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检索条件"任意字段=5th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2022"

共 4257 条记录，以下是41-50 订阅

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GNSS Receiver Anti-jamming Algorithm Based on Estimation of Array Steering Vector 5

GNSS Receiver Anti-jamming Algorithm Based on Estimation of ...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Wang, Dawei Wang, Xibin Wang, Junhong Du, Peng Yao, Xiaoqiong North China Institute of Astronautic Engineering Langfang Key Laboratory of Information Acquisition and Application School of Electronic and Control Engineering Lang Fang China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

An adaptive blind beam-forming algorithm based on array steering vector estimation is proposed to suppress interference signals received by the satellite navigation receivers. the received data are firstly projected to signal plus noise subspace to suppress strong interference in the first stage. then the output data from the signal plus noise subspace are sent to carrier phase estimation modules, where each satellite navigation signal is tracked to estimate array steering vector. With the estimated steering vector, beams pointing to the direction of navigation satellites can be formed. Simulation results show that the proposed algorithm is valid for interference suppression in presence of interference, and the anti-jamming performance is much better than the power inversion (PI) algorithm and maximum carrier to noise ratio (MCNR) with three successive integrations. © 2022 ieee.

关键词： Vectors

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VPN Traffic Identification Based on Tunneling Protocol Characteristics 5

VPN Traffic Identification Based on Tunneling Protocol Chara...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Li, Yu Wang, Fei Chen, Shuhui National University of Defense Technology College of Computer Changsha China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Virtual Private Network (VPN) provides a secure, encrypted, and anonymous communication between remote networks. More and more people are inclined to use, with the raising network security and privacy protection awareness. However, VPNs are also widely abused by malicious pursuers, and thereby distinguishing VPN traffic for father judgment is of the utmost importance for network supervision. In this paper, we focus on the characteristics of VPN tunneling protocols, and then extract four categories of traffic features, including two novel entropy features, for VPN traffic identification. Feature evaluation upon real-world VPN traffic demonstrates, that all these features can notably embody differences between VPN and Non-VPN traffic, and the entropy features work still well even when applications traffic is mixed. Finally, the proposed VPN traffic identification method achieves the accuracy of 99.02% and 95.32% respectively on self-gathered and public VPN datasets, remarkably higher than the existing research. © 2022 ieee.

关键词： Virtual private networks

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Epidemic Data Analysis Based on Data Visualization technology 5

Epidemic Data Analysis Based on Data Visualization Technolog...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Jiao, Shizuo Nanjing Foreign Language School International Department Jiangsu Nanjing210008 China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

the outbreak of COVID-19 at the end of 2019 has had a heavy impact on countries around the world, and its rapid spread has posed a huge challenge to the prevention and control work of all countries. through data visualization technology, this paper comprehensively analyzes the epidemic data in China from multiple levels, including comparison analysis based on time span and comparison analysis based on countries. At the same time, this paper also analyzes the significant impact of the epidemic on China's national economy from the macro and micro levels. Finally, the linear regression model is used to predict the GDP data during the epidemic period, and the comparison between the real and predicted GDP data during the epidemic period is made. It was found that the epidemic had a great impact on China's national economy in the early stage, but the impact gradually weakened as time went by. Epidemic data analysis based on data visualization technology can provide powerful data support for epidemic prevention and control. © 2022 ieee.

关键词： COVID-19

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Multi-modal Graph Attention Network for Video Recommendation 5

Multi-modal Graph Attention Network for Video Recommendation

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Liu, Huizhi Li, Chen Tian, Lihua Xi'an Jiaotong University School of Software Engineering Xi'an China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

In view of the problems of cold start and data interaction in recommendation systems, and most current recommendation algorithms ignore the diversity of data types, the combination of multimodal data and knowledge graph is bound to improve the pertinence of video recommendation. In this paper, we propose Multi-modal Knowledge Graph Attention Network (MMKGV) model, and all the entity nodes of the knowledge graph are innovatively introduced into multimodal information. the high-order recursive node information dissemination and information aggregation are carried out on the multimodal knowledge graph through the graph attention network. In the model, the triplet function of the knowledge graph is used to construct the triplet inference relationship, and the vector representation generated by the final aggregation is used for recommendation. through extensive experiments on two public datasets TikTok and Kwai, the results show that the MMKGV can effectively improve the effect of video recommendation compared with other comparison algorithms. © 2022 ieee.

关键词： Recommender systems

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Design of Data Processing Software for Raindrop Spectrometer Based on LabVIEW 5

Design of Data Processing Software for Raindrop Spectrometer...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Yefeng, Xu Ruili, Jiao Minsong, Huang Beijing Information Science and Technology University School of Information and Communication Engineering Beijing China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics Cas Beijing China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

this paper studies design of data processing software for raindrop spectrometer. It includes reading, decompression, data processing, storage and display. the difficulties are data decompression and the software architecture design. this software applies digital image processing technology to process the raindrop data measured by dual-line raindrop spectrometer data processing software (DRDPS) based on LabVIEW, realizes the display from data file to particle image, stores the obtained information and geometric parameters of precipitation particles, and the obtained meteorological information of precipitation intensity, precipitation particle shape, precipitation type and precipitation particle spectrum is displayed in the front panel of the software. the test results show that the software meets the system requirements completely and can be applied to the research of cloud precipitation and other atmospheric science relevant fields. © 2022 ieee.

关键词： Software testing

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A Novel Character-Word Fusion Chinese Named Entity Recognition Model Based on Attention Mechanism 5

A Novel Character-Word Fusion Chinese Named Entity Recogniti...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Luo, Jinshang Zou, Xinchun Hou, Mengshu University of Electronic Science and Technology of China Information Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Named Entity Recognition (NER) is a fundamental task in natural language processing. Compared with English NER, the difficulty of Chinese NER lies in word segmentation ambiguity and polysemy. Aiming at the issue, a novel character-word fusion Long Short-Term Memory (LSTM) model combined with the sentence-level attention mechanism (CWSA-LSTM) is proposed. Firstly, the method encodes the representations of characters and words through the pretrained models. the word information is incorporated into the character sequence by matching the potential word with a lexicon. then the feature vectors are fed into the LSTM layer to learn contextual information. the attention mechanism is utilized to capture the tightness of the correlation in the sentence. Experiments on benchmark datasets demonstrate that CWSA-LSTM outperforms other state-of-the-art methods, and verify the effectiveness of character-word fusion. For the MSRA dataset, CWSA-LSTM achieves a 2.46% improvement in F1 score over baseline Lattice LSTM. © 2022 ieee.

关键词： Long short-term memory

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Research on Fault Identification Method of Power System communication Network Based on Deep Learning 5

Research on Fault Identification Method of Power System Comm...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Wang, Yuting Hao, Ting Wang, Hai State Grid Gansu Electric Power Company Digital Business Department Lanzhou China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

With the communication network scale, the increasing bandwidth and complexity of the constant improvement of the quality of network service, and user requirements, an urgent need to intelligent communication system of the current high speed communication network for effective and reliable management, and fault management is becoming more difficult and important than ever, when the network produces a fault or failure, Many thousands of alarms are generated in a short period of time, so analyzing the signals of these alarms becomes more complicated. Some existing alarm analysis systems have some shortcomings, such as poor scalability, difficulty in dealing with complex situations, and lack of learning ability. this paper proposes a method of fault identification and alarm correlation analysis based on deep learning algorithm. Combined with deep reinforcement learning technology, a sleep scheduling strategy based on multi-level is designed to reduce energy consumption, and its effectiveness is verified by simulation. Experimental results show thAT this method can overcome the limitations of common alarm correlation analysis methods, and create favorable conditions for improving the efficient utilization of spectrum resources in private networks. © 2022 ieee.

关键词： Learning algorithms

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Dynamic RAN Slicing with Effective Isolation under Imperfect CSI 5

Dynamic RAN Slicing with Effective Isolation under Imperfect...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Zhang, Jian Zu, Yunxiao Zhang, Yong Hou, Bin Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Network slicing is a key technology for addressing the issue of differentiated performance requirements of diversified services in mobile networks. Radio access network (RAN) slicing is a challenging task, as different levels of isolation requirements need to be considered. In this work, we investigate a network slicing problem for the downlink RAN of a cellular network and focus on three major 5G services, namely Ultra-Reliable Low-delay communication (URLLC), and Large-scale Machine Type communication (mMTC), Enhanced Mobile Broadband (eMBB). To be more practical, only the imperfect channel state information (CSI) is assumed to be available at the base station (BS) and allocate time-frequency resources dynamically to users through Lyapunov optimization, while ensuring slice isolation and setting different Quality-of-Service (QoS) requirements according to different slice types. Due to the scarcity of wireless resources, this paper aims to reduce resource usage. Simulation shows that this experiment is effective in ensuring QoS requirements, providing isolation and minimizing resource consumption. © 2022 ieee.

关键词： Radio access networks

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Hierarchical Feature Fusion and Multi-scale Cost Aggregation for Stereo Matching 5

Hierarchical Feature Fusion and Multi-scale Cost Aggregation...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Zhang, Jiaquan Li, Pengfei Wang, Xinan Zhao, Yong Shenzhen Graduate School of Peking University School of Information Engineering China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

To further improve the accuracy of disparity estimation in ill-posed regions and weak texture regions, in this paper we propose HFMANet: which is a stereo matching method based on hierarchical feature fusion and multi-scale cost aggregation. Specifically, we first propose a hierarchical feature fusion module, which innovatively fuses low-level features and high-level features to obtain rich semantic information while retaining the edge information of the image. Secondly, we propose a multi-scale cost aggregation module to extract rich global context information. At the same time, the layer-by-layer fusion optimization helps increase the receptive field to capture more structural information, reduce the dependence on local information, and help the disparity estimation of ill-posed regions and weak-textured regions. Comprehensive experiments are conducted on the SceneFlow and KITTI datasets, and achieve competitive results, which proves the effectiveness of the proposed method. © 2022 ieee.

关键词： Textures

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Attribute Analysis and Diagnosis of LUNG CT Images of COVID-19 5

Attribute Analysis and Diagnosis of LUNG CT Images of COVID-...

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5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Xu, Qi Shi, Xiaohong Li, Qingshu Huang, Wei Yang, Peng Shanghai Maritime University College of Information Engineering Shanghai201306 China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Computed Tomography (CT) is an authoritative verification standard for patients with Corona Virus Disease 2019 (COVID-19). Automatic detection of lung infection through CT is of great significance for epidemic prevention and control and prevention of cross-infection. the accuracy of existing lung CT image segmentation methods is not high, and due to the privacy protection measures of hospitals, the number of COVID-19 lung CT data sets is too small, which is prone to over-fitting during training. In this paper, we propose a qualitative mapping model for the diagnosis and localization of COVID-19 lesions. the binary image processed by U-net network is used as input, and lung CT is segmented as four attributes, and attribute diagnosis is carried out with the help of correlation matrix and transformation degree function. Experiments show that this method not only avoids the over-fitting risk of data sets, but also increases the robustness of data. Experiments also prove that this design has higher accuracy than the simple neural network learning. © 2022 ieee.

关键词： Image segmentation

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