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Modelling of Electrical Conductivity of a Silver Plasma at Low Temperature

Plasma Science and Technology 2016年第8期18卷 812-820页

作者： Pascal ANDRE William BUSSIERE Alain COULBOIS Jean-Louis GELET David ROCHETTE Université Clermont Auvergne LAEPT MERSEN 15 rue Jacques de Vaucanson

During the working of electrical fuses, inside the fuse element the silver ribbon first begins to melt, to vaporize and then a fuse arc appears between the two separated parts of the element. Second, the electrodes are struck and the burn-back phenomenon takes place. Usually, the silver ribbon is enclosed inside a cavity filled with silica sand. During the vaporization of the fuse element, one can consider that the volume is fixed so that the pressure increase appears to reach pressures higher than atmospheric pressure. Thus, in this paper two pressures, 1 atm and 10 atm, are considered. The electrical field inside the plasma can reach high values since the distance between the cathode surface and the anode surface varies with time. That is to say from zero cm to one cm order. So we consider various electrical fields： 102 V/m, 103 V/m, 5×103 V/m, 104 V/m at atmospheric pressure and 105 V/m at a pressure of 10 atm. This study is made in heavy species temperature range from 2,400 K to 10,000 K. To study the plasma created inside the electric fuse, we first need to determine some characteristics in order to justify some hypotheses. That is to say： are the classical approximations of the thermal plasmas physics justified？ In other words： plasma frequency, the ideality of the plasma, the Debye-Hückel approximation and the drift velocity versus thermal velocity. These characteristics and assumptions are discussed and commented on in this paper. Then, an evaluation of non-thermal equilibrium versus considered electrical fields is given. Finally, considering the high mobility of electrons, we evaluate the electrical conductivities.

关键词： electrical conductivity silver fuses non-ideal plasma Debye-Hiickelapproximation drift velocity thermal velocity thermal non-equilibrium electrical fields

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Multi-Scale Binocular Stereo Matching Based on Semantic Association

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Chinese Journal of Electronics 2024年第4期33卷 1010-1022页

作者： Jin ZHENG Botao JIANG Wei PENG Qiaohui ZHANG State Key Laboratory of Virtual Reality Techonology and Systems Beihang University School of Computer Science and Engineering Beihang University

Aiming at the low accuracy of existing binocular stereo matching and depth estimation methods, this paper proposes a multi-scale binocular stereo matching network based on semantic association. A semantic association module is designed to construct the contextual semantic association relationship among the pixels through semantic category and attention mechanism. The disparity of those regions where the disparity is easily estimated can be used to assist the disparity estimation of relatively difficult regions, so as to improve the accuracy of disparity estimation of the whole image. Simultaneously, a multi-scale cost volume computation module is proposed. Unlike the existing methods, which use a single cost volume, the proposed multi-scale cost volume computation module designs multiple cost volumes for features of different scales. The semantic association feature and multi-scale cost volume are aggregated, which fuses the high-level semantic information and the low-level local detailed information to enhance the feature representation for accurate stereo matching. We demonstrate the effectiveness of the proposed solutions on the KITTI2015 binocular stereo matching dataset, and our model achieves comparable or higher matching performance, compared to other seven classic binocular stereo matching algorithms.

关键词： Costs Accuracy Attention mechanisms fuses Computational modeling Semantics Estimation

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FMR-GNet: Forward Mix-Hop Spatial-Temporal Residual Graph Network for 3D Pose Estimation

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Chinese Journal of Electronics 2024年第6期33卷 1346-1359页

作者： Honghong YANG Hongxi LIU Yumei ZHANG Xiaojun WU Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism School of Computer Science Shaanxi Normal University

Graph convolutional networks that leverage spatial-temporal information from skeletal data have emerged as a popular approach for 3D human pose estimation. However, comprehensively modeling consistent spatialtemporal dependencies among the body joints remains a challenging task. Current approaches are limited by performing graph convolutions solely on immediate neighbors, deploying separate spatial or temporal modules, and utilizing single-pass feedforward architectures. To solve these limitations, we propose a forward multi-scale residual graph convolutional network(FMR-GNet) for 3D pose estimation from monocular video. First, we introduce a mix-hop spatialtemporal attention graph convolution layer that effectively aggregates neighboring features with learnable weights over large receptive fields. The attention mechanism enables dynamically computing edge weights at each layer. Second,we devise a cross-domain spatial-temporal residual module to fuse multi-scale spatial-temporal convolutional features through residual connections, explicitly modeling interdependencies across spatial and temporal domains. Third, we integrate a forward dense connection block to propagate spatial-temporal representations across network layers, enabling high-level semantic skeleton information to enrich lower-level features. Comprehensive experiments conducted on two challenging 3D human pose estimation benchmarks, namely Human3.6M and MPI-INF-3DHP, demonstrate that the proposed FMR-GNet achieves superior performance, surpassing the most state-of-the-art methods.

关键词： Location awareness Three-dimensional displays Correlation Graph convolutional networks fuses Aggregates Pose estimation Semantics Benchmark testing Skeleton

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MalFSM: Feature Subset Selection Method for Malware Family Classification

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Chinese Journal of Electronics 2023年第1期32卷 26-38页

作者： KONG Zixiao XUE Jingfeng WANG Yong ZHANG Qian HAN Weijie ZHU Yufen School of Computer Science and Technology Beijing Institute of Technology School of Space Information Space Engineering University

Malware detection has been a hot spot in cyberspace security and academic research. We investigate the correlation between the opcode features of malicious samples and perform feature extraction, selection and fusion by filtering redundant features, thus alleviating the dimensional disaster problem and achieving efficient identification of malware families for proper classification. Malware authors use obfuscation technology to generate a large number of malware variants, which imposes a heavy analysis burden on security researchers and consumes a lot of resources in both time and space. To this end, we propose the MalFSM framework. Through the feature selection method, we reduce the 735 opcode features contained in the Kaggle dataset to 16, and then fuse on metadata features(count of file lines and file size)for a total of 18 features, and find that the machine learning classification is efficient and high accuracy. We analyzed the correlation between the opcode features of malicious samples and interpreted the selected features. Our comprehensive experiments show that the highest classification accuracy of MalFSM can reach up to 98.6% and the classification time is only 7.76 s on the Kaggle malware dataset of Microsoft.

关键词： Correlation fuses Filtering Cyberspace Machine learning Metadata Feature extraction

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Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

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Chinese Journal of Electronics 2023年第6期32卷 1329-1340页

作者： SUN Haoran WANG Yang LIU Haipeng QIAN Biao Department of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of EducationHefei University of Technology

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel finegrained text-image fusion based generative adversarial networks(FF-GAN), which consists of two modules: Finegrained text-image fusion block(FF-Block) and global semantic refinement(GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

关键词： Visualization fuses Convolution Semantics Linguistics Benchmark testing Generative adversarial networks

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Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features

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Chinese Journal of Electronics 2023年第1期32卷 76-84页

作者： WEN Juan DENG Yaqian PENG Wanli XUE Yiming College of Information and Electrical Engineering China Agricultural University

Deep learning based language models have improved generation-based linguistic steganography,posing a huge challenge for linguistic steganalysis. The existing neural-network-based linguistic steganalysis methods are incompetent to deal with complicated text because they only extract single-granularity features such as global or local text features. To fuse multi-granularity text features, we present a novel linguistic steganalysis method based on attentional bidirectional long-shortterm-memory(BiLSTM) and short-cut dense convolutional neural network(CNN). The BiLSTM equipped with the scaled dot-product attention mechanism is used to capture the long dependency representations of the input sentence. The CNN with the short-cut and dense connection is exploited to extract sufficient local semantic features from the word embedding matrix. We connect two structures in parallel, concatenate the long dependency representations and the local semantic features, and classify the stego and cover texts. The results of comparative experiments demonstrate that the proposed method is superior to the state-of-the-art linguistic steganalysis.

关键词： Deep learning Steganography fuses Semantics Transfer learning Linguistics Feature extraction

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Islanding Protection and Islanding Detection in Low Voltage CIGRE Distribution Network with Distributed Generations

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Smart Grid and Renewable Energy 2014年第6期5卷 152-159页

作者： Ghullam Mustafa Bhutto Ehsan Ali Jagdesh Kumar Muhammad Akram Bhayo Department of Electrical Engineering Quaid-e-Awam University of Engineering Science & Technology Nawabshah Pakistan Department of Energy Technology Aalborg University Copenhagen Denmark

The Power Quality (PQ), security, reliability etc., are the prime objectives of the power system. The protection is developed in such a way that it should be selective, fast, reliable and the cost effective. The study about the islanding protection in Low Voltage (LV) CIGRE distribution and networks like this has been proposed in this paper. This is achieved by developing the protection against the short circuit faults which might appear at the Medium Voltage (MV) bus. The protection of the network with significant penetration of the Distributed Generations (DGs) is a complicated process. The DG units which are directly connected to the grid such as synchronous or induction generators contribute large short power, whereas the DG units which are connected to the grid via inverters carry small amount of the short circuit power. This creates the problems in the protection of the network. If the proper protection coordination measures have not been taken, it might cause the mal-function of the protection devices which put the portion of the power network into the security threats. The selection of the islanding protection devices in this paper is made to protect the network against bi-directional currents at the time of short circuit fault. The LV CIGRE distribution network will enter into islanding if a fault is cleared at the MV bus by the proposed islanding protection devices. It is therefore, essential to detect the islanding in the CIGRE power network. The detection of the island in this network is another major objective of this paper. The detection of the island is proposed by using the technique which is based on the voltage phase angle difference. The simulations are carried out by using DIgSILENT power factory software version 15.0.

关键词： Islanding Protection Loss of Grid (LOG) Over Current Protection Under Voltage Protection fuses Relays Island Detection (ID)

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