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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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One-dimensional Continuous Dynamic Modeling and Optimal Sensor Placement Strategy of Heat Storage Tank

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Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical engineering 2025年第8期45卷 3120-3131页

作者： Zhang, Yi Liu, Jianbang Yang, Tingting Liu, Jinfeng Fang, Fang School of Control and Computer Engineering North China Electric Power University Changping District Beijing102206 China School of Electrical and Information Engineering Jiangsu University Jiangsu Province Zhenjiang212013 China Department of Chemical & Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada

Thermocline heat storage tanks, known for their high net energy and exergy efficiency, are becoming a mainstream thermal energy storage solution. However, flow direction switching caused by real-time variations in charging/discharging mass flow creates discontinuities in conventional temperature stratification models. To address this, this study employs continuous smooth functions to approximate the 0-1 variables characterizing working fluid flow direction, resulting in a one-dimensional continuous dynamic stratified temperature model along the tank height. Building on this model, we propose an optimal sensor placement strategy based on sensitivity matrices. The system observability index is redefined using information derived from sensitivity matrices, with the minimum sensor count and optimal positions determined by each location's contribution to system observability. Simulation results demonstrate that the proposed model accurately captures vertical temperature stratification across various operating scenarios when an appropriate smoothing parameter μ is selected. Among all observable sensor combinations with equal sensor numbers, this strategy achieves the smallest mean and variance of RMSE in moving horizon estimation (MHE) observation errors, confirming its superior system observability performance. © 2025 Chinese Society for Electrical engineering. All rights reserved.

关键词： Tanks (containers)

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Beamspace Channel Estimation Via PARAFAC Decomposition for RIS Assisted Millimeter-Wave Multiuser MISO Communications

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7594-7609页

作者： Guo, Xinying Xie, Zongyuan Zhang, Jiankang Chen, Sheng Zhu, Chunhua Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing & Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

The reconfigurable intelligent surface (RIS) with massive low-cost passive reflecting elements integrated on a planar surface has the ability to favourably reconfigure the wireless propagation environment, thereby significantly improving the performance of wireless communication networks. In this work, we consider uplink (UL) channel estimation for the RIS assisted millimeter-wave multiuser multiple-input single-output beamspace system where the base station (BS) is equipped with lens antenna array. This channel state information (CSI) estimation task is extremely challenging for two reasons. First, the BS only has limited number of radio frequency chains but the size of beamspace channel is very large. Second, the number of passive components in the RIS is abundance but they lack signal processing capabilities. By exploiting the parallel factor (PARAFAC) decomposition of the received signals, we derive an iterative estimation algorithm, called unitary approximate message passing (UAMP), to accurately estimate the channels between the BS and the RIS as well as the channels between the RIS and the users. To guide the selection of the system parameters, we provide the uniqueness conditions for our PARAFAC decomposition based channel estimation. To theoretically verify the efficiency of our UAMP algorithm, the Cramér-Rao bound (CRB) of the estimation is also derived. Besides, we investigate the achievable downlink (DL) sum rate for the channel estimation obtained by the proposed algorithm by using the maximum power beam selection, the optimized phase shift matrix and the zero forcing precoding. Extensive simulation results demonstrate the excellent mean squared error (MSE) performance of our UAMP estimation algorithm. In particular, for sufficiently high UL signal-to-noise ratio, the MSE of our channel estimation reaches the CRB. Simulation results also show that the DL sum rate achieved by the estimated CSI is very close to that obtained by the perfect CSI. Theoretica

关键词： Mean square error

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A New Belief-Based Incomplete Pattern Unsupervised Classification Method : Extended Abstract 39

A New Belief-Based Incomplete Pattern Unsupervised Classific...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Zuowei Liu, Zhe Ma, Zongfang Zhang, Yiru Wang, Hao University of Rennes 1 Institut de Recherche en Informatique et Systèmes Aléatoires France Xi'an University of Architecture and Technology School of Information and Control Engineering China Norwegian University of Science and Technology Department of Computer Science Norway

ISBN: (纸本)9798350322279

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets. © 2023 IEEE.

关键词： Uncertainty analysis

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Demand-Side Joint Electricity and Carbon Trading Mechanism

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 14-25页

作者： Hua, Haochen Chen, Xingying Gan, Lei Sun, Jiaxiang Dong, Nanqing Liu, Di Qin, Zhaoming Li, Kang Hu, Shiyan College of Energy and Electrical Engineering Hohai University Nanjing211100 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Department of Automation Tsinghua University Beijing100084 China Automatic Control Laboratory at Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Decarbonization of the whole energy chain has been recognized as a measure to tackle the global challenge of climate change, and significant progress has already been made on the generation side to integrate renewable energy. However, the demand side is the single largest underlying factor in shaping decarbonization roadmap. Hence, the carbon emission cost should also be shared by the users according to their power consumption. In this paper, a joint electricity-carbon trading framework is designed to reduce the carbon emission through trading and demand response. A delayed carbon emission liability settlement for asynchronous markets is proposed to ameliorate the users' optimal decision from single-point optimization to interval-based optimization. To develop the optimal strategy of trading within the proposed mechanism, an improved proximal policy optimization (PPO) algorithm based on Monte Carlo reward sampling is applied. Simulation studies reveal that, compared with the market without carbon trading and users without delayed settlement, the proposed mechanism has achieved a carbon emission reduction by 40.7% and 12.7% respectively. Simulations also show the algorithm's training efficiency can be significantly improved with the proposed Monte Carlo sampling method. © 2023 IEEE.

关键词： Real time systems

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Mixture-of-Experts as Continual Knowledge Adapter for Mobile Vision Understanding

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IEEE Transactions on Mobile Computing 2025年

作者： Guo, Bicheng Zhou, Conghao He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou310027 China Xidian University School of Telecommunications Engineering China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Continual machine learning in the context of limited computational resources and data availability is critical in the connected digital world. Current intelligent applications predominantly rely on deep learning models requiring labor/computation-intensive training. These models often struggle to adapt effectively to new data while preserving performance on previously learned knowledge. In this paper, we introduce a lightweight method for continual knowledge adaptation that can address these challenges. To prevent disruption of the existing services, we propose a Mixture-of-Experts (MoE) adapter that integrates seamlessly with the existing vision model to encode new data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. The MoE technique enables scaling up the parameters of the adapter while maintaining a relatively low computation, making it fit for constrained devices in mobile computation scenarios. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between the existing knowledge and the information extracted from new data. The timing of employing the fusion module is further investigated. We find that it is conducive in scenarios where the task's performance requirements are enhanced. The MoE adapter and knowledge fusion module are integrated at each stage with minimal trainable parameters, efficiently optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed method. Specifically, the proposed method prevents an accuracy drop of 43.02% on the previous data compared to the continual train method, while achieving an accuracy of 44.81% on the new data, which is even 0.34% higher than fully training a new model. © 2002-2012 IEEE.

关键词： Deep learning

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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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Enhanced Vgg16 for Medical Image Zero Watermarking: Integrating Attention, Skip Connections, Depth-Separated Convolution, and Dual-Logistic Encryption

SSRN

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SSRN 2024年

作者： Li, Jingyou Rongle, Wei Xi, Xiaotian Zhang, Guangda Yang, Zixin Zhang, Fengshan 1. School of Computer and Control Engineering Qiqihar University 2. Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University

The paper proposes an enhanced VGG16 network((referred to as DS-ResVGG16 with CBAM)) that incorporates the attention mechanism CBAM, skip connections, and depth-separable convolution to solve the medical image content protection problem. The three innovative improvements of the enhanced VGG16 network are reflected in the following: first, replacing the regular convolution with depth-separable convolution substantially improves the computational efficiency and parameter utilization of the network;second, the introduction of the CBAM after the 2nd, 3rd, 4th, and 5th max-pooling layers further strengthens the network's capability to recognize and respond to crucial features.;and lastly, by adding a skip connection after the 2nd, 3rd, and 4th CBAM modules followed by the addition of skip connections, the transfer and integration of features were enhanced, improving the learning ability and generalization of the model. The improved network extracts Deep features from the original medical images, and binary feature vectors are generated using the mean hash algorithm. To further strengthen content protection security, this study innovatively adopts an encryption technique based on dual-logistics mapping and Joseph's ring for high-strength encryption of watermarks. During the generation of the zero watermark, the encrypted watermark and the feature vectors of the medical image are subjected to an iso-orthogonal operation, which ensures the concealment and security of the embedding. The validity and reasonableness of the modules and locations added by the scheme are verified and compared through experiments. The experimental results show that the scheme is robust enough to handle typical and geometric attacks. The medical image content protection problem is realized. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Zero Watermarking Algorithms for Medical Images Based on Cnn and Transformer with Dual-Branch Network

SSRN

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SSRN 2024年

作者： Li, Jingyou Wei, Rongle Xi, Xiaotian Zhang, Guangda Yang, Zixin Zhang, Fengshan 1. School of Computer and Control Engineering Qiqihar University 2. Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University

The current research proposes a new zero-watermarking approach that aims to protect medical images from tampering by utilizing the power of deep learning. In this paper, we construct an innovative dual-branch network to capture subtle features of pictures and use them efficiently. First, one of the branches of the dual-branch network is to analyze the local details of the medical images by an enhanced convolutional neural network (specifically the Skip-connected VGG16, i.e., Res-VGG16). In contrast, the other branch employs the state-of-the-art Swin transformer v2 structure to parse the global context of the medical images. Second, adding a spatial transform network and a Gaussian filter as preprocessing before the two-branch network can further overcome the low robustness of medical images to geometric and noise attacks. Thirdly, we use a feature fusion method with a maximum voting mechanism to merge the features extracted from the two-branch network. We generate feature vectors of medical images using the DCT method and achieve watermark encryption by utilizing logistic mapping and a hash function. Finally, we generate a zero watermark by applying logical operations to the encrypted watermark and the medical image feature vectors. The implementation results show that the algorithm can effectively overcome the tampering problem of medical images and can recover the original data from the encrypted watermark with good robustness under conventional and geometric attacks. © 2024, The Authors. All rights reserved.

关键词： Hash functions

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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