Xilong Qu
Hunan Institute of Engineering

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Support Vector Machine Optimized by Improved Genetic Algorithm Xiang Chang Sheng; Zhou Yu; Xilong Qu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: January 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Parameters of support vector machines (SVM) which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3182
The Research on Software Resource Re-sharing for Small and Medium-sized Enterprise Cloud Manufacturing System Xilong Qu; Wang Yingjun
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: January 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

The background, significance and the related research at home and abroad of software resource re-sharing in cloud manufacturing system are introduced. The current use and system structure of software in manufacturing are analyzed, and they are Web-based, software directly sharing, based package to share software indirectly and server-based technology for common software across the internet remote sharing. The special requirements for software sharing of cloud manufacturing system are presented.  According to the feature of small and medium-sized enterprise, a re-sharing approach of cloud manufacturing software sharing scheme for small and medium-sized enterprise is proposed, and this approach is very compatible, extended, and low-cost, it is very suitable for the manufacturing cloud of small and medium-sized enterprise. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.2918
Adaptive Neural Network Approach for a Class of Uncertain Non-affine Nonlinear Systems Hui Hu; Yingjun Wang; Xilong qu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5284-5292

Abstract

The paper proposes a new output feedback adaptive tracking control scheme using neural network for a class of uncertain non-affine nonlinear systems that only the system output variables can be measured. The scheme adopts low-pass filter to transform non-affine nonlinear systems into affine in the pseudo-input dynamics. No state observer is employed and few adapting parameters to be tuned and Lipschiz assumption, SPR condition are not required. Only the output error is used in control laws and weights update laws which make the system construct simple. Boundedness for the output tracking error and all states in the closed-loop system are guaranteed, and simulation results have verified the effectiveness of the proposed approach.
A New Systemic Safety Detecting Software Xilong Qu; Yingjun Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 10: October 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Because it is hard to find and to clear cockhorse and virus developed by root kit technology, antivirus soft at present is hard to clear virus in the system, which make the system in dangers status of hazard. So, designing a speedy clear Trojan and virus makes by root kit is very important. The article.is based on SDK, adopting the technology.of kernel to design the Clairvoyant systemic safety detecting software. It major function is monitors.the service of the system and the operation. Monitor the register changer. Search the file, process, system module hided by the virus. It can also end protected processes and delete protected files forcibly. Through the port.mapping of processes, it can find.port messages opened.by system, processes opening ports and.cockhorse effectively. http://dx.doi.org/10.11591/telkomnika.v12i10.5345 
Network Intrusion Detection Based on PSO-SVM Changsheng Xiang; Yong Xiao; Peixin Qu; Xilong Qu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: February 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

In order to improve network intrusion detection precision, this paper proposed a network intrusion detection model based on simultaneous selecting features and parameters of support vector machine (SVM) by particle swarm optimization (PSO) algorithm. Firstly, the features and parameters of SVM are coded to particle, and then the PSO is used to find the  optimal features and SVM parameters by collaboration among particles, lastly, the performance of the model was tested by KDD Cup 99 data. Compared with other network models, the proposed model has reduced input features for SVM and has significantly improved the detection precision of network intrusion. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3826