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Parallel Vision ■ Image Synthesis/Augmentation

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 782-784页

作者： Wenwen Zhang Wenbo Zheng Qiang Li Fei-Yue Wang the School of Computer Science Shaanxi Normal UniversityXi’an 710119China the School of Computer Science and Technology Wuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,Scene understanding is an essential task in computer *** ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans *** vision is a research fram... 详细信息

关键词： instru explanation computer

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Character recognition competition for street view shop signs

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National science Review 2023年第6期10卷 27-31页

作者： Jingqun Tang Weidong Du Bin Wang Wenyang Zhou Shuqi Mei Tao Xue Xing Xu Hai Zhang Shanghai Byte Dance Technology Co. Ltd School of Control Science and Engineering Zhejiang University School of Computer Science and Technology University of Science and Technology of China School of Computer Science and Technology Tsinghua Univeristy Shenzhen Tencent Technology Co. Ltd School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Store signboards provide important information in street view images,andcharacter recognition in natural scenes is an important research direction in computer *** view store signboard character recognitio...

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B+-Tree-Quantization-Based Shared Secret Key Extraction Scheme From Atmospheric Optical Wireless Channel

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IEEE Transactions on Communications 2025年第5期73卷 3394-3408页

作者： Pan, Gang Chen, Chunyi Changchun University of Science and Technology School of Computer Science and Technology Changchun130022 China Changchun University of Science and Technology School of Computer Science and Technology Key Laboratory of Photoelectric Measurement & Control and Optical Information Transfer Technology Changchun130022 China Chongqing Research Institute Changchun University of Science and Technology Chongqing401135 China

To reduce key disagreement rate and increase key generation rate, this paper proposes a lightweight and robust shared secret key extraction scheme from atmospheric optical wireless channel. A conception of grouping samples by using B+ tree and quantizing groups is introduced to improve the balance of distribution of channel measurement values in quantization intervals. Based on the conception, a basic B+ tree quantization algorithm (B-BPTQ) and a compensation-technology-based B+ tree quantization algorithm (C-BPTQ) are designed. A LDPC-based multi-scale information reconciliation algorithm (LDPC-MSIR) is put forward to simplify information reconciliation and reduce its computation complexity. An enhanced randomness post-processing algorithm is formulated to increase statistical randomness of initial key sequence. Finally, the performance of the proposed scheme is validated by numerical simulations and field experiments. The experimental results show that it has better performance in terms of key disagreement rate, key generation rate and key randomness compared with other schemes. © 1972-2012 IEEE.

关键词： Optical communication

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Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

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computers, Materials & Continua 2024年第3期78卷 4343-4361页

作者： Yunchang Liu Fei Wan Chengwu Liang School of Computer and Data Science Henan University of Urban ConstructionPingdingshan467036China School of Electrical and Control Engineering Henan University of Urban ConstructionPingdingshan467036China

Traffic flow prediction plays a key role in the construction of intelligent transportation ***,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very *** of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between ***,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial *** paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic *** combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic *** on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

关键词： Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism

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Charging Infrastructure for Electric Vehicles Forecasting: A Study Based on ARIMA Algorithm and OLS Regression Model

Charging Infrastructure for Electric Vehicles Forecasting: A...

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2024 International Conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Ma, Haoxin Wang, Jiaqi Zhu, Mingkai School of Computer and Control Engineering Qiqihar University Qiqihar China School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9798350369212

This study comprehensively analyzes the future production, sales and charging infrastructure expansion of new energy electric vehicles in China over the next decade, including production, sales and charging infrastructure expansion. The study uses the ARIMA algorithm, combined with industry data analysis, to forecast the future development of the industry. In addition, the impact of new energy vehicles on the traditional automotive industry is analyzed through OLS regression analysis to gain insight into the challenges and opportunities brought about by the rise of new energy vehicles. This study provides valuable experience for the development of new energy vehicles and global sustainable development strategies. © 2024 IEEE.

关键词： ARIMA algorithm New energy electric vehicles OLS regression analysis

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A multistage intrusion detection method for alleviating class overlapping problem

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Neural Computing and Applications 2025年第6期37卷 4853-4867页

作者： Pang, He Jin, Fusheng Chen, Mengnan Jiang, Yutong Yuan, Ye Beijing Aerospace Automatic Control Institute Beijing100854 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Intrusion detection system (IDS) can identify abnormal network traffic and attacks, which is an important means of network security defense. However, some intrusion data are often disguised as normal data for transmission, which increases the difficulty of intrusion data classification. In addition, the existing packet-based or flow-based data feature extraction methods result in low feature dimensions, causing the problem of class overlapping between different categories with the same features. To clarify, overlapping samples are those that overlap between erroneous samples and correct samples. Nonoverlapping samples are those in the test set that do not match the characteristics of the already identified overlapping samples and are therefore considered nonoverlapping samples. Therefore, the detection effect of some attacks with high concealment is poor. In order to solve the above problems, this paper proposes a multistage intrusion detection method: an existing intrusion detection model with higher classification performance (OBLR) is used to predict the data in the first stage. In the second stage, for the overlapping data in the confusing data, the method learns the distribution of each feature group according to the randomly divided "intermediary set," and realizes the prediction of overlapping samples through the prior distribution knowledge, and achieves efficient classification of overlapping samples without increasing the computational burden of the model. For nonoverlapping data in the confusing data, KPCA (kernel principal component analysis) dimension elevation is used in the third stage to capture more detailed difference information between samples, and GMM (Gaussian mixed model) is combined with the "representative samples" proposed in this paper to assist classifier classification. At the same time, all the base classifiers are integrated through LTR (learning to rank) to improve the classification effect of the model for nonoverlapping data in the

关键词： Network intrusion

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Enhancing surrogate-assisted evolutionary optimization for medium-scale expensive problems: a two-stage approach with unsupervised feature learning and Q-learning

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Neural Computing and Applications 2024年第25期36卷 15545-15565页

作者： Gong, Yiyun Yu, Haibo Kang, Li Sun, Chaoli Zeng, Jianchao School of Electrical and Control Engineering North University of China Taiyuan030051 China School of Computer Science and Technology North University of China Taiyuan030051 China School of Environment and Safety Engineering North University of China Taiyuan030051 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan030024 China

This paper presents a novel two-stage progressive search approach with unsupervised feature learning and Q-learning (TSLL) to enhance surrogate-assisted evolutionary optimization for medium-scale expensive problems. The method attempts to address the challenges posed by multi-polar and multi-variable coupling properties in such problems. During the iteration, TSLL splits the optimization process into two distinct stages. First, two unsupervised feature learning techniques: principal component analysis (PCA) and Autoencoder, are utilized to improve the representation of potential optimal samples in the solution space. PCA is used for feature reduction, extracting the most important features. On the other hand, Autoencoder focuses on reconstructing features within the medium-scale solution space. To ensure comprehensive exploration of the entire solution space, TSLL employs two distinct surrogate modeling approaches along with Q-learning in the second stage to dynamically select the mutation strategy for the differential evolution operator. Numerous experiments demonstrate the superiority of TSLL over five state-of-the-art surrogate-assisted approaches and two sophisticated evolutionary algorithms including the winner of CEC 2017 on medium-scale benchmark problems and a wind farm layout problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Reinforcement learning

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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