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2022 Chinese Automation Congress, CAC 2022

作者： Ma, Tao Huang, Weijian Du, Linlin Wang, Chun Ge, Shuangquan Liu, Yunfei Yu, Kun China Academy of Engineering Physics Institute of Computer Application Mianyang China Naval University of Engineering National Key Laboratory of Science and Technology on Vessel Integrated Power System Wuhan China

ISBN: (纸本)9781665465335

Robust registration is able to register the complementary information captured by multimodal images. Owning to the nonlinear intensity differences and repeating patterns in multimodal images, many classic descriptors are unable to achieve good performance. Aiming at this problem, a remote sensing image registration method based on key point description and filtering is proposed. First, this work utilizes a new keypoint descriptor with Log-Gabor filters to characterize the oriented phase congruency information in multimodal images. Then, a keypoint filtering is performed on all descriptors by mean-shift clustering to remove the repeating patterns. This approach is evaluated on three commonly used data sets, which are used for structure map construction and image registration comparisons. The experimental results show that the proposed method is able to preserve highly similar structure features and remove most repeating keypoints in multimodal images, which indicates this method is efficient and can achieve good registration performance. © 2022 IEEE.

关键词： Image registration

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Research and application of Machine Learning in Automatic Program Generation

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Chinese Journal of Electronics 2020年第6期29卷 1001-1015页

作者： ZHANG Xiaojiang JIANG Ying Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology

With the development of artificial intelligence,machine learning has been applied in more and more *** order to improve the quality and efficiency of software,automatic program generation is becoming a research *** recent years,machine learning has also been gradually applied in automatic program *** trees,language models,and cyclic neural networks have been applied in code generation,code completion and code knowledge *** efficiency of software development has been improved to a certain extent using machine *** at the automatic program generation,this paper analyzes and summarizes the models of machine learning,the modifications involved in the models and the application *** research direction is discussed from the aspects of programmer behavior and automatic program generation of machine learning.

关键词： automatic programming data mining decision trees learning (artificial intelligence) neural nets software engineering programmer behavior cyclic neural networks language models decision trees code knowledge mining code completion code generation automatic program generation machine learning Machine learning Code generation Code completion Code knowledge mining application effect

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Research on Two-stage Conveying Bar Counting and Dividing System Based on Vision 21

Research on Two-stage Conveying Bar Counting and Dividing Sy...

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21st International Symposium on Distributed Computing and applications for Business Engineering and Science, DCABES 2022

作者： Chen, Guojun Wu, Yang Wuxi Taihu University Jiangsu Key Laboratory of IoT Application Technology Wuxi214064 China College of Computer Internet of Tings Engineering Wuxi Taihu University Wuxi214064 China

ISBN: (纸本)9781665454629

A vision-based automatic bar counting system for two-stage conveying bars is proposed. The system solves the counting problems of sticking and relative sliding of a large number of rebar stacks through image processing algorithms. Through the improvement of the splitting end splitting machine, the design of two-step splitting and visual verification is adopted to achieve accurate splitting of stacked rebars. The system obviously reduces the requirements for installation space, and can make more effective use of the space in the rebar workshop. The system has fast running speed, high accuracy, small changes to the production line, and easy maintenance. It can not only meet the needs of production, but also greatly improve the efficiency of the existing counting system. It is suitable for rebar workshops with limited installation space. The experimental results show that the system can accurately count the bars and can accurately realize the steel separation. For bars with a diameter of 8-12mm, the counting accuracy is over 99.85%, and for bars with a diameter greater than 12mm, the counting accuracy is over 99.96%. © 2022 IEEE.

关键词： Image processing

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Optical polarized orthogonal matrix

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Photonics Research 2025年第2期13卷 373-381页

作者： SHUJUN ZHENG JIAREN TAN XIANMIAO XU HONGJIE LIU YI YANG XIAO LIN XIAODI TAN Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal UniversityFuzhou 350117China Department of Electrical and Computer Engineering Duke UniversityDurhamNorth Carolina 27708USA College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal UniversityFuzhou 350117China

Multiplexing technology serves as an effective approach to increase both information storage and transmission ***,when exploring multiplexing methods across various dimensions,the polarization dimension encounters limitations stemming from the finite orthogonal *** that only two mutually orthogonal polarizations are identifiable on the basic Poincarésphere,this poses a hindrance to polarization *** overcome this challenge,we propose a construction method for the optical polarized orthogonal matrix(OPOM),which is not constrained by the number of orthogonal ***,we experimentally validate its application in high-dimensional multiplexing of polarization *** explore polarization holography technology,capable of recording amplitude,phase,and polarization,for the purpose of recording and selective reconstruction of polarization *** research reveals that,despite identical polarization states,multiple images can be independently manipulated within distinct polarization channels through orthogonal polarization combinations,owing to the orthogonal selectivity among information.

关键词： two mutually orthogonal polarizations optical polarized orthogonal matrix construction method multiplexing methods polarization dimension multiplexing orthogonal polarization combinations polarization holography

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Variational Graph Autoencoders Method Based on Attentional Mechanisms for Overlapping Community Detection 12th

Variational Graph Autoencoders Method Based on Attentional M...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Wen, Kunhui Lin, Mugang Zhu, Xuanying Zhao, Huihuang College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421008 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421008 China

ISBN: (纸本)9789811969003

Detecting overlapping communities of an attribute network is an important and difficult issue in network science. Traditional methods for overlapping community detection generally considered only its topological structure and neglected node attribute so that the detected communities could be inaccurate. Graph neural networks (GNN) have recently gained attention because of their learning representation ability for node attributes and the topology of a graph and demonstrating superior performance in natural language processing, data mining and pattern recognition, etc. However, GNN methods are still less for overlapping community detection. In this paper, we present an overlapping community detection method based on GNN for attribute networks. It applies a variational graph autoencoder based on attentional mechanisms to learn the representation of nodes in the graph and enhances the representation learning capability of GNN through semi-supervised learning. Various real-world networks of publicly available datasets are used to verify the effectiveness of the proposed approach. The experimental results show that the proposed method outperforms many other methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graph neural networks

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Reinforcement Learning-Based Explainable Recommendation over Knowledge Graphs with Negative Sampling 19

Reinforcement Learning-Based Explainable Recommendation over...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhang, Siyuan Ouyang, Yuanxin Liu, Zhuang Rong, Wenge Xiong, Zhang Beihang University State Key Laboratory of Software Development Environment Beijing China Beihang University Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beijing China

ISBN: (纸本)9798350346558

Introducing knowledge graphs (KGs) into the recommender systems not only improves their performance but also enhances the interpretability. However, most KG-based recommendation methods have the problem of inefficiency and ex-post explanation, which reinforcement learning (RL) methods can solve properly. Most existing RL-based methods for explainable recommendations only consider positive rewards when designing the reward part of the RL environment, which is defective and misleads the policy of the RL agent. To address this problem, we propose Reinforced Knowledge Graph Reasoning with Reinforced Negative Sampling (RKGR-RNS) by introducing a negative sampling method into RL-based recommendation, which refines the reward mechanism to help optimize the agent's policy. And a judge module is proposed to improve the performance of the recommender system further. Experiments on three real datasets demonstrate that our method is better than the state-of-the-art baseline. © 2022 IEEE.

关键词： Reinforcement learning

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A Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Zhu, Zhirong Liu, Yiwen Gao, Yanxia Wen, Wenkan Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

With the rise of data storage computing and IoT technology. The increase in data volume and user demand, the accurate delivery of data and low latency during transmission become important factors that affect the end-user experience. To address this issue, previous authors have proposed the concept of edge computings. In the general environment of edge computing, reasonable scheduling of edge caches can largely achieve low latency and high efficiency, thus improving user experience. In this paper, based on existing research, we propose a combination of a joint learning framework for cache prediction based on region popularity and an edge collaborative cache value optimization method to further improve cache hit rate and cache utilization efficiency. The method obtains excellent expected results through simulation experiments. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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Hypernetwork Representation Learning With the Transformation Strategy 22

Hypernetwork Representation Learning With the Transformation...

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6th International Conference on High Performance Compilation, Computing and Communications, HP3C 2022

作者： Zhu, Yu Zhao, Haixing Huang, Jianqiang Wang, Xiaoying Qinghai University Department of Computer Technology and Applications Xining810000 China Qinghai Normal University State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810000 China

ISBN: (纸本)9781450396295

In real life, there are many cases that cannot be described by the network abstracted as the graph, but can be described perfectly by the hypernetwork abstracted as the hypergraph. Different from the network, the hypernetwork structure is more complex and poses a great challenge to the existing network representation learning methods. Therefore, in order to overcome the challenge of the hypernetwork structure, a hypernetwork representation learning method with the transformation strategy is proposed. Firstly, as three types of transformation strategies from the hypergraph to the graph, line graph, incidence graph and 2-section graph are combined into three types of integral graphs with the hyperedge information, namely incidence graph + 2-section graph, line graph + incidence graph and line graph + incidence graph + 2-section graph. Secondly, a shallow neural network algorithm is trained respectively on five types of networks abstracted as incidence graph, 2-section graph, incidence graph + 2-section graph, line graph + incidence graph and line graph +incidence graph + 2-section graph to obtain node representation vectors. Finally, the evaluation experiment is conducted on four different types of hypernetwork datasets. The experimental results demonstrate that the node classification performance of 2-section graph is better than that of other graphs, and the link prediction performance of incidence graph + 2-section graph is better than that of other graphs. © 2022 ACM.

关键词： Graph theory

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Deep Learning Image Steganalysis Method Fused with CBAM 12th

Deep Learning Image Steganalysis Method Fused with CBAM

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Chen, Huasuo Jiao, Ge College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421002 China

ISBN: (纸本)9789811969003

Steganography is a critical technical tool for preventing the disclosure of sensitive information. The detection performance of picture steganography algorithms based on deep learning has to be enhanced in tandem with the ongoing improvement of adaptive steganography algorithm performance. In this paper, a new model SRNet-CBAM based on SRNet fusion channel attention module and spatial attention module was presented to address the challenge of adaptive steganography analysis and the difficulty of model focused analysis for picture favorable regions. In three different embedding rates of WOW, S-UNIWARD, and HUGO algorithms, the experimental results reveal that the SRNet-CBAM model increases the accuracy of the SRNet model by 1.36% on average. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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Research on Fast Adaptive Transmission Models for International Inland Port Based on Edge Intelligence 10

Research on Fast Adaptive Transmission Models for Internatio...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yiwen, Liu Zhirong, Zhu Tangyan Wenkan, Wen Xiaoning, Peng Yuanquan, Shi Huaihua University School of Computer and Artificial Intelligence Huaihua Hunan418000 China Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Huaihua Hunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9798350312461

In this paper, we propose a logistics International Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. The proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua International Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations. © 2023 IEEE.

关键词： Resource allocation

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