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IEEE Conference on Decision and Control

作者： Mark Jeeninga Juan E. Machado Michele Cucuzzella Giacomo Como Jacquelien Scherpen Department of Automatic Control Lund University Lund Sweden Chair of Control Systems and Network Control Technology Faculty 3 Mechanical Engineering Electrical and Energy Systems Brandenburg University of Technology Cottbus - Senftenberg Cottbus Germany Department of Electrical Computer Science and Biomedical Engineering Univeristy of Pavia Pavia Italy Department of Mathematical Science “G.L. Lagrange” Politecnico di Torino Torino Italy Department of Automatic Control Lund University Faculty of Science and Engineering Jan C. Willems Center for Systems and Control ENTEG University of Groningen Groningen the Netherlands

In this paper, using tools from graph theory we provide verifiable necessary and sufficient conditions for the existence of a unique hydraulic equilibrium in district heating systems of meshed topology and containing multiple heat sources. Even though numerous publications have addressed the design of efficient algorithms for numerically finding hydraulic equilibria in the general context of water distribution networks, this is not the case for the analysis of existence and uniqueness. Moreover, most of the existing work dealing with these aspects exploit the equivalence between the nonlinear algebraic equations describing the hydraulic equilibria and the KKT conditions of a suitably defined nonlinear convex optimization problem. Differently, this paper proposes necessary and sufficient graph-theoretic conditions on the actuator placement for the existence and uniqueness of a hydraulic equilibrium, independent of the actuators' control objective. An example based on a representative district heating network is considered to illustrate the key aspects of our contribution, and an explicit formulation of the steady state solution is given for the case in which pressure drops through pipes are linear with respect to the flow rate.

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STAR-RIS Assisted MISO-NOMA networks: A Simultaneous Signal Enhancement and Interference Mitigation Design

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IEEE Transactions on Communications 2024年

作者： Li, Jie Song, Zhengyu Hou, Tianwei Huang, Chongwen Li, Anna Zhou, Gui Liu, Yuanwei China Mobile Group Design Institute Company Ltd China Mobile Research and Development Center for Network Planning and Optimization Beijing100080 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Lancaster University School of Computing and Communications Lancaster United Kingdom Katholieke Universiteit Leuven KU Leuven Leuven Belgium Institute for Digital Communications Erlangen91054 Germany The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong

Simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) technique has recently received considerable attention due to its omni-directional radiation capability. In this paper, motivated by the interference-mitigation-based (IMB) and signal-enhancement-based (SEB) designs, we introduce an innovative STAR-RIS assisted simultaneous-signal-enhancement-and-interference-mitigation (SSEIM) design in non-orthogonal multiple access (NOMA) multiple-input single-output cellular communication networks. Our objective is to maximize the system spectral efficiency (SE) by jointly optimizing the reflection and transmission phase shifts at the STAR-RIS, the precoding matrix of BSs, and the power allocation factors of NOMA users. We propose a low-complexity simultaneous enhancement and mitigation algorithm. Furthermore, by exploiting the manifold optimization technique, we introduce the Riemannian conjugate gradient algorithm to solve the non-convex subproblems with unit modulus constraint. Our analysis reveals that the proposed SSEIM design exceeds the traditional RIS-aided SEB and IMB designs. © 1972-2012 IEEE.

关键词： Mobile telecommunication systems

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Implementation of Industrial Automation Sustainable Training in Savannakhet University, Laos

Implementation of Industrial Automation Sustainable Training...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Tsvetelina Georgieva Nina Bencheva Plamen Daskalov Hamed Yahoui Sitha Khemmarath Khonesavanh Norasane Lamnguen Chanthanouvong Youlananh Leaunglith Vinai Keovorabouth Automation and Mechatronics Department University of Ruse “Angel Kanchev” Ruse Bulgaria Telecommunications Department University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Mechanical Engineering Automation &Processing Claude Bernard University Lyon 1 Lyon France Faculty of Engineering Savannakhet University Savannakhet Laos Technology computer network Dept. Faculty of Information Technology Savannakhet University Savannakhet Laos Faculty of Information Technology Savannakhet University Savannakhet Laos

The paper presents part of the work fulfilled under the Asean Factori 4.0 Erasmus+ project focused on the implementation of industrial automation in the education in 6 universities from 3 countries in South-East Asia: Thailand, Laos and Cambodia. The implementation of the project tasks, related to increasing the capacity and more precisely to the training of the professors from the Asian Universities is presented. According to the planned activities, professors from the University of Ruse conduct training for teachers from two universities: Chulalongkorn University, Thailand and Savannakhet University, Laos. The phases of training included in the project activities and how many of them have already been implemented are briefly presented. The program and the tasks of the teachers practical training for building the necessary teaching capacity in Savannakhet University and implementation of sustainable training in the field of industrial automation were presented and analyzed. Conclusions and future training of the staff of Asian universities planned under the project are presented in the paper.

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

arXiv

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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An Online Instant Assessment System Based-on Constructivism for Classroom Teaching in Higher Education 2

An Online Instant Assessment System Based-on Constructivism ...

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2nd International Conference on Information Science and Education, ICISE-IE 2021

作者： Liang, Ronghua Tang, Lei Li, Ye Qi, Yuansheng Zhang, Yongbin Zhang, Hao Zhang, Yixiao Department of Network and Information Beijing Institute of Graphic Communication Beijing China School of New Media Beijing Institute of Graphic Communication Beijing China Department of Mechanical and Electric Beijing Institute of Graphic Communication Beijing China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781665438292

Assessing students' classroom learning outcomes from each class is usually complicated (if not impossible) with traditional methods in higher education. A key reason is that most instructors cannot find an effective and efficient tool to evaluate students' classroom learning outcomes. Therefore, we presented an online assessment system for students' classroom learning outcomes based on social constructivism learning theory. The system consists of five modules, including a library of questions, the task design, the task management, the results management, and the user management. Instructors can easily set up an online test with this system. And students can submit their answers on their mobile phones. We used this system experimentally in classroom teaching in higher education. The results show that students participated in the interaction actively. More students answered questions on the mobile phones with this system, while fewer students raised hands to answer questions. The system helps both the teacher and students better to understand learning objectives and learning outcomes than the traditional question-answer method. The teacher could timely perceive students' learning outcomes through the submissions, which guides successive strategies for teaching and learning. Therefore, instructors can employ this system in their classrooms to improve the quality of learning in higher education. © 2021 IEEE

关键词： Students

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Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification

arXiv

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arXiv 2022年

作者： Shen, Guanxiong Zhang, Junqing Marshall, Alan Woods, Roger Cavallaro, Joseph Chen, Liquan The Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom The School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast BelfastBT9 5AG United Kingdom The Department of Electrical and Computer Engineering Rice University Houston United States The School of Cyber Science and Engineering Southeast University Nanjing210096 China The Purple Mountain Laboratories for Network and Communication Security Nanjing211111 China

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. RFFI is implemented in the wireless receiver and acts to extract the transmitter impairments and then perform classification. The receiver hardware impairments will actually interfere with the feature extraction process, but its effect and mitigation have not been comprehensively studied. In this paper, we propose a receiver-agnostic RFFI system that is not sensitive to the changes in receiver characteristics;it is implemented by employing adversarial training to learn the receiver-independent features. Moreover, when there are multiple receivers, this functionality can perform collaborative inference to enhance classification accuracy. Finally, we show how it is possible to leverage fine-tuning for further improvement with fewer collected signals. To validate the approach, we have conducted extensive experimental evaluation by applying the approach to a LoRaWAN case study involving ten LoRa devices and 20 software-defined radio (SDR) receivers. The results show that receiver-agnostic training enables the trained neural network to become robust to changes in receiver characteristics. The collaborative inference improves classification accuracy by up to 20% beyond a single-receiver RFFI system and fine-tuning can bring a 40% improvement for under-performing receivers. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning

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IEEE Transactions on network Science and engineering 2025年第4期12卷 3244-3258页

作者： Chen, Xianfu Zhao, Zhifeng Mao, Shiwen Wu, Celimuge Zhang, Honggang Bennis, Mehdi Shenzhen CyberAray Network Technology Co. Ltd. Shenzhen518100 China Zhejiang University Zhejiang Lab Hangzhou310027 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States The University of Electro-Communications Meta-Networking Research Center Chofu-shi Tokyo182-8585 Japan The University of Electro-Communications Graduate School of Informatics and Engineering Chofu-shi Tokyo182-8585 Japan City University of Macau Faculty of Data Science Taipa999078 China University of Oulu Centre for Wireless Communications Oulu90570 Finland

The age of information metric fails to correctly describe the intrinsic semantics of a status update. In an intelligent reflecting surface-aided cooperative relay communication system, we propose the age of semantics (AoS) for measuring the semantics freshness of status updates. Specifically, we focus on status updates from a source node (SN) to the destination, which is formulated as a Markov decision process. The objective of the SN is to maximize the expected satisfaction of AoS and energy consumption under the maximum transmit power constraint. To seek the optimal control policy, we first derive an online deep actor-critic (DAC) learning scheme under the on-policy temporal difference learning framework. However, implementing the online DAC in practice poses key challenge in infinitely repeated interactions between the SN and the system, which can be dangerous particularly during exploration. We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system. Numerical experiments verify the theoretical results and show that our offline DAC scheme significantly outperforms the online DAC scheme and the most representative baselines in terms of mean utility, demonstrating strong robustness to dataset quality. © 2013 IEEE.

关键词： Deep reinforcement learning

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ASDIA: An Adversarial Sample to Preserve Privacy Program in Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Tian, Shujuan Tan, Yi Wang, Han Liu, Haolin Li, Zhetao Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan China Xiangtan University Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan411105 China Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan411105 China Department of Electrical Engineering and Computer Science University of Kansas 1520 W 15th St LawrenceKS United States Jinan University College of Information Science and Technology Guangzhou510632 China

Federated learning enables training across multiple entities while ensuring data security and the effectiveness of knowledge dissemination. Despite its benefits, it remains susceptible to privacy breaches by both external and internal adversaries, who may exploit data or model parameters to glean sensitive participant information or disrupt the training process, thus compromising participant privacy and security. This paper proposes a novel methodology, Adversarial Samples for Defense Inference Attack (ASDIA), aimed at dual protection of data privacy and model robustness within federated learning through adversarial samples and gradient reconstruction. ASDIA includes gradient processing approach before uploading: initially identifying privacy-sensitive gradient, followed by the injection of well-calibrated noise to these gradients. This method not only obfuscates the adversary's classification demarcations but also aids in model performance recovery, all the while maintaining computational efficiency. ASDIA reduces the efficacy of attacks to near-random guessing levels and shows better balance between the model utility and privacy protection compared to the most advanced defense strategies. Additionally, regarding model performance, ASDIA proves its merit across diverse datasets under overfitting and non-overfitting scenarios. © 2004-2012 IEEE.

关键词： Adversarial machine learning

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DITE: Achieving Distributed Intelligent Traffic engineering With Sparse network-Wide Guidance

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IEEE Transactions on network Science and engineering 2025年

作者： Guo, Yingya Zhou, Weihong Hu, Cheng Lin, Bin Deng, Yuhui Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou 350025 China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350025 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou 350025 China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou 510515 China Jinan University Department of Computer Science Guangzhou 510632 China University of Exeter Department of Computer Science College of Engineering Mathematics and Physical Sciences Exeter EX4 4PY United Kingdom

Distributed Traffic engineering (TE) adopts decentralized routing and offers natural merits over centralized TE in rapidly responding to dynamic network flows. However, based solely on local network observations, traditional distributed TE methods often lack sufficient routing evidence necessary for generating optimal policies. Furthermore, reinforcement learning, commonly used in distributed TE methods to avoid the need for labeled data, faces challenges in accumulating valuable routing experiences due to its trial-and-error exploration. To handle these challenges, we propose a novel Distributed Intelligent Traffic engineering (DITE) framework that empowers geographically distributed agents to rapidly and adaptively optimize routing policies for dynamic network flows with low communication overhead. Specifically, to address the limitation of optimizing local routing based solely on partial network observations, we propose enriching the routing evidence for each agent with sparse network-wide information. To promote agents to learn cooperative routing polices efficiently, we explore a new paradigm for training routing agents by seamlessly integrating imitation learning with reinforcement learning. Additionally, to efficiently describe the implicit spatiotemporal features among the routing evidence, we customize a transformer-based agent to utilize the attention mechanism for establishing the relationships between agent states and routing policies. Extensive comparative evaluations on three real network topologies and traffic traces demonstrate the superior performance of DITE in achieving distributed TE. Compared to the existing distributed TE approaches, DITE achieves a notable reduction of 35.95%–42.35% in maximum link utilization and a reduction of 23.62%-68.39% in end-to-end latency. Moreover, the experimental results indicate DITE is robust to network failures and traffic changes. © 2013 IEEE.

关键词： Distributed traffic engineering imitation learning network-wide guidance reinforcement learning transformer

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Advancing Personalized Medicine: A Scalable LLM-based Recommender System for Patient Matching

Advancing Personalized Medicine: A Scalable LLM-based Recomm...

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IEEE International Conference on Big Data

作者： Armin Berger David Berghaus Ali Hamza Bashir Lorenz Grigull Lara Fendrich Tom Anglim Lagones Henriette Högl Gundula Ernst Ralf Schmidt David Bascom Tobias Deußer Thiago Bell Max Lübbering Rafet Sifa Department of Media Engineering Fraunhofer IAIS Germany Department of Computer Science University of Bonn Germany West-AI Federal Ministry of Education and Research Germany Lamarr Institute Germany Center for Rare Diseases University Hospital Bonn Germany Department of Health Queensland Australia School of Medicine Griffith University Australia Children’s Network for Chronic Illnesses and Disabilities Germany Department of Medical Psychology Medical School Hannover Germany

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study explores efficient algorithms to enhance user matching in ***, a novel social networking platform designed to connect individuals affected by rare diseases. Our primary objective is to develop a recommender system that identifies and suggests users with similar medical conditions, facilitating meaningful connections within these unique communities. Utilizing textual user profile data, we train sentence embedder models to generate similar embeddings for users that have rated each other high. We investigate various fine-tuning strategies, as well as a hybrid approach between a dense embedder and sparse SPLADE embeddings. Furthermore, we investigate the efficacy of various clustering algorithms, such as TopicBERT for thematic analysis, K-Means for centroid-based grouping, and Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, to reduce the matching complexity and enable better scalability of the platform.

关键词： Measurement Sparse approximation Social networking (online) Biological system modeling Computational modeling Clustering algorithms Performance gain Data models Recommender systems Diseases

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