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arXiv 2024年

作者： Su, Huai-An Geng, Jiaxiang Li, Liang Qin, Xiaoqi Hou, Yanzhao Wang, Hao Fu, Xin Pan, Miao Department of Electrical and Computer Engineering University of Houston United States School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Frontier Research Center Peng Cheng Laboratory China Department of Electrical and Computer Engineering Stevens Institute of Technology United States

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices’ computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices’ communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Portable equipment

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Optimization of Crane Scheduling Time for Steel Plate Outbound Operations Based on Backtracking Algorithm

Optimization of Crane Scheduling Time for Steel Plate Outbou...

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2024 China Automation Congress, CAC 2024

作者： Xiang, Jiang Xu, Zhezhuang Wang, Jinlong Chen, Boyu Zhang, Qingdong Wu, Xiuren Xia, Yuxiong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming365000 China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou350000 China

ISBN: (纸本)9798350368604

Steel plate is a highly customized product, and steel plates from the same order may be stored in different stacks. However, during the outbound process, the steel plates of the next order can only be dispatched from the warehouse once all plates from the preceding order have been fully completed. In this case, the outbound scheduling of steel plates is a challenging problem since its efficiency and complexity are affected by the shuffling of steel plates. In order to address the challenge, this paper formulates the steel plate outbound scheduling as a dynamic programming model with the aim of minimizing the crane operation time. To solve the problem, an decision tree-based backtracking search algorithm (DTBSA) is developed, in which the search tree is pruned through lower bound functions and dominance rules, so as to reduce the search range of the solution space. Experimental results prove that the proposed method can obtain optimal scheduling results within a reasonable time range. © 2024 IEEE.

关键词： Dynamic programming

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Adaptive Ant Selection for Pheromone Update in Ant Colony Optimization

Adaptive Ant Selection for Pheromone Update in Ant Colony Op...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Biao Duan, Dan-Ting Yang, Qiang Zhao, Xiao-Yan Li, Tao Liu, Dong Zhang, Jun College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China Communication University of China Key Laboratory of Media Audio & Video Ministry of Education Beijing China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China College of Artificial Intelligence Nankai University Tianjin China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9781665410205

Ant selection for updating the pheromone is one most crucial operation in ant colony optimization (ACO). In this direction, this paper designs an adaptive ant selection strategy (AAS) to adaptively and dynamically select ants to update the pheromone for ACO. Therefore, a new ACO, called AAS-ACO is developed. Specifically, AAS-ACO first assigns a non-linear selection probability to each ant based on its path ranking. As a result, better ants preserve exponentially higher selection probabilities. Then, based on the selection probabilities, ants are adaptively selected for the pheromone update. By this means, on the one hand, the number of ants involved in the pheromone update is uncertain;on the other hand, relatively better ants instead of absolutely better ones are adaptively selected to update the pheromone, leading to the promotion of search diversity. Subsequently, a dynamic weighting strategy is designed to adjust the amount of the pheromone deposited by the best ant in the current iteration to enhance the search convergence. With the two schemes, AAS-ACO is expected to maintain a suitable compromise between search diversity and search convergence to seek the optimal solutions to TSP. Experiments on 10 classical TSP instances varying from 100 to 1000 cities have proven the significant superiority of AAS-ACO to 5 classic ACOs, especially on high-dimensional TSP problems. © 2024 IEEE.

关键词： Ant colony optimization

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Pointer Meter Reading Under Noise Based on Virtual Data Generation

Pointer Meter Reading Under Noise Based on Virtual Data Gene...

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Chinese Automation Congress (CAC)

作者： Jinyang Zhu Zhezhuang Xu Ye Lin College of Electrical Engineering and Automation Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Using machine vision for meter reading significantly enhances the efficiency of industrial monitoring. However, meters in outdoor environment are often subject to the noise such as rain and fog, which affect the accuracy of meter readings. Additionally, the meter images and labels under noise are hard to obtain. To address these challenges, We propose a method for pointer meter reading under noise based on virtual data generation. Firstly, We utilize Unity to construct an industrial meter scene, which generates the images of meter under noise and theri labels. Secondly, we develop a U-Net network with an ASPP module, which expands the receptive field of network and enhances the ability of segmentation network to capture global semantic information under noise. Finally, the angular method is used to calculate the meter readings. Experimental results show that this method can effectively read meters under noise in outdoor environments.

关键词： Meters Image segmentation Rain Accuracy Noise Semantics Object segmentation Data collection Meter reading Monitoring

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Modeling Statistical Delay, Error-Rate, and Joint-Delay/Error-Rate QoS-Exponents Over M-MIMO Mobile Wireless Networks Using FBC

Modeling Statistical Delay, Error-Rate, and Joint-Delay/Erro...

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IEEE International Symposium on information Theory

作者： Xi Zhang Jingqing Wang H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing massive multiple-input and multiple-output (m-MIMO) mobile wireless network architectures for massive ultra-reliable and low-latency communications (mURLLC). One of the major design issues raised by mURLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying m-MIMO based wireless fading channels. To efficiently accommodate statistical QoS for mURLLC traffic, it is crucial to model and investigate m-MIMO based wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, etc., in the finite blocklength regime. However, how to rigorously and efficiently characterize the stochastic dynamics of m-MIMO mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither fundamentally understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling techniques and frameworks for statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish system models using FBC. Second, we develop a set of new statistical delay and error-rate bounded QoS metrics including delay, error-rate, and joint-delay/error-rate QoS-exponents, and the corresponding ϵ-effective capacities. Finally, our simulations validate and evaluate our developed modeling schemes for statistical QoS to support 6G mURLLC.

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The ε-Effective Capacity for Statistical Delay and Error-Rate Bounded QoS Provisioning Over 6G CF M-MIMO Wireless Networks Using HARQ-IR

The ε-Effective Capacity for Statistical Delay and Error-Ra...

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Annual Conference on information Sciences and Systems (CISS)

Significant effort has been devoted to the problem of guaranteeing stringent ultra-reliable and low-latency communications (URLLC) while introducing new requirements for better quality-of-services (QoS) over next generation wireless networks. One of the major design issues raised by URLLC is how to support explosively growing demands for delay-sensitive multimedia applications while guaranteeing ultra-reliability. Towards this end, there have been a wide spectrum of promising techniques, including statistical delay and error rate-bounded QoS provisioning, cell-free (CF) massive multiple-input-multiple-output (m-MIMO), finite blocklength coding (FBC), hybrid automatic repeat request with incremental redundancy (HARQ-IR) protocol, etc. However, when being integrated with FBC, how to rigorously and efficiently characterize the dynamics of mobile wireless networks in terms of statistical delay and error-rate bounded QoS provisioning for CF m-MIMO has imposed many new challenges not encountered before. To overcome these challenges, in this paper we develop statistical delay and error-rate bounded QoS analytical modeling schemes to characterize the FBC-based $\epsilon$ -effective capacity over 6G CF m-MIMO wireless networks using HARQ-IR. In particular, first we establish CF m-MIMO based system architecture models. Second, we apply a HARQ-IR protocol for deriving the channel coding rate, outage probability, and FBC based $\epsilon$ -effective capacity function using the Mellin transform. Finally, we conduct a set of simulations to validate and evaluate our proposed schemes.

关键词： 6G mobile communication Analytical models Protocols Wireless networks Systems architecture Quality of service Transforms

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Incomplete multi-view clustering via local and global co-regularization

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Science China(information Sciences) 2022年第5期65卷 96-111页

作者： Jiye LIANG Xiaolin LIU Liang BAI Fuyuan CAO Dianhui WANG Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Artificial Intelligence Research Institute China University of Mining and Technology Department of Computer Science and Computer Engineering La Trobe University

The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization(IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, ***, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.

关键词： incompleteness multi-view clustering local clustering global clustering co-regularization

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Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mURLLC Wireless Networks

Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mUR...

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Annual Conference on information Sciences and Systems (CISS)

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

The sixth generation (6G) mobile wireless networks are expected to provide massive ultra-reliable and low-latency communications (mURLLC) for data services, which require extremely stringent quality-of-services (QoS) with massive connectivity. Rate splitting (RS) multiple access schemes have been proposed recently for tackling the difficulty of the massive access yielded by a large number of mobile users (MUs) requesting to connect to a single base station. Massive multiple-input and multiple-output (massive-MIMO) communication is considered a promising solution to serve massive MUs in 6G wireless networks. However, how to apply RS schemes into massive-MIMO techniques to support the massive access has not been sufficiently studied. In this paper, we propose to integrate RS schemes with massive-MIMO techniques to achieve mURLLC transmissions under a Nakagamir $m$ fading wireless channel. First, we establish the system model for the integrated RS and massive-MIMO schemes. Then, we derive closed-form expressions for the signal-to-interference-plus-noise ratios (SINRs) under a Nakagami- $-m$ fading channel and obtain the channel capacity. Finally, we use numerical analyses to validate and evaluate our proposed RS and massive-MIMO integrated schemes.

关键词： 6G mobile communication Fading channels Closed-form solutions Channel capacity Wireless networks Interference Quality of service

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FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication

FSSC: Federated Learning of Transformer Neural Networks for ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yan, Yuna Zhang, Xin Li, Lixin Lin, Wensheng Li, Rui Cheng, Wenchi Han, Zhu Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Samsung Ai Center Cambridge United Kingdom Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients' data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm. © 2024 IEEE.

关键词： Federated learning

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AR-CNN: an attention ranking network for learning urban perception

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Science China(information Sciences) 2022年第1期65卷 164-174页

作者： Zhetao LI Ziwen CHEN Wei-Shi ZHENG Sangyoon OH Kien NGUYEN Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Data and Computer Science Sun Yat-sen University Department of Computer and Information Engineering Ajou University Graduate School of Engineering Chiba University

An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most advanced methods extract image feature representations from street-level images through conventional visual computation algorithms or deep convolutional neural networks and then directly predict the results using features. Unfortunately, these methods take color and texture information together during processing. Color and texture are prime image features, and they affect human perception and judgment differently. We argue that color and texture should be operated differently; therefore, we formulate an end-to-end learning methodology to process input images according to color and texture information before inputting it into the neural network. The processed images and the original image constitute three input streams for the triad attention ranking convolutional neural network(AR-CNN) model proposed in this *** accordance with the aspects of color and texture, an improved attention mechanism in the convolution layer is proposed. Our objective is to obtain the scores of humans on urban appearance in accordance with the prediction results computed from pairwise comparisons generated by the AR-CNN model.

关键词： ranking network urban perception attribute learning attention network colour and texture

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