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arXiv 2022年

作者： Siew, Marie Sharma, Shikhar Li, Zekai Guo, Kun Xu, Chao Lorido-Botran, Tania Quek, Tony Q.S. Joe-Wong, Carlee The Information Systems and Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The Electrical and Computer Engineering department Carnegie Mellon University PittsburghPA15213 United States The Shanghai Key Laboratory of Multidimensional Information Processing School of Communications and Electronics Engineering East China Normal University Shanghai200241 China The School of Information Engineering Northwest A&F University Yangling712100 China Roblox United States

In edge computing, users’ service profiles are migrated between edge servers due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE’s boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. We extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures. © 2022, CC BY.

关键词： Mobile edge computing

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An integrated discrete-event simulation with functional resonance analysis and work domain analysis methods for industry 4.0 implementation

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Decision Analytics Journal 2023年 9卷

作者： Ruiz Zúñiga, Enrique Yasue, Naruki Hirose, Takayuki Nomoto, Hideki Sawaragi, Tetsuo JSPS Research Fellow Department of Intelligent Automation University of Skövde Högskolevägen Box 408 Skövde 541 28 Sweden Systems Design Laboratory Department of Mechanical Engineering and Science Kyoto University Kyoto 615-8540 Japan Japan Manned Space Systems Corporation Otemachi Bldg. 1-6-1 Otemachi Chiyoda-ku Tokyo 100-0004 Japan

In the Industry 4.0 era, advanced analytical tools are essential for progressing with digital transformation, especially within complex socio-technical systems. However, the growing complexity of these systems in manufacturing impedes system improvement, and traditional analytical methods focusing solely on the technological aspect often fall short. To overcome this problem, this paper introduces an integrated methodology combining Discrete-Event Simulation, Functional Resonance Analysis Method, and Work Domain Analysis for analysing and enhancing manufacturing systems by considering factors like operator skill levels, demand changes, and production constraints. Implemented in two industrial case studies, this methodology effectively identifies system limitations and aids in structured data analysis, positioning it as a vital decision support system in the digital transformation of Industry 4.0. © 2023 The Authors

关键词： Discrete-event simulation Functional resonance analysis method Industry 4.0 Manufacturing Socio-technical systems Work domain analysis

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Low Power and Temperature- Resilient Compute-In-Memory Based on Subthreshold-FeFET

Low Power and Temperature- Resilient Compute-In-Memory Based...

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design, automation and Test in Europe Conference and Exhibition

作者： Yifei Zhou Xuchu Huang Jianyi Yang Kai Ni Hussam Amrouch Cheng Zhuo Xunzhao Yin Zhejiang University Hangzhou China ZJU - Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Department of Electrical Engineering University of Notre Dame Notre Dame USA Chair of AI Processor Design Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as “memory wall” issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a crossbar structure can efficiently accelerate multiply-accumulation (MAC) computation, a crucial operator in neural networks among various AI models. Low power CiM designs are thus highly desired for further energy efficiency optimization on AI models. Ferroelectric FET (FeFET), an emerging device, is attractive for building ultra-low power CiM array due to CMOS compatibility, high ION / $I$ O F F ratio, etc. Recent studies have explored FeFET based CiM designs that achieve low power consumption. Nevertheless, subthreshold-operated FeFETs, where the operating voltages are scaled down to the subthreshold region to reduce array power consumption, are particularly vulnerable to temperature drift, leading to accuracy degradation. To address this challenge, we propose a temperature-resilient 2T-1FeFET CiM design that performs MAC operations reliably at subthreahold region from 0°C to 85°C, while consuming ultra-low power. Benchmarked against the VGG neural network architecture running the CIFAR-10 dataset, the proposed 2T1FeFET CiM design achieves 89.45% CIFAR-10 test accuracy. Compared to previous FeFET based CiM designs, it exhibits immunity to temperature drift at an 8-bit wordlength scale, and achieves better energy efficiency with 2866 TOPS/W.

关键词： Semiconductor device modeling Power demand Nonvolatile memory Microprocessors Neural networks Computer architecture Energy efficiency

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Ray-Based Characterization of the AMPLE Model from 0.85 to 5 GHz

arXiv

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arXiv 2025年

作者： Zhou, Lingyou Dong, Xin Qiu, Kehai Yu, Gang Zhang, Jie Zhang, Jiliang School of Electrical and Electronic Engineering The University of Sheffield SheffieldS10 2TN United Kingdom Ranplan Wireless Network Design Ltd. CambridgeCB23 3UY United Kingdom Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom College of Engineering Design and Physical Sciences Brunel University of London UxbridgeUB8 3PH United Kingdom 5G/6G Innovation Centre University of Surrey SurreyGU2 7XH United Kingdom State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang110819 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

In this paper, we characterize the adaptive multiple path loss exponent (AMPLE) radio propagation model under urban macrocell (UMa) and urban microcell (UMi) scenarios from 0.85-5 GHz using Ranplan Professional. We first enhance the original AMPLE model by introducing an additional frequency coefficient to support path loss prediction across multiple carrier frequencies. By using measurement-validated Ranplan Professional simulator, we simulate four cities and validate the simulations for further path loss model characterization. Specifically, we extract the close-in (CI) model parameters from the simulations and compare them with parameters extracted from measurements in other works. Under the ray-based model characterization, we compare the AMPLE model with the 3rd Generation Partnership Project (3GPP) path loss model, the CI model, the alpha-beta-gamma (ABG) model, and those with simulation calibrations. In addition to standard performance metrics, we introduce the prediction-measurement difference error (PMDE) to assess overall prediction alignment with measurement, and mean simulation time per data point to evaluate model complexity. The results show that the AMPLE model outperforms existing models while maintaining similar model complexity. Copyright © 2025, The Authors. All rights reserved.

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Target-enclosing affine formation control of two-layer networked spacecraft with collision avoidance

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Chinese Journal of Aeronautics 2019年第12期32卷 2679-2693页

作者： Yang XU Delin LUO Dongyu LI Yancheng YOU Haibin DUAN School of Aerospace Engineering Xiamen UniversityXiamen 361005China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117583Singapore

This paper addresses a target-enclosing problem for multiple spacecraft systems by proposing a two-layer affine formation control strategy. Compared with the existing methods,the adopted two-layer network structure in this paper is generally directed, which is suitable for practical space missions. Firstly, distributed finite-time sliding-mode estimators and formation controllers in both layers are designed separately to improve the flexibility of the formation control system. By introducing the properties of affine transformation into formation control protocol design,the controllers can be used to track different time-varying target formation patterns. Besides, multilayer time-varying encirclements can be achieved with particular shapes to surround the moving target. In the sequel, by integrating adaptive neural networks and specialized artificial potential functions into backstepping controllers, the problems of uncertain Euler-Lagrange models, collision avoidance as well as formation reconfiguration are solved simultaneously. The stability of the proposed controllers is verified by the Lyapunov direct method. Finally, two simulation examples of triangle formation and more complex hexagon formation are presented to illustrate the feasibility of the theoretical results.

关键词： Affine formation control Collision avoidance Lyapunov stability Target enclosing Two-layer strategy

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Hierarchical Optimization of Landing Performance for Lander with Adaptive Landing Gear

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Chinese Journal of Mechanical engineering 2019年第2期32卷 24-35页

作者： Zongmao Ding Hongyu Wu Chunjie Wang Jianzhong Ding School of Mechanical Engineering and Automation Beijing University of Aeronautics and Astronautics Beijing System Design Institute of Electro-Mechanic Engineering China Aerospace Science and Industry Corporation Limited Department of Mechanical Engineering Tsinghua University State Key Laboratory of Virtual Reality and Systems Beijing University of Aeronautics and Astronautics

A parameterized dynamics analysis model of legged lander with adaptive landing gear was established. Based on the analysis model, the landing performances under various landing conditions were analyzed by the optimized Latin hypercube experimental design method. In order to improve the landing performances, a hierarchical optimization method was proposed considering the uncertainty of landing conditions. The optimization problem was divided into a higher level(hereafter the "leader") and several lower levels(hereafter the "follower"). The followers took condition?ing factors as design variables to find out the worst landing conditions, while the leader took bu er parameters as design variables to better the landing performance under worst conditions. First of all, sensitivity analysis of landing conditioning factors was carried out according to the results of experimental design. After the sensitive factors were screened out, the response surface models were established to reflect the complicated relationships between sensi?tive conditioning factors, bu er parameters and landing performance indexes. Finally, the response surface model was used for hierarchical optimization iteration to improve the computational e ciency. After selecting the optimum bu er parameters from the solution set, the dynamic model with the optimum parameters was simulated again under the same landing conditions as the simulation before. After optimization, nozzle performance against damage is improved by 5.24%, the acceleration overload is reduced by 5.74%, and the primary strut improves its performance by 21.10%.

关键词： Landing gear Soft landing Sensitivity analysis Response surfaces Hierarchical optimization

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Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors

arXiv

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arXiv 2022年

作者： Li, Zishuo Niazi, Muhammad Umar B. Liu, Changxin Mo, Yilin Johansson, Karl H. Department of Automation Tsinghua University Beijing100084 China Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. In other words, at each time, the attacker has the freedom to choose an arbitrary set of no more that p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observer for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. © 2022, CC BY-NC-ND.

关键词： Fault detection

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A decentralized power sharing strategy for wind farm de-loading

A decentralized power sharing strategy for wind farm de-load...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Fan, Xinkai Crisostomi, Emanuele Thomopulos, Dimitri Zhang, Baohui Shorten, Robert Xi'An Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment Xi'an China Territory and Constructions Engineering University of Pisa Department of Energy Systems Pisa Italy Dyson School of Design Engineering Imperial College London London United Kingdom

ISBN: (纸本)9781728155081

This paper revises current power sharing algorithms used for de-loading operations in wind farms with variable speed wind turbines. In particular, we show that classic fair power sharing solutions can be obtained without requiring wind turbines to communicate local wind or power measurements, which is a very attractive feature for practical applications. For this purpose, algorithms borrowed from communication network theory, namely, Additive-Increase-Multiplicative-Decrease (AIMD) algorithms, are used to solve the power sharing problem in a decentralized fashion. Extensive simulations on wind farms consisting of ten wind turbines are provided to illustrate the potential and the efficacy of the proposed methodology. © 2020 IEEE.

关键词： Wind turbines

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Hardware implementation of memristor-based artificial neural networks

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Nature Communications 2024年第1期15卷 1974页

作者： Aguirre, Fernando Sebastian, Abu Le Gallo, Manuel Song, Wenhao Wang, Tong Yang, J. Joshua Lu, Wei Chang, Meng-Fan Ielmini, Daniele Yang, Yuchao Mehonic, Adnan Kenyon, Anthony Villena, Marco A. Roldán, Juan B. Wu, Yuting Hsu, Hung-Hsi Raghavan, Nagarajan Suñé, Jordi Miranda, Enrique Eltawil, Ahmed Setti, Gianluca Smagulova, Kamilya Salama, Khaled N. Krestinskaya, Olga Yan, Xiaobing Ang, Kah-Wee Jain, Samarth Li, Sifan Alharbi, Osamah Pazos, Sebastian Lanza, Mario Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Departament d’Enginyeria Electrònica Universitat Autònoma de Barcelona (UAB) Barcelona 08193 Spain IBM Research – Zurich Rüschlikon Switzerland Department of Electrical and Computer Engineering University of Southern California (USC) Los Angeles 90089 CA United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor 48109 MI United States Department of Electrical Engineering National Tsing Hua University Hsinchu 30013 Taiwan Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano and IUNET Piazza L. da Vinci 32 Milano 20133 Italy School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Electrical Engineering University College London (UCL) Torrington Place WC1E 7JE London United Kingdom Departamento de Electrónica y Tecnología de Computadores Facultad de Ciencias Universidad de Granada Avenida Fuentenueva s/n Granada 18071 Spain Engineering Product Development (EPD) Pillar Singapore University of Technology & amp Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province Hebei University Baoding 071002 China Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore (NUS) Singapore Singapore

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach. © The Author(s) 2024.

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Curvature-Constrained Vector Field for Motion Planning of Nonholonomic Robots

arXiv

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arXiv 2025年

作者： Qiao, Yike He, Xiaodong Zhuo, An Sun, Zhiyong Bao, Weimin Li, Zhongkui State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China China Aerospace Science and Technology Corporation Beijing100048 China

Vector fields are advantageous in handling nonholonomic motion planning as they provide reference orientation for robots. However, additionally incorporating curvature constraints becomes challenging, due to the interconnection between the design of the curvature-bounded vector field and the tracking controller under underactuation. In this paper, we present a novel framework to co-develop the vector field and the control laws, guiding the nonholonomic robot to the target configuration with curvature-bounded trajectory. First, we formulate the problem by introducing the target positive limit set, which allows the robot to converge to or pass through the target configuration, depending on different dynamics and tasks. Next, we construct a curvature-constrained vector field (CVF) via blending and distributing basic flow fields in workspace and propose the saturated control laws with a dynamic gain, under which the tracking error’s magnitude decreases even when saturation occurs. Under the control laws, kinematically constrained nonholonomic robots are guaranteed to track the reference CVF and converge to the target positive limit set with bounded trajectory curvature. Numerical simulations show that the proposed CVF method outperforms other vector-field-based algorithms. Experiments on Ackermann UGVs and semi-physical fixed-wing UAVs demonstrate that the method can be effectively implemented in real-world scenarios. 1 © 2025, CC BY.

关键词： Invariance

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