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Joint Precoding and Fronthaul Compression for Cell-Free MIMO With Hybrid Topology

IEEE Internet of Things Journal 2025年第11期12卷 18125-18136页

作者： Chen, Yijie Xia, Wenchao Zhang, Jun Hou, Xiaoyun Wong, Kai-Kit Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Nanjing University of Posts and Telecommunications Institute of Signal Processing and Transmission Nanjing210003 China University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom Yonsei University Seoul03722 Korea Republic of

Cell-free multiple-input-multiple-output generally uses a star topology for superior communication but faces high costs due to long cables. An economical alternative, the stripe topology, is suitable for specific deployments but cannot meet user demands in densely populated areas due to limited fronthaul capacity. To address these limitations, we propose a hybrid network structure combining stripe and star topologies, ensuring system performance while reducing deployment costs. In such a network, joint precoding and fronthaul compression is considered to maximize system sum-rate and an alternating optimization (AO) algorithm is proposed. However, the AO algorithm involves an iterative process and complex matrix calculations, making it unsuitable for practical applications. To deal with this issue, we propose a low-complexity iterative gradient descent (IGD) algorithm with simple matrix operations. To further reduce online computational complexity, we propose a novel deep unfolding neural network (DUNN) scheme, which is interpretable and scalable, based on the IGD algorithm. Simulation results show that the hybrid topology significantly improves system capacity compared to the stripe-only topology. Additionally, the DUNN achieves a tradeoff between the achievable sum-rate performance and the corresponding computational complexity. © 2014 IEEE.

关键词： Iterative methods

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Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 4

Event-Triggered Online Learning Distributionally Robust Ener...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

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Research on Medical Image Classification for Oral Cancer 3rd

Research on Medical Image Classification for Oral Cancer

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, Kunkun Shi, Linjun Gu, Chaochen Mo, Fan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Oral Mucosal Diseases Ninth People’s Hospital Shanghai Jiaotong University School of Medicine Shanghai China Shanghai Key Laboratory of Stomatology and Shanghai Research Institution of Stomotology National Clinical Research Center of Stomatology Shanghai China

ISBN: (纸本)9789819624676

In recent years, deep learning has made significant progress in medical imaging, deepening the crossover between the medical and industrial fields. However, not all medical images are suitable for deep learning neural networks;simultaneously, some medical images are difficult to collect and preprocess. In this paper, we reconstruct the image information collected by dental clinicians over many years, normalizing it with diagnostic results as the gold standard. On this basis, we explore the applicability and potential of deep learning on complex medical images. Further, we compare the performance of multiple models on this dataset and visualize the learning outcomes, demonstrating that deep learning networks exhibit good learning capabilities on certain images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Timeslot Clustering-Based Hybrid Traffic Scheduling in Time-Sensitive Networking

A Timeslot Clustering-Based Hybrid Traffic Scheduling in Tim...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Zhihao Yang Shouliang Wang Qimin Xu Xin Li Cailian Chen Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The development of Industrial Internet of Things necessitates deterministic transmission of hybrid traffic with varying real-time requirements. Time-Sensitive Networking offers schemes combining scheduling mechanisms like the Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). Current methods utilize the combination of TAS&CQF to schedule Time-Triggered (TT) and Audio-Video Bridging (AVB) traffic under corresponding mechanisms, respectively. However, the prior TT traffic orchestration restricts the scheduling solution space of AVB traffic, decreasing the effectiveness of combination and finally resulting in low schedulability. In this paper, we focus on enhancing the schedulability of TT&AVB scheduling by integrating TAS and Cyclic Specified Queuing and Forwarding (CSQF), which brings larger scheduling solution space to AVB traffic. The integration of TAS&CSQF is proposed by analyzing the impact of TAS on CSQF to expand the allocatable resource for AVB traffic. Then TAS&CSQF scheduling model is formulated to ensure deterministic requirements. Within this model, Timeslot Clustering Optimization (TCO) is proposed to optimize the scheduling solution space of AVB traffic and slice timeslot by refining the scheduling of TAS. Based on TCO, Flow Congestion Metric (FCM) is designed to improve schedulability by the congestion level of each link. An FCM-based algorithm is further presented to generate TT&AVB solutions incrementally. Simulation results demonstrate that our method reduces scheduling time cost by over 168 times and improves schedulability by 32% for 3000 flows compared to existing methods.

关键词： Measurement Schedules Job shop scheduling Simulation Refining Real-time systems Internet Informatics Optimization Industrial Internet of Things

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Research on High-Precision Contouring control of Gantry Dual-Drive H-Type Planar Motion Platform

Research on High-Precision Contouring Control of Gantry Dual...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Chao Chen Haoyu Li Chaochen Gu Department of Automation Ministry of Education of China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The H-type two-axis linear plane motion platform with dual gantry drive is one of the most crucial components in contour processing equipment. The dual gantry structure employs independently controlled dual-side motors to collectively drive the motion of the gantry crossbeam axis. While the introduction of dual gantry drive brings higher single-axis positioning accuracy and greater operational speed to contour processing *** these issues, this paper starts with the dynamic modeling of the dual gantry H-type plane motion platform. It modifies and extends the task coordinates within the framework of the Global Task Coordinate Frame (GTCF). Subsequently, it effectively addresses the coordination between axes and the synchronization of dual drive in the task space, obtaining both motion coordination and dual-axis synchronization capabilities, leading to more accurate contour error calculation and evaluation. The paper includes a comprehensive design of experiments to assess the dual gantry synchronization performance in contour control. The proposed control method is compared with the traditional GTCF-based contour control method, demonstrating the effectiveness and superiority of the integrated coordination and synchronization contour control method.

关键词： Accuracy Uncertainty Tracking Robot kinematics Dynamics Aerospace electronics Motors

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Leader-Follower Consensus on Matrix-Weighted Switching Networks

Leader-Follower Consensus on Matrix-Weighted Switching Netwo...

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International Conference on control and Automation (ICCA)

作者： Kai Wang Lulu Pan Haibin Shao Dewei Li Yuanlong Li Shaoying He Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper examines the consensus problem on leader-follower matrix-weighted undirected switching networks. First, we introduce the leader-follower matrix-weighted integral network for analyzing such a category of networks. The connection between the null space of matrix-valued Laplacian associated with the integral network and that associated with the series of switching matrix-weighted networks has been established. Under some mild assumptions, sufficient conditions for leader-follower consensus under matrix-weighted periodic switching networks are provided. Simulation results are presented to demonstrate the theoretical results.

关键词： Sufficient conditions Laplace equations Automation Simulation Null space Switches

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Prioritize Team Actions: Multi-Agent Temporal Logic Task Planning with Ordering Constraints

Prioritize Team Actions: Multi-Agent Temporal Logic Task Pla...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Bowen Ye Jianing Zhao Shaoyuan Li Xiang Yin Department of Automation Shanghai Jiao Tong University and the Key Laboratory of System Control and Information Processing the Ministry of Education of China Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we investigate the problem of linear temporal logic (LTL) path planning for multi-agent systems, introducing the new concept of ordering constraints. Specifically, we consider a generic objective function that is defined for the path of each individual agent. The primary objective is to find a global plan for the team of agents, ensuring they collectively meet the specified LTL requirements. Simultaneously, we aim to maintain a pre-determined order in the values of the objective function for each agent, which we refer to as the ordering constraints. This new requirement stems from scenarios like security-aware planning, where relative orders outweigh absolute values in importance. We present an efficient algorithm to solve this problem, supported by proofs of correctness that demonstrate the optimality of our solution. Additionally, we provide a case study in security-aware path planning to illustrate the practicality and effectiveness of our proposed approach.

关键词： Uncertainty Computer aided software engineering Automation Information security Linear programming Path planning Planning Logic Multi-agent systems

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OIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical systems

OIDM: An Observability-based Intelligent Distributed Edge Se...

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IEEE Conference on Decision and control

作者： Shigeng Wang Tiankai Jin Yehan Ma Cailian Chen Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system’s ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness.

关键词： Job shop scheduling Accuracy Processor scheduling Linear approximation Cyber-physical systems Aerospace electronics Probabilistic logic Sensor systems Observability Intelligent sensors

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Robust and Real-Time Road Edge Detection with Flexible LiDAR Configuration

Robust and Real-Time Road Edge Detection with Flexible LiDAR...

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Chinese control and Decision Conference, CCDC

作者： Jia-Chen Li Jun-Guo Lu Ming Wei Hong-Yi Kang Qing-Hao Zhang Key Laboratory of System Control and Information Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Road edge detection is an important component in autonomous driving for along-side scenarios. The road edge is defined as a boundary line where there is a height change from the road surface. However, the traditional LiDAR-based methods are not sensitive enough for small height difference. In addition, the point cloud needs to be provided in a channel-based organization, which means that at most one LiDAR’s data can be used for edge detection. In this paper, a novel road edge detection method is proposed to utilize flexible LiDAR configuration, e.g., multiple LiDARs. Specifically, the raw point cloud is first sorted in polar coordinates, and coarse edge points are generated according to height gradient for accurate ground segmentation. The non-ground points are further used to determine the road types based on occupancy map and beam models. The final edge points in the region of interest are extracted by dynamic slide window. Our method is evaluated on unmanned ground vehicle equipped with two Robosense LiDARs, and extensive experiments are conducted to demonstrate the effectiveness of our method.

关键词： Point cloud compression Laser radar Accuracy Roads Image edge detection Organizations Real-time systems

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wen, Xiaojing Chen, Cailian Ren, Cheng Lyu, Ling Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China Dalian Maritime University School of Information Science and Technology Dalian116026 China

ISBN: (纸本)9798350345384

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance. © 2023 IEEE.

关键词： Resource allocation

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