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IEEE International Conference on computer-Aided Design

作者： Shengxi Shou Che-Kai Liu Sanggeon Yun Zishen Wan Kai Ni Mohsen Imani X. Sharon Hu Jianyi Yang Cheng Zhuo Xunzhao Yin College of Information Science and Electronic Engineering Zhejiang University China Department of Information and Computer Science University of California Irvine USA School of Electrical and Computer Engineering Georgia Institute of Technology USA Department of Electrical and Microelectronic Engineering Rochester Institute of Technology USA Department of Computer Science and Engineering University of Notre Dame USA School of Micro-Nano Electronics. Zhejiang University China Key Laboratory of Collaborative Sensing and Autonomous Unmaned Systems of Zhejiang Province China

Artificial intelligence has made remarkable advancements in recent years, leading to the development of algorithms and models capable of handling ever-increasing amounts of data. The computational demands of these algorithms necessitate circuit and architecture designs that go beyond the von-Neumann paradigm. Content addressable memories (CAMs), which implement parallel associative search functionality within memory blocks to overcome the memory wall bottleneck, have proven to be effective for data-intensive tasks. While current CAM designs have achieved higher storage density and energy efficiency than their CMOS-based counterparts by leveraging emerging non-volatile memories (NVM), most of these implementations are limited to binary storage cells. In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi-level-cell characteristics of FeFETs, our proposed SEE-MCAM designs enable multi-bit associative search functions and achieve better energy efficiency and performance than existing FeFET-based CAM designs. We validated the functionality of our proposed designs by achieving 3 bits per cell CAM functionality, resulting in 3x improvement in storage density. The area per bit of the proposed SEE-MCAM cell is 8% of the conventional CMOS CAM. We thoroughly investigated the scalability and robustness of the proposed design. Evaluation results suggest that the proposed 2FeFET-1 T SEE-MCAM achieves 9.8× more energy efficiency and 1.6× less search latency compared to the CMOS CAM, respectively. When compared to existing MCAM designs, the proposed SEE-MCAM can achieve 8.7× and 4.9× more energy efficiency than ReRAM-based and FeFET-based MCAMs, respectively. Benchmarking results show that our approach provides up to 3 orders of magnitude improvement in speedup and energy efficiency over a GPU implementation in accelerating a novel quantized hyperdimensiona

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Internet-of-Things technology and applications for clean energy systems

Energy Conversion and Economics

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Energy Conversion and Economics 2021年第4期2卷 183-185页

作者： Strunz, Kai Wang, Xuanyuan Guo, Qinglai Xie, Le Zhang, Song Fang, Xin Wang, Jianxiao School of Electrical Engineering and Computer Science Berlin10587 Germany State Grid Jibei Electric Power Co. LTD. Beijing China Department of Electrical Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Business Architecture and Technology ISO New England Inc. HolyokeMA United States Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO United States School of Electrical and Electronic Engineering North China Electric Power University Beijing China

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Multiple Redundant Weighted Cluster Path Scheduling Scheme for Damaged Sensor-Based Wireless Sensor Network

Multiple Redundant Weighted Cluster Path Scheduling Scheme f...

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IEEE Future Networks World Forum (FNWF)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Houbing Song Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Department of Electrical & Electronic Engineering Science University of Cyprus Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Information Systems University of Maryland Baltimore Country Baltimore MD USA

In a predefined geographical field, a group of sensor nodes communicating wirelessly form a Wireless Sensor Network (WSN). The sensors' goal is to upload the sensed data to the control station to determine if any immediate action should be taken or to analyze and monitor the situation. In clustering algorithms, the sensors are grouped as clusters, where each cluster has one sensor selected as the cluster head (CH) responsible for all inter-cluster communication. In a crisis scenario, the CH might be non-functional, resulting in a disconnected cluster. An enhanced WSN weighted cluster routing scheme is proposed in this paper. A cluster index based on distance, rewarding index, and energy is used to select the CH and cluster members (CM). The proposed scheme aims at ensuring that the data is uploaded even though the CH is non-functional by selecting a redundant CH for every node. Therefore, no matter how many sensors are inactive, a CH is still selected to ensure inter-cluster communication. The delays generated in the proposed routing scheme are studied using MATLAB simulation. In addition, the effect of different weights is studied on the delay.

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Learning to Control Linear systems can be Hard

arXiv

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arXiv 2022年

作者： Tsiamis, Anastasios Ziemann, Ingvar Morari, Manfred Matni, Nikolai Pappas, George J. Automatic Control Laboratory ETH Zurich Switzerland School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Electrical and Systems Engineering University of Pennsylvania United States

In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regret of the online learning of the Linear Quadratic Regulator (LQR). Prior results state that the statistical difficulty for both benchmarks scales polynomially with the system state dimension up to system-theoretic quantities. However, this does not reveal the whole picture. By utilizing minimax lower bounds for both benchmarks, we prove that there exist non-trivial classes of systems for which learning complexity scales dramatically, i.e. exponentially, with the system dimension. This situation arises in the case of underactuated systems, i.e. systems with fewer inputs than states. Such systems are structurally difficult to control and their system theoretic quantities can scale exponentially with the system dimension dominating learning complexity. Under some additional structural assumptions (bounding systems away from uncontrollability), we provide qualitatively matching upper bounds. We prove that learning complexity can be at most exponential with the controllability index of the system, that is the degree of underactuation. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

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Loss-induced phase transition in mid-infrared plasmonic metamaterials for ultrasensitive vibrational spectroscopy

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InfoMat 2022年第12期4卷 85-102页

作者： Hong Zhou Dongxiao Li Zhihao Ren Xiaojing Mu Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Key Laboratory of Optoelectronic Technology&Systems of Ministry of Education International R&D center of Micro-nano Systems and New Materials TechnologyChongqing UniversityChongqingPeople's Republic of China Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingaporeSingapore National University of Singapore Suzhou Research Institute(NUSRI) SuzhouPeople's Republic of China NUS Graduate School—Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingaporeSingapore

Metamaterials have proven their ability to possess extraordinary physical properties distinct from naturally available materials,leading to exciting sensing functionalities and ***,metamaterial-based sensing applications suffer from severe performance limitations due to noise interference and design ***,we propose a dual-phase strategy that leverages loss-induced different Fano-resonant phases to access both destructive and constructive signals of molecular *** the two reverse signals are innovatively combined,the noise in the detection system is effectively suppressed,thereby breaking through the noise-related ***,by utilizing loss optimization of the plasmon-molecule coupling system,our dual-phase strategy enhances the efficiency of infrared energy transfer into the molecule without any additional fabrication complex,thereby overcoming the trade-off dilemma between performance and fabrication *** to the pioneering breakthroughs in the limitations,our dual-phase strategy possesses an overwhelming competitive advantage in ultrasensitive vibrational spectroscopy over traditional metamaterial technology,including strong signal strength(×4),high sensitivity(×4.2),effective noise suppression(30%),low detection limit(13 ppm),and excellent selectivity among CO_(2),NH_(3),and CH_(4) *** work not only opens the door to various emerging ultrasensitive detection applications,including ultrasensitive in-breath diagnostics and high-information analysis of molecular information in dynamic reactions,but also gains new insights into the plasmon-molecule interactions in advanced metamaterials.

关键词： Fano resonance IR vibrational spectroscopy metamaterials phase transition ultrasensitive sensing

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Fast Mapping of Flexibility Regions at TSO-DSO Interfaces under Uncertainty

arXiv

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arXiv 2022年

作者： Patig, Alice Stanojev, Ognjen Aristidou, Petros Kiprakis, Aristides Hug, Gabriela Eeh - Power Systems Laboratory Eth Zurich Switzerland Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Cyprus Institute for Energy Systems School of Engineering University of Edinburgh United Kingdom

The gradual decommissioning of fossil fuel-driven power plants, that traditionally provide most operational flexibility in power systems, has led to more frequent grid stability issues. To compensate for the lack of flexible resources, Distributed Energy Resources (DERs) in distribution networks can be employed. To facilitate the use of DERs, the aggregated flexibility in a distribution grid is commonly represented on a PQ-plane displaying the feasible active and reactive power exchange with the upstream grid. This paper proposes a fast feasible operating region mapping mechanism that utilizes a linear power flow approximation in combination with linearized generator, current, and voltage constraints to construct a high-dimensional polyhedral feasible set of DER power injections. The obtained polytope is projected onto the PQ-plane using Fourier-Motzkin Elimination to represent the aggregate network flexibility. Additionally, uncertainty in DER generation is addressed using chance-constraints. Our analysis of a modified 33-bus IEEE test system demonstrates that the proposed method obtains more accurate approximations than former geometric methods and is ten times faster than the tested optimization-based method. Copyright © 2022, The Authors. All rights reserved.

关键词： Electric load flow

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Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO systems Under High-Mobility Scenarios

arXiv

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arXiv 2024年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China Department of Automation Tsinghua University Beijing100084 China School of Intelligent Systems Science and Engineering Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics Jinan University Zhuhai519070 China

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge- and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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Multi-Task Semantic Communications via Large Models

arXiv

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arXiv 2025年

作者： Ni, Wanli Qin, Zhijin Sun, Haofeng Tao, Xiaoming Han, Zhu Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Electrical and Computer Engineering University of Houston HoustonTX77004 United States

Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abilities to extract semantics from raw data, this integration entails multifaceted challenges including high resource demands, model complexity, and the need for adaptability across diverse modalities and tasks. To overcome these challenges, we propose a LAM-based multi-task SemCom (MTSC) architecture, which includes an adaptive model compression strategy and a federated split fine-tuning approach to facilitate the efficient deployment of LAM-based semantic models in resource-limited networks. Furthermore, a retrieval-augmented generation scheme is implemented to synthesize the most recent local and global knowledge bases to enhance the accuracy of semantic extraction and content generation, thereby improving the inference performance. Finally, simulation results demonstrate the efficacy of the proposed LAM-based MTSC architecture, highlighting the performance enhancements across various downstream tasks under varying channel conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Data integration

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Identification of Substation Configurations in Modern Power systems using Artificial Intelligence

arXiv

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arXiv 2022年

作者： Madurasinghe, Dulip Venayagamoorthy, Ganesh K. Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition. © 2022, CC BY.

关键词： Network topology

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Long Duration Battery Sizing, Siting, and Operation Under Wildfire Risk Using Progressive Hedging

arXiv

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arXiv 2024年

作者： Piansky, Ryan Stinchfield, Georgia Kody, Alyssa Molzahn, Daniel K. Watson, Jean-Paul School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Chemical Engineering Carnegie Mellon University PittsburghPA United States Energy Systems and Infrastructure Analysis Argonne National Laboratory LemontIL United States Lawrence Livermore National Laboratory LivermoreCA United States

Battery sizing and siting problems are computationally challenging due to the need to make long-term planning decisions that are cognizant of short-term operational decisions. This paper considers sizing, siting, and operating batteries in a power grid to maximize their benefits, including price arbitrage and load shed mitigation, during both normal operations and periods with high wildfire ignition risk. We formulate a multi-scenario optimization problem for long duration battery storage while considering the possibility of load shedding during Public Safety Power Shutoff (PSPS) events that de-energize lines to mitigate severe wildfire ignition risk. To enable a computationally scalable solution of this problem with many scenarios of wildfire risk and power injection variability, we develop a customized temporal decomposition method based on a progressive hedging framework. Extending traditional progressive hedging techniques, we consider coupling in both placement variables across all scenarios and state-of-charge variables at temporal boundaries. This enforces consistency across scenarios while enabling parallel computations despite both spatial and temporal coupling. The proposed decomposition facilitates efficient and scalable modeling of a full year of hourly operational decisions to inform the sizing and siting of batteries. With this decomposition, we model a year of hourly operational decisions to inform optimal battery placement for a 240-bus WECC model in under 70 minutes of wall-clock time. Copyright © 2024, The Authors. All rights reserved.

关键词： Charging (batteries)

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