised system is one of the methods that a intruder may use. The kernel of the operating system which is the lowest level of most modern OS will be modified by a kernel level rootkit. In this paper...
The malicious code has characteristic of various types, and its ability of hiding increases quickly. In this paper, according to the analysis of hiding technology of malicious programs, it proposed a new idea of detecting malware based on the raw data. Finally, the results are given, which are compared with the current security detection scanners. It is a beneficial attempt of this method in detecting...
Support vector machine is an algorithm based on structural risk minimization, which has good generalization performance. In the course of multi-sensor information fusion of industrial control, sensor has bigger nonlinearity and fuzzy relation between coefficient and relevant parameter. This paper proposed an algorithm of multi-sensor information fusion based on support vector machine, which offered...
A face recognition method similar to human cognitive mechanism has been proposed. Motivated by the continuity rule of a same class samples, face recognition can be regarded as face pattern cognition. Compared with traditional statistical pattern based face recognition, it has a kind of relationship with human's cognitive mechanism. When face coverage of different people overlap, a person's face distribution...
A key obstacle to large-scale network simulation over PC clusters is the memory balancing problem where a memory-overloaded machine can slow down an entire simulation due to disk I/O overhead. Memory balancing is complicated by (i) the dfficulty of estimating the peak memory consumption of a group of nodes during network partitioning-a consequence of per-node peak memory not being synchronized-and...
In order to know how different conditions influence the behavior of the RAM Enhanced Disk Cache Project (RED-CAP), we have analyzed the impact of the file system and the REDCAP cache size. The results show that, for work-loads which exhibit some spatial locality the application time can be reduced by more than 80% for file systems that split the disk into block groups, while for those that do not...
In traditional spectral clustering algorithms, the number of cluster is choosen in advance. A self-adaption spectral clustering algorithm is proposed to decide the cluster number automatically, which eliminates the drawbacks of two kinds of spectral clustering methods. In our algorithm, eigengap is used to discover the clustering stability and decide the cluster number automatically. We prove theoretically...
Shape context is a rich descriptor for shapes and can be exploited to find pointwise correspondences between shapes, and thereby to obtain shape alignment by thin plate spline (TPS). It is invariant under scaling and translation and robust under small geometrical distortions and presence of outliers. These features will supply a gap of the defect of semigroup kernel for its weakness in dealing with...
Generalized cross entropy estimator (GCEE) based nonparametric Blind Signal Separation (BSS) algorithm is proposed under the framework of natural gradient optimization method. In order to improve the performance of signal separation by BSS, the probability distribution of source signals must be described as accurately as possible. Compared to the nonparametric fixed-width kernel density estimator...
In order to identify the rotating machinery fault, a method based on support vector machine (SVM) is proposed in this paper. After the feature vectors from the fault signals by means of wavelet packet are extracted and the support vector machine (SVM) classification algorithm to the classification of faults in rolling bearing is applied. By drawing a comparison between the classification and BP neural...
Aimed at heart disease diagnose is an important issue and hybrid kernel functions have excellent learning ability and generalization performance, we propose SVM based on hybrid kernel function and apply the model to test the heart disease dataset. In this paper, K-type kernel function combine with linear kernel and polynomial kernel is firstly proposed, Linear combinations with different kernel functions...
This paper presents a new SVM algorithm framework optimized by PSO algorithm. The value of parameters in the SVM has great influence on the performance of regression model. In previous works the choice of these parameters mainly depends on the experience. In our work PSO algorithm was used to optimize these parameters to form a new SVM framework - PSVM. The proposed algorithm was used to forecast...
Based on the comprehensive analysis of the existing risk early warning methods in real estate, a new risk forecast method based on support vector machine is put forward. And a risk early warning model in real estate market based on support vector machine is established. The realization process of the risk early warning method is discussed. Taking the practical data in real estate exploitation as the...
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