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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xu, Yanqing Song, Enbin Shi, Qingjiang Chang, Tsung-Hui The Chinese University of Hong Kong School of Science & Engineering Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Sichuan University College of Mathematics Chengdu610064 China Tongji University School of Software Engineering Shanghai201804 China

ISBN: (纸本)9781728163277

To cope with the bottlenecks of the high computational complexity and excessive inter-connection communication in the conventional centralized baseband processing architecture, the decentralized baseband processing (DBP) architecture has been proposed, where the antennas are partitioned into multiple clusters, each connected to a local baseband unit (BBU). In this paper, we are interested in the distributed channel estimation (DCE) method under such DBP architecture, which is rarely studied in the literature. Our goal is to devise a DCE algorithm that can perform as well as the centralized scheme but with a small inter-connection communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose an aggregate-then-estimate based DCE algorithm. In contrast to the existing DCE algorithm which requires iterative information exchanges among BBUs, our algorithm only requires one round-trip communication between the nodes. Experiment results are presented to demonstrate the efficacy of the proposed DCE algorithm. © 2023 IEEE.

关键词： channel estimation decentralized baseband processing Massive MIMO

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A Multi-attribute Decision-making based Importance Assessment Method of Sensor Nodes 3

A Multi-attribute Decision-making based Importance Assessmen...

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3rd International Symposium on Computer Technology and Information science, ISCTIS 2023

作者： Duan, Xiusheng Yang, Jingchao Kong, Dehan Zhang, Weilong Hebei Polytechnic Institute Department of Artificial Intelligence and Big Data China Hebei Jiaotong Vocational and Technical College Shijiazhuang China Hebei Provincial University Road Traffic Perception Intelligent Application Technology Research and Development Center China Hebei University of Environmental Engineering Department of Information Engineering Qinhuangdao China

ISBN: (纸本)9798350325386

In this paper, an air defense early warning sensor network node importance assessment method based on multi-attribute decision was proposed. Firstly, according to the characteristics of the air defense early warning sensor network, the factors that affect the node importance degree were analyzed, and the node importance degree was divided into three aspects, which were performance importance degree, function importance degree and structure importance degree. Secondly, three calculation methods of importance degree were introduced. Then the synthesis method of comprehensive importance degree based on TOPSIS method was proposed. Finally, the validity of the method and model proposed in this paper was verified. © 2023 IEEE.

关键词： Sensor nodes

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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The Jessen inequality of semi-E-preinvex functions and its applications 14

The Jessen inequality of semi-E-preinvex functions and its a...

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14th International Conference on intelligent Human-Machine Systems and Cybernetics, IHMSC 2022

作者： Fu, Zufeng Wang, Haiying Jia, Xiao School of Computer Science and Technology Nanyang Normal University Nanyang China2. Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image Nanyang Normal University Nanyang China

The Jensen inequality for the semi-E-preinvex functions is obtained by introducing the η-E-convex linear combination suitable for E-invex sets and semi-E-preinvex functions, and an upper bound of the error of the sem... 详细信息

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

关键词： Upper bound Man-machine systems Cybernetics

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SETAR Model research Based on Support Vector Regression Parameter Estimation Method and Empirical Analysis 3

SETAR Model Research Based on Support Vector Regression Para...

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2021 3rd International Conference on Robotics, intelligent Control and Artificial Intelligence, ICRICA 2021

作者： Li, Yun Yin, Xiangfeng Wen, Xueting School of Mathematics and Computational Science Hunan University of Science and Technology Xiangtan China Sch. of Mathematics and Computational Science Hunan University of Science and Technology Hunan Engineering Research Center for Big Data and Intelligent Decision-making of Industrial Development Xiangtan China

In this paper, we use support vector regression (SVR) to estimate the parameters of the SETAR model for predicting the log-return of stock price data. SETAR model is usually estimated using maximum likelihood (ML) and minimize conditional sum-of-squares (CSS), assuming that the data are normal distribution. SVR is a powerful tool for regression estimation. Therefore, this paper proposes to use SVR instead of maximum likelihood and conditional least squares to estimate the parameters of the SETAR model. Empirical analysis on the two stock price data of China Unicom and ICBC, the results show that on the data of China Unicom, the prediction error of the Gaussian kernel SVR-SETAR model is 8% lower than that of the ML-SETAR model and the CSS-SETAR model. And on ICBC data, the prediction error of the Gaussian kernel SVR-SETAR model is 26.32% and 20% lower than that of the ML-SETAR model and CSS-SETAR model, respectively. This suggests that SVR can be used to estimate the parameters of the SESAT model and the SVR-SETAR model has higher predictive power. © Published under licence by IOP Publishing Ltd.

关键词： Normal distribution

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Edge Learning-Based Efficient data Imputation of Water Quality 7th

Edge Learning-Based Efficient Data Imputation of Water Quali...

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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Wang, Yongsheng Chen, Zhen Liu, Limin Gao, Jing Liu, Guangwen Xu, Zhiwei School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100000 China

ISBN: (纸本)9789819927296

Water quality monitoring is critical for developing effective surface water management strategies, whereas data preprocessing is the foundation for monitoring water quality. Water quality monitoring usually suffers from abnormal and missing values in the collected data of surface water quality, and thus an efficient data imputation method for water quality data is highly desired. To handle these challenges, we propose an edge learning-based data imputation method. By using VAE as the generator of WGAN-LP, the performance of value imputation of water quality is generalized and improved. In addition, we demonstrate the performance of the proposed method on two real-world datasets, collected from Wuliangsu Lake and Taihu Lake in Inner Mongolia. Compared with state of the art, the enhanced imputation accuarcy of water quality is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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Synergistic Low-Carbon Strategies for Robust Electricity-Gas System Dispatch Under Uncertainty

SSRN

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SSRN 2025年

作者： Fu, Zezheng Yang, Degang Lin, Wenting Liu, Kangming School of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331 China

Reducing carbon emissions in energy systems remains a critical concern, giving rise to a variety of low-carbon initiatives. Current research largely focuses on the application of individual or dual low-carbon technologies (such as carbon capture, carbon trading, renewable energy, etc.), with limited exploration of the synergistic effects of integrating multiple technologies. To address this, we propose a low-carbon economic dispatch model for an electricity-gas system designed to maintain system stability under extreme conditions in renewable energy scenarios. This study integrates several low-carbon technologies—including carbon capture systems, carbon trading mechanisms, demand response strategies, power-to-gas technology, and renewable energy generation—and examines their collective impact on the system. To address uncertainties in renewable energy output, a two-stage robust optimization framework with a min-max-min structure is developed, aiming to determine optimal renewable energy scheduling under extreme scenarios. By employing a column-and-constraint generation algorithm and the KKT conditions, the problem is decomposed into a primary mixed-integer linear programming problem and a series of subproblems, which are solved iteratively to achieve the optimal solution. Simulation results verify the proposed model's effectiveness. © 2025, The Authors. All rights reserved.

关键词： Carbon capture and utilization

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PDHG-unrolled learning-to-optimize method for large-scale linear programming 24

PDHG-unrolled learning-to-optimize method for large-scale li...

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Proceedings of the 41st International Conference on Machine Learning

作者： Bingheng Li Linxin Yang Yupeng Chen Senmiao Wang Haitao Mao Qian Chen Yao Ma Akang Wang Tian Ding Jiliang Tang Ruoyu Sun Michigan State University School of Data Science The Chinese University of Hong Kong Shenzhen China and Shenzhen International Center For Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen International Center For Industrial and Applied Mathematics Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center For Industrial and Applied Mathematics Shenzhen Research Institute of Big Data and School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Rensselaer Polytechnic Institute

Solving large-scale linear programming (LP) problems is an important task in various areas such as communication networks, power systems, finance, and logistics. Recently, two distinct approaches have emerged to expedite LP solving: (i) First-order methods (FOMs); (ii) Learning to optimize (L2O). In this work, we propose a FOM-unrolled neural network (NN) called PDHG-Net, and propose a two-stage L2O method to solve large-scale LP problems. The new architecture PDHG-Net is designed by unrolling the recently emerged PDHG method into a neural network, combined with channel-expansion techniques borrowed from graph neural networks. We prove that the proposed PDHG-Net can recover PDHG algorithm, thus can approximate optimal solutions of LP instances with a polynomial number of neurons. We propose a two-stage inference approach: first use PDHG-Net to generate an approximate solution, and then apply the PDHG algorithm to further improve the solution. Experiments show that our approach can significantly accelerate LP solving, achieving up to a 3× speedup compared to FOMs for large-scale LP problems.

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stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics data Imputation

arXiv

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arXiv 2024年

作者： Li, Xiaoyu Min, Wenwen Wang, Shunfang Wang, Changmiao Xu, Taosheng School of Information Science and Engineering Yunnan University China Shenzhen Research Institute of Big Data China Hefei Institutes of Physical Science Chinese Academy of Science China

Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations. However, this high degree of spatial resolution entails a drawback, as the resulting spatial transcriptomic data at the cellular level is notably plagued by a high incidence of missing values. Furthermore, most existing imputation methods either overlook the spatial information between spots or compromise the overall gene expression data distribution. To address these challenges, our primary focus is on effectively utilizing the spatial location information within spatial transcriptomic data to impute missing values, while preserving the overall data distribution. We introduce stMCDI, a novel conditional diffusion model for spatial transcriptomics data imputation, which employs a denoising network trained using randomly masked data portions as guidance, with the unmasked data serving as conditions. Additionally, it utilizes a GNN encoder to integrate the spatial position information, thereby enhancing model performance. The results obtained from spatial transcriptomics datasets elucidate the performance of our methods relative to existing approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Gene expression

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Interference Characterization and Mitigation for Multi-Beam ISAC Systems in Vehicular Networks

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IEEE Transactions on Wireless Communications 2024年第10期23卷 14729-14742页

作者： Wang, Yi Zhang, Qixun Zhang, J. Andrew Wei, Zhiqing Feng, Zhiyong Peng, Jinlin The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Quzhou324003 China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The Global Big Data Technologies Centre University of Technology Sydney SydneyNSW2007 Australia The Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing100071 China

— Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes. © 2002-2012 IEEE.

关键词： Resource allocation

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