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Monitoring functions on global states of distributed programs

JOURNAL OF parallel AND DISTRIBUTED COMPUTING 1997年第2期41卷 173-189页

作者： Tomlinson, AI Garg, VK Parallel and Distributed Systems Laboratory Department of Electrical and Computer Engineering 4 The University of Texas at Austin Austin Texas 78712

The domain of a global function is the set of all global states of an execution of a distributed program, We show how to monitor a program in order to determine if there exists a global state in which the sum x(1) + x(2) +...+ x(N), exceeds some constant K, where x(i) is defined in process i. We examine the cases where x(i) is an integer variable for N = 2 and where x(i) is a boolean variable for general N, For both cases we provide algorithms, prove their correctness, and analyze their complexity. (C) 1997 Academic Press.

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Intelligent data-driven methods for enhancing distributed excitation control in electrical grid

Intelligent data-driven methods for enhancing distributed ex...

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International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Hafiz Ghulam Murtza Qamar Muhammad Imran Fareed Ahmad Ayesha Riaz Adil Ashraf Muhammad Zain Yousaf Imran Khan School of Electrical Engineering Yanshan University Qinhuangdao 066004 People's Republic of China Department of Electrical Electronics and Computer Systems University of Sargodha People's Republic of China Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji Zhejiang 311816 People's Republic of China

ISBN: (数字)9781837243150

This paper proposes a decentralized excitation control method based on synchronized estimation using asynchronous measurements from voltage/current transformers (VT/CT), addressing the challenges of decentralized excitation control caused by asynchronous updates. Due to the high cost and limited use of Phasor Measurement Units (PMUs), synchronized generator output measurements are often not feasible. The proposed method establishes a framework for decentralized excitation control that aligns with synchronized estimation in multi-machine power systems. It develops an estimation model for synchronized acquisition of excitation control feedback inputs, even under asynchronous sampling conditions. Simulation results using IEEE standard test systems demonstrate that synchronized estimated values offer a more accurate representation of the system’s status compared to asynchronous measurements, with better compatibility with the network structure. The findings show that decentralized excitation control based on synchronized estimation significantly enhances power system stability compared to traditional control methods that rely on asynchronous feedback.

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Amorphous boron nitride: synthesis, properties and device application

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Nano Convergence 2025年第1期12卷 1-22页

作者： Sattari-Esfahlan, Seyed Mehdi Mirzaei, Saeed Josline, Mukkath Joseph Moon, Ji-Yun Hyun, Sang-Hwa Jang, Houk Lee, Jae-Hyun Institute for Microelectronics TU Vienna 1040 Austria CEITEC BUT Brno University of Technology Purkynova 123 Brno 61200 Czech Republic Fraunhofer Institute for Material and Beam Technology WinterbergstraBe 28 Dresden E01277 Germany Department of Energy Systems Research Ajou University Suwon 16499 South Korea Department of Mechanical Engineering and Materials Science Washington University in St. Louis St. Louis 63130 MO United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 South Korea Center for Functional Nanomaterials Brookhaven National Laboratory Upton New York 11973 United States Institute for Functional Intelligent Materials National University of Singapore Singapore 117575 Singapore

Amorphous boron nitride (a-BN) exhibits remarkable electrical, optical, and chemical properties, alongside robust mechanical stability, making it a compelling material for advanced applications in nanoelectronics and photonics. This review comprehensively examines the unique characteristics of a-BN, emphasizing its electrical and optical attributes, state-of-the-art synthesis techniques, and device applications. Key advancements in low-temperature growth methods for a-BN are highlighted, offering insights into their potential for integration into scalable, CMOS-compatible platforms. Additionally, the review discusses the emerging role of a-BN as a dielectric material in electronic and photonic devices, serving as substrates, encapsulation layers, and gate insulators. Finally, perspectives on future challenges, including defect control, interface engineering, and scalability, are presented, providing a roadmap for realizing the full potential of a-BN in next-generation device technologies. © The Author(s) 2025.

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Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitudes. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic programming

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Robust Co-Optimization of Distribution Network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

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Enabling Real-time Computing and Transmission Services in Large-Scale LEO Satellite Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Binquan Zhang, Zhou Atapattu, Saman Pan, Miao Yan, Ye Xiong, Zehui Li, Hongyan Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Tianjin300457 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Hunan University College of Computer Science and Electronic Engineering Changsha410012 China University School of Engineering MelbourneVIC3000 Australia University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Tianjin Artificial Intelligence Innovation Center Tianjin300387 China

Enhanced by onboard computing resources and inter-satellite links (ISLs), large-scale satellite networks (SNs) can supplement terrestrial networks, facilitating real-time edge computing and transmission in remote areas at low latency. This capability benefits applications such as real-time remote object tracking, vehicular edge computing, and environmental monitoring. However, different from terrestrial vehicular networks, satellite mobility and the intermittent nature of ISLs often render many computing satellites inaccessible, necessitating efficient scheduling scheme for joint computing and communication resource allocation. To meet this requirement, we investigate joint computing and transmission strategy for real-time computing applications over SNs to reduce end-to-end delays. We formulate the real-time computing and transmission problem over time-varying SNs as an integer linear programming problem. The mathematical optimization method has an exponential time complexity of O(2|L⫟| . (4 |V⫟| + 4∣ L⫟|)2|L∣) in the worst case, which grows rapidly with the growth of network sizes. Here, |V⫟| and |L⫟| denote the number of nodes and links, respectively, within the time window {\tau }. Resorting to the designed k-shortest path-based method yields a worst-case complexity of O(|V⫟|! . |V⫟|3). While efficient under common scenarios, its running time is still relatively unstable and may become tens of times longer when computing resources become scarce. To enhance efficiency, we further design a graph-based algorithm that leverages the solution space's unique structure, thereby achieving optimal solutions with polynomial time complexity O(3|L⫟| + (2\log |V⫟|+1) |V⫟|). Extensive evaluations conducted on the world's largest Starlink system validate the efficacy of the designed methods and highlight their potential synergy. © 1967-2012 IEEE.

关键词： Satellite links

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Microfluidics with Machine Learning for Biophysical Characterization of Cells

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Annual review of analytical chemistry (Palo Alto, Calif.) 2025年第1期18卷 447-472页

作者： Jeon, Hyungkook Han, Jongyoon Department of Manufacturing Systems and Design Engineering (MSDE) Seoul National University of Science and Technology (SEOULTECH) Seoul Republic of Korea email: Departments of Biological Engineering and Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts USA email: Critical Analytics for Manufacturing Personalized Medicine (CAMP) Interdisciplinary Research Group Singapore-MIT Alliance for Research and Technology (SMART) Centre Singapore

Understanding the biophysical properties of cells is essential for biological research, diagnostics, and therapeutics. Microfluidics enhances biophysical cell characterization by enabling precise manipulation and real-time measurement at the microscale. However, the high-throughput nature of microfluidic systems generates vast amounts of data, complicating analysis. Integrating artificial intelligence (AI) methods, including machine learning and deep learning, with microfluidic technologies addresses these challenges. AI excels at analyzing large, complex datasets, improving the accuracy and efficiency of microfluidic experiments and facilitating new biological discoveries. This review examines the synergy between microfluidics and machine learning for biophysical cell characterization, categorizing existing methods based on the types of input data used for machine learning analysis, highlighting recent advancements, and discussing challenges and future directions in this interdisciplinary field.

关键词： artificial intelligence biophysical properties of cells cell analysis cell characterization machine learning microfluidic signature microfluidics

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DEMAND ASSIGNMENT MULTIPLE ACCESS SCHEMES IN BROADCAST BUS LOCAL AREA NETWORKS

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IEEE TRANSACTIONS ON computerS 1984年第12期33卷 1130-1159页

作者： FINE, M TOBAGI, FA Computer Systems Laboratory Department of Electrical Engineering Stanford University

Local area communications networks based on packet broadcasting techniques provide simple architectures and efficient and flexible operation. Various ring systems and CSMA contention bus systems have been in operation for several years. More recently, a number of distributed demand assignment multiple access (DAMA) schemes suitable for broadcast bus networks have emerged which provide conflict-free broadcast communications by means of various scheduling techniques. Among these schemes, the Token-Passing Bus Access method uses explicit tokens, i.e., control messages, to provide the required scheduling. Others use implicit tokens, whereby stations in the network rely on information deduced from the activity on the bus to schedule their transmissions. In this paper we present many implicit-token DAMA schemes in a unified manner grouped according to their basic access mechanisms, and compare them in terms of performance and other important attributes.

关键词： Broadcast bus networks carrier sensing local area networks multiaccess protocols packet switching performance random access token passing

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TRACKING THE TOPOLOGY OF NEURAL MANIFOLDS ACROSS POPULATIONS

arXiv

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arXiv 2025年

作者： Yoon, Iris H.R. Henselman-Petrusek, Gregory Yu, Yiyi Ghrist, Robert Smith, Spencer Lavere Giusti, Chad Department of Mathematics and Computer Science Wesleyan University 265 Church Street MiddletownCT06459 United States Pacific Northwest National Laboratory 902 Battelle Boulevard RichlandWA99352 United States Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States Department of Mathematics and Electrical & Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Mathematics Oregon State University 2000 SW Campus Way CorvallisOR97331 United States

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds. © 2025, CC BY-NC-ND.

关键词： Dimensionality reduction

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