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An immune local concentration based virus detection approach
Wei Wang, Peng-tao Zhang, Ying Tan, Xin-gui He
Front. Inform. Technol. Electron. Eng., 2011, 12(6): 443-454.
https://doi.org/10.1631/jzus.C1000445
Along with the evolution of computer viruses, the number of file samples that need to be analyzed has constantly increased. An automatic and robust tool is needed to classify the file samples quickly and efficiently. Inspired by the human immune system, we developed a local concentration based virus detection method, which connects a certain number of two-element local concentration vectors as a feature vector. In contrast to the existing data mining techniques, the new method does not remember exact file content for virus detection, but uses a non-signature paradigm, such that it can detect some previously unknown viruses and overcome the techniques like obfuscation to bypass signatures. This model first extracts the viral tendency of each fragment and identifies a set of statical structural detectors, and then uses an information-theoretic preprocessing to remove redundancy in the detectors’ set to generate ‘self’ and ‘nonself’ detector libraries. Finally, ‘self’ and ‘nonself’ local concentrations are constructed by using the libraries, to form a vector with an array of two elements of local concentrations for detecting viruses efficiently. Several standard data mining classifiers, including K-nearest neighbor (KNN), radial basis function (RBF) neural networks, and support vector machine (SVM), are leveraged to classify the local concentration vector as the feature of a benign or malicious program and to verify the effectiveness and robustness of this approach. Experimental results show that the proposed approach not only has a much faster speed, but also gives around 98% of accuracy.
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Image stabilization with support vector machine
Wen-de Dong, Yue-ting Chen, Zhi-hai Xu, Hua-jun Feng, Qi Li
Front. Inform. Technol. Electron. Eng., 2011, 12(6): 478-485.
https://doi.org/10.1631/jzus.C1000236
We propose an image stabilization method based on support vector machine (SVM). Since SVM is very effective in solving nonlinear regression problems, an SVM model was constructed and trained to simulate the vibration characteristic. Then this model was used to predict and compensate for the vibration. A simulation system was built and four assessment metrics including the signal-to-noise ratio (SNR), gray mean gradient (GMG), Laplacian (LAP), and modulation transfer function (MTF) were used to verify our approach. Experimental results showed that this new method allows the image plane to locate stably on the CCD, and high quality images can be obtained.
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An efficient hardware design for HDTV H.264/AVC encoder
Liang Wei, Dan-dan Ding, Juan Du, Bin-bin Yu, Lu Yu
Front. Inform. Technol. Electron. Eng., 2011, 12(6): 499-506.
https://doi.org/10.1631/jzus.C1000201
This paper presents a hardware efficient high definition television (HDTV) encoder for H.264/AVC. We use a two-level mode decision (MD) mechanism to reduce the complexity and maintain the performance, and design a sharable architecture for normal mode fractional motion estimation (NFME), special mode fractional motion estimation (SFME), and luma motion compensation (LMC), to decrease the hardware cost. Based on these technologies, we adopt a four-stage macro-block pipeline scheme using an efficient memory management strategy for the system, which greatly reduces on-chip memory and bandwidth requirements. The proposed encoder uses about 1126k gates with an average Bjontegaard-Delta peak signal-to-noise ratio (BD-PSNR) decrease of 0.5 dB, compared with JM15.0. It can fully satisfy the real-time video encoding for 1080p@30 frames/s of H.264/AVC high profile.
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Partial discharge diagnostics in wind turbine insulation
Michael G. Danikas, Athanasios Karlis
Front. Inform. Technol. Electron. Eng., 2011, 12(6): 515-522.
https://doi.org/10.1631/jzus.C1000256
The purpose of this paper is to review work undertaken on partial discharges and their influence on the insulation of wind turbines. No matter whether partial discharges can be considered as the main cause of deterioration of the insulation material, the initial cause of failure or not but an indication of the material degradation, there is no doubt that they are intimately linked to the aging of machine insulation. Material degradation can be detected by non-destructive techniques (e.g., partial discharge measurements, change of tan δ) or by destructive techniques, such as by cutting small pieces of the insulating material and by putting them under the scrutiny of the scanning electron microscope (SEM). Wind generators are a modern subject of research, especially in view of the growing demands of electric energy worldwide and the problems facing the environment all over the globe. Wind turbines are a novel field of research regarding partial discharge diagnostics since they are subjected to a variety of aging factors, which are different from conventional turbines. In this respect, particular attention should be paid to the multi-factor stressing of insulation and their consequences on the partial discharges.
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9 articles
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