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The Strength of Structural Diversity in Online Social Networks

Research 2021年第1期2021卷 1203-1212页

作者： Yafei Zhang Lin Wang Jonathan JHZhu Xiaofan Wang Alex‘Sandy’Pentland Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China Department of Media and Communication and School of Data Science City University of Hong KongHong Kong S.A.R.China Department of Automation Shanghai UniversityShanghai 200444China Media Lab Massachusetts Institute of TechnologyCambridgeMA 02139USA

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective *** a large-scale dataset from a knowledge-sharing website,this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social *** capture the structural diversity of an individual,we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into *** show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation,and the inclusion of a coexposure network provides an additional ingredient to achieve that *** synthetically controlling several possible confounding factors through matching experiments,structural diversity still plays a nonnegligible role in the prediction of personal online social *** work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains,such as social influence and collective intelligence studies.

关键词： collective networks founding

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Conditional Adversarial Domain Adaption based on Self-attention

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 175-188页

作者： Zhao, Li-Quan Zhang, Yu-Peng Teng, Zi-Ming Jiang, Zi-Cong Cui, Ying Kan, Zhong-Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of information and communication Jilin University Changchun130012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan Zhuhai Power Supply Bureau Guangdong Electric Power Corporation Zhuhai519000 China Jilin Power Supply Bureau Jilin Electric Power Corporation Jilin132012 China

Transfer learning is a deep learning method. It transforms knowledge in the source domain and feeds it to the target domain to improve the performance of the target domain model. The conditional adversarial domain adaption method is one of a number of transfer learning methods. It uses Resnet as the feature extraction network;however, Resnet can only determine the relationship between local features, and so its feature extraction accuracy is limited. To overcome this problem, a self-attention layer has been designed in order to determine the relationship among all features. This layer is added between the fourth layer and fifth layer of Resnet. Besides, a cross-entropy function is introduced into loss function, and a sigmoid layer is designed and used as the out layer in conditional adversarial domain adaption. These produce gradient disappearance in the deep network, and influence the discriminator training effect. To solve that problem, a least squares function is used as to design the loss function. The new type of loss function does not require the sigmoid function. Using theoretical analysis, we explain how this new loss function can be used to solve the gradient disappearance problem, thus rendering the network structure more stable. We compare our proposed method with other datasets, including Joint Adaptation Networks, adversarial domain adaptation, conditional adversarial domain adaption, Resnet-50, domain-adversarial neural network on imageCLEF-DA, Office-Home, and Office-31. Our simulation results show that the proposed conditional adversarial domain adaption based on self-attention method has the fastest convergence speed and highest accuracy the among different datasets. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Computer vision

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

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IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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Channel Measurement and Analysis for Human Exhalation and Inhalation in Living Room Scenario

Channel Measurement and Analysis for Human Exhalation and In...

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IEEE Conference on Vehicular Technology (VTC)

作者： Ran Pan Danping He Ke Guan Xiaodong Sun Dajie Jiang Fei Qin State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Beijing Engineering Research Center of High-Peed Railway System Beijing China Frontiers Science Center for Smart High-Speed Railway System Beijing China VIVO Mobile Communication Co.Ltd. Beijing China

The smart home integrates home-related facilities, which can greatly facilitate people’s life. To support applications such as breath detection and health monitoring for the smart home, it is significant to understand the effect of the human body on the characteristics of the wireless channel. In this paper, the channel characteristics for human exhalation and inhalation at 6.5 GHz are measured and analyzed. Based on the measurement results in the living room scenario, the ray tracing (RT) simulator is calibrated and used to analyze the continuous process of exhalation and inhalation. According to the measurement and simulation results, the effect of human exhalation and inhalation processes on the power and phase of the received signal is studied. The analysis of this paper could be useful in guiding the deployment of smart home devices for human body monitoring. The calibrated electromagnetic (EM) parameters of typical indoor materials will support RT-based channel simulation and modeling in indoor scenarios.

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Non-Singular Fixed-Time Robust Containment control for Autonomous Surface Vehicles

SSRN

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SSRN 2023年

作者： Ma, Junda Sun, Hefang Tan, Chong Zhao, Tianyuan Liu, Ke College of Automation Harbin University of Science and Technology Harbin150000 China Provincial Key Laboratory of Complex Intelligent System and Integration Harbin150000 China College of Journalism and Communication Jilin University Changchun130000 China College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150000 China

For the containment control problem of autonomous surface vehicles with external disturbances, a novel non-singular fixed-time control scheme is developed, where the multi-ship system consists of real leaders and followers. In contrast with traditional containment cases studied in most of the existing literature, the real leaders are required to track the desired trajectories, and the followers need to get into the convex hull spanned by the real leaders in fixed time. The method for solving the fixed-time containment control problem is divided into the following steps: Firstly, a fixed-time disturbance observer is designed to estimate the external disturbance for the leaders and the followers, and the estimation result is used to compensate for the input of the control system;Second, combined with fixed-time stability theory and a switch function, a non-singular fixed-time trajectory tracking controller for leaders is designed, such that the position tracking error could be bounded within a fixed time;Third, to achieve formation efficiently, a non-singular fixed-time containment controller strategy incorporating with graph theory is proposed for coordinating followers, such that the fixed-time stability can be ensured. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Graph theory

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P2P transaction method for distributed energy prosumers based on reputation value

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Global Energy Interconnection 2023年第3期6卷 308-323页

作者： Tao Jiang Ting Hua Hao Xiao Linbo Fu Wei Pei Tengfei Ma Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012P.R.China Institute of Electrical Engineering Chinese Academy of Sciences Beijing 100190P.R.China School of Electronic Electrical and Communication Engineering(EECE)University of Chinese Academy of SciencesBeijing 100049P.R.China

Adding a reputation incentive system to peer-to-peer(P2P)energy transactions can encourage prosumers to regulate their trading behavior,which is important for ensuring the efficiency and reliability of P2P *** study proposed a P2P transaction mechanism and game optimization model for prosumers involved in distributed energy sources considering reputation-value ***,the deviation of P2P transactions and the non-consumption rate of distributed renewable energy in P2P transactions were established as indicators to quantify the influencing factors of the reputation value,and a reputation incentive model of P2P transactions for prosumers was ***,the penalty coefficient was applied to the cost function of the prosumers,and a non-cooperative game model of P2P transactions based on the complete information of multi-prosumers was ***,the Nash equilibrium problem was transformed into a nonlinear optimization problem by constructing the modified optimal reaction function,and the Nash equilibrium solution of the game was obtained via a relaxation ***,the modified IEEE 33-node test system based on electricity market P2P and an IEEE 123-node test system were used to analyze and verify the cost and P2P participation of prosumers considering the reputation *** results show that the addition of the reputation incentive system can encourage prosumers to standardize their interactive transaction behavior and actively participate in P2P *** can also improve the operation efficiency of the power grid and promote the perfection of the P2P transaction mechanism.

关键词： P2P Prosumer Non-cooperative game Reputation value Multi-agent of interest Operation optimization

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Research on Emotion Classification of Film and Television Scene Images Based on Different Salient Region

Research on Emotion Classification of Film and Television Sc...

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2020 International Conference on Culture-Oriented Science and Technology, ICCST 2020

作者： Su, Zhi-Bin Liu, Bing Zhang, Li-Jing Ren, Hui Communication University of China School of Information and Communication Engineering China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

ISBN: (纸本)9781728181387

With the rapid development of intelligent image processing and computer vision, interpreting and extracting emotion information from images have become a hot research topic, especially the studies on film and television scene images with rich expressions and certain feelings from the aspect of audience. To investigate the distribution of emotions and its affect on the result of machine learning, this paper established the emotion classification models with different salient and non-salient regions based on self-built dataset from film and television works. Three salient extraction algorithms were used to the regional segmentation, and the color and texture features of each region were extracted. Emotion classification models were established based on SVM and RF algorithms respectively. Since features of the salient region could not cover all the information of the original image, the prediction accuracy of the unprocessed image was the highest. Nevertheless, when models made an error in the emotion prediction of the original image, the experimental results of salient region could get the correct answer under the certain circumstances. Through the analysis, the areas of interest can be used for emotion prediction to reduce the redundancy;however, to make it more accurate, the appropriate salient region extraction algorithm should be selected for different types of images and features. Our study provides data and theoretical guidance for image understanding and retrieval, and it has practical significance for more detailed classification based on the affective sensation. © 2020 IEEE.

关键词： Support vector machines

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Research on Leakage Inductance and Eddy Current Loss of Nanocrystalline High-Frequency Transformers

Research on Leakage Inductance and Eddy Current Loss of Nano...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Yang Liu Xue Liu Zhenhuan Yin Kan Dong Chi Ma Dongdong Cui State Key Laboratory for Traction and Control System of EMU and Locomotive China Academy of Railway Science Corporation Limited Beijing China Key Laboratory of Power Electronics and Electric Drive Institute of Electrical Engineering Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

High-frequency transformers are a vital component of isolated DC/DC converters, and its leakage inductance can be adjusted by changing the winding method of the two-phase windings. Nanocrystalline magnetic cores have been widely applied due to their advantages in high-saturation flux and high magnetic permeability. However, their quite high electrical conductivity can easily lead to appreciable eddy current loss. This paper investigates the mechanism behind the eddy current loss caused by leakage flux by varying the number of windings cross turns, and verifies through simulation that the eddy current loss of nanocrystalline high-frequency transformers increases with increasing leakage inductance.

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Research on Multimodal Algorithms for Multi-routes Planning Based on Niche Technique

Research on Multimodal Algorithms for Multi-routes Planning ...

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2020 International Conference on Culture-Oriented Science and Technology, ICCST 2020

作者： Ren, Hui Shen, Xiaochen Jia, Xiaojun Communication University of China Ministry of Culture and Tourism Beijing Key Laboratory of Modern Entertainment Technology Communication University of China School of Information and Communication Engineering State Key Laboratory of Media Convergence of Communication Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing100024 China

ISBN: (纸本)9781728181387

Multiple routes strategy, classified as a typical multimodal optimization, is an effective method for AGV to tackle the real-world problem. This article majorly explores the traditional optimization algorithm and the evolutionary algorithm for the routes planning problem. Compared with the traditional path planning method, the evolutionary algorithm based on niche technique can successfully avoid the particle interference in the multimodal condition, thereby ensuring the solution diversity. However, various restrictions may be produced when the niche technique cooperates with the evolutionary algorithm. Hence, this paper summarizes some niche techniques with evolutionary algorithms to analyze their merits for solving the multimodal problem. Also, several benchmark functions are involved for further discussing the effectiveness of the niche technology on the multimodal problem and some improvements are conducted for the algorithm to achieve a more effective work. © 2020 IEEE.

关键词： Evolutionary algorithms

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年 27卷 3716-3728页

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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