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Industrial Cyber-Physical systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

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Nanoscale spin rectifiers for harvesting ambient radiofrequency energy

arXiv

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arXiv 2024年

作者： Sharma, Raghav Ngo, Tung Raimondo, Eleonora Giordano, Anna Igarashi, Junta Jinnai, Butsurin Zhao, Shishun Lei, Jiayu Guo, Yong-Xin Finocchio, Giovanni Fukami, Shunsuke Ohno, Hideo Yang, Hyunsoo Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Department of Electrical Engineering Indian Institute of Technology Ropar Rupnagar 140001 India Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina MessinaI-98166 Italy Department of Engineering University of Messina MessinaI-98166 Italy Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Aoba Sendai980-8577 Japan Tohoku University Sendai980-8577 Japan Center for Science and Innovation in Spintronics Tohoku University 2-1-1 Katahira Aoba Sendai980-8577 Japan Center for Innovative Integrated Electronic Systems Tohoku University 468-1 Aramaki Aza Aoba Sendai980-0845 Japan

Radiofrequency harvesting using ambient wireless energy could be used to reduce the carbon footprint of electronic devices. However, ambient radiofrequency energy is weak (less than -20 dBm), and the performance of state-of-the-art radiofrequency rectifiers is restricted by thermodynamic limits and high-frequency parasitic impedance. Nanoscale spin rectifiers based on magnetic tunnel junctions have recently demonstrated high sensitivity, but suffer from a low a.c.-to-d.c. conversion efficiency (less than 1%). Here, we report a sensitive spin rectifier rectenna that can harvest ambient radiofrequency signals between -62 and -20 dBm. We also develop an on-chip co-planar waveguide-based spin rectifier array with a large zero-bias sensitivity (around 34,500 mV mW-1) and high efficiency (7.81%). The self-parametric excitation driven by voltage-controlled magnetic anisotropy is a key mechanism that contributes to the performance of the spin-rectifier-array. We show that these spin rectifiers can be used to wirelessly power a sensor at a radiofrequency power of -27 dBm. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic anisotropy

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Work-in-Progress: Using the PerFECt Framework to Design and Implement Blended Learning Activities to Introduce the Binary System in Primary School Students

Work-in-Progress: Using the PerFECt Framework to Design and ...

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9th International Conference on Interactive Collaborative and Blended Learning, ICBL 2020

作者： Moumoutzis, Nektarios Rigas, Nikolaos Apostolos Xanthaki, Chara Maragkoudakis, Yiannis Christodoulakis, Christina Paneva-Marinova, Desislava Pavlova, Lilia Laboratory of Distributed Multimedia Information Systems and Applications School of Electrical and Computer Engineering Technical University of Crete Chania Crete73100 Greece Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria Directorate of Secondary Education Chania Crete Greece Department of Computer Science University of Toronto Toronto Canada Laboratory of Telematics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783030672089

This paper presents how the PerFECt framework is employed to enable innovative teaching and learning mathematics in schools using drama-based approaches. The topic addressed lies in the cross-section between mathematics and computer science: teaching and learning the binary system of arithmetic that constitutes the basis of modern computing. The work builds on previous research efforts targeting gamification of mathematics and is seen under new light offered by the Theatre in Mathematics (TIM) Methodology. The result is an engaging game to teach the binary system to children using their bodies and simple rules for interacting with each other. Following these simple rules the participants enact the operation of a calculator that is able to transform integers into their binary representations and compute the result of the four arithmetic operations (addition, subtraction, multiplication and division). The game can be combined with online activities using a simulator of the game, so that learners can explore it for themselves and discover how the arithmetic operations are done, even when they are not in the same physical space with their peers. This way, an effective blended learning approach is ultimately offered combining face-to-face and online activities. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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MARSELLUS: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasing

arXiv

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arXiv 2023年

作者： Conti, Francesco Paulin, Gianna Garofalo, Angelo Rossi, Davide Mauro, Alfio Di Rutishauser, Georg Ottavi, Gianmarco Eggimann, Manuel Okuhara, Hayate Benini, Luca University of Bologna Bologna40126 Italy The Integrated Systems Laboratory ETH Zürich Zürich8092 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore The University of Bologna Italy The University of Bologna Bologna40126 Italy ETH Zürich Zürich8092 Switzerland

Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT) System-on-a-Chips (SoCs) for augmented reality, personalized healthcare, and nano-robotics need to run many diverse tasks within a power envelope of a few tens of mW over a wide range of operating conditions: compute-intensive but strongly quantized Deep Neural Network (DNN) inference, as well as signal processing and control requiring high-precision floating-point. We present MARSELLUS, an all-digital heterogeneous SoC for AI-IoT end-nodes fabricated in GlobalFoundries 22nm FDX that combines 1) a general-purpose cluster of 16 RISC-V digital signal processing (DSP) cores attuned for the execution of a diverse range of workloads exploiting 4-bit and 2-bit arithmetic extensions (XpulpNN), combined with fused MAC&LOAD operations and floating-point support;2) a 2-8bit Reconfigurable Binary Engine (RBE) to accelerate 3×3 and 1×1 (pointwise) convolutions in DNNs;3) a set of On-Chip Monitoring (OCM) blocks connected to an Adaptive Body Biasing (ABB) generator and a hardware control loop, enabling on-the-fly adaptation of transistor threshold voltages. MARSELLUS achieves up to 180 Gop/s or 3.32 Top/s/W on 2-bit precision arithmetic in software, and up to 637 Gop/s or 12.4 Top/s/W on hardware-accelerated DNN layers. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Performance Investigation of Instantaneous Reactive Power Control for 5 Level-Neutral Point Clamped Grid-Connected Photovoltaic Inverter

Performance Investigation of Instantaneous Reactive Power Co...

引用

electrical engineering and Automatic Control (ICEEAC), International Conference on

作者： Oumaymah Elamri Alla Eddine Toubal Maamar Charaf Chermite Moulay Rachid Douiri Mostefa Kermadi Saad Mekhilef LAMIGEP multidisciplinary research laboratory École Marocaine des Sciences de l'Ingénieur (EMSI) of Marrakech Marrakech Morocco Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara of Boumerdes Boumerdes Algeria Physics Department Control and Computer Science for Intelligent Systems and Green Energy Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco School of Engineering University of Leicester Leicester United Kingdom Power Electronics and Renewable Energy Research Laboratory (PEARL) Kuala Lumpur Malaysia Swinburne University of Technology Hawthorn Vic Australia

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The utilization of solar energy in grid-connected photovoltaic installations has become increasingly significant. This article introduces a photovoltaic system seamlessly integrated with a three-phase network. The system employing a five-level Neutral Point Clamped (NPC) voltage source inverter is designed for a single-step connection to enhance power quality in the distribution network. The control technique employed for the efficient functioning of the photovoltaic system is based on the notion of instantaneous reactive power. Notably, this strategy is straightforward, operates the system throughout the day, and eliminates the need for Phase-Locked Loop (PLL) usage. The Pulse-Width Modulated (PWM) Current Controller sends switching signals to the NPC multilevel inverter, which does a great job of dynamic performance under steady-state and transient situations. The proportional-integral controller regulates the voltage of the Uninterruptible Power Supply DC bus capacitors. The proposed system undergoes comprehensive simulation validation under various irradiation levels.

关键词： Photovoltaic systems Reactive power Voltage source inverters Switches Solar energy Steady-state Transient analysis

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Topological entropy of switched nonlinear and interconnected systems

arXiv

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arXiv 2023年

作者： Yang, Guosong Liberzon, Daniel Hespanha, João P. The Department of Electrical and Computer Engineering Rutgers University PiscatawayNJ08854 United States The Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States The Center for Control Dynamical Systems and Computation University of California Santa Barbara Santa BarbaraCA93106 United States

A general upper bound for topological entropy of switched nonlinear systems is constructed, using an asymptotic average of upper limits of the matrix measures of Jacobian matrices of strongly persistent individual modes, weighted by their active rates. A general lower bound is constructed as well, using a similar weighted average of lower limits of the traces of these Jacobian matrices. In a case of interconnected structure, the general upper bound is readily applied to derive upper bounds for entropy that depend only on "network-level" information. In a case of block-diagonal structure, less conservative upper and lower bounds for entropy are constructed. In each case, upper bounds for entropy that require less information about the switching signal are also derived. The upper bounds for entropy and their relations are illustrated by numerical examples of a switched Lotka–Volterra ecosystem *** Codes 37B40, 93C30 (Primary) 93C57 (Secondary) © 2023, CC BY-NC-ND.

关键词： Jacobian matrices

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Matrix Completion over Finite Fields: Bounds and Belief Propagation Algorithms

arXiv

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arXiv 2023年

作者： Soleymani, Mahdi Liu, Qiang Mahdavifar, Hessam Balzano, Laura Halıcıoglu Data Science Institute University of California San Diego United States The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China The Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

We consider the low rank matrix completion problem over finite fields. This problem has been extensively studied in the domain of real/complex numbers, however, to the best of authors' knowledge, there exists merely one efficient algorithm to tackle the problem in the binary field, due to Saunderson et al. [1]. In this paper, we improve upon the theoretical guarantees for the algorithm provided in [1]. Furthermore, we formulate a new graphical model for the matrix completion problem over the finite field of size q, Fq, and present a message passing (MP) based approach to solve this problem. The proposed algorithm is the first one for the considered matrix completion problem over finite fields of arbitrary size. Our proposed method has a significantly lower computational complexity, reducing it from O(n2r+3) in [1] down to O(n2) (where, the underlying matrix has dimension n × n and r denotes its rank), while also improving the performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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UAV-Aided Covert Federated Learning Networks

引用

IEEE Network 2025年

作者： Wang, Chao Guo, Shaoyong Xiong, Zehui Xing, Chengwen Zhao, Nan Niyato, Dusit Karagiannidis, George Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Beijing Institute of Technology School of Information and Electronics Beijing100081 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

With the increasing emphasis on data privacy, federated learning (FL) networks show great potential through distributed training without directly sharing the raw data. However, the coverage of FL terrestrial servers is usually limited. Therefore, we propose leveraging the unmanned aerial vehicle (UAV) as a mobile flying server to further improve the wireless coverage and training efficiency of FL. Nevertheless, frequent exchanges of model parameters in UAV-assisted FL can result in serious security risks. In order to achieve this, we propose a covert FL scheme assisted by the UAV, which can protect the transmission key features of local models from being detected by wardens. Furthermore, we discuss the FL network and covert communications, as well as the exceptional features of UAV-assisted covert FL. Finally, we present two case studies, one of which focuses on minimising the network latency, while the other concentrates on reducing the energy consumption of covert FL. The simulation results demonstrate the efficacy of the proposed schemes in addressing future challenges. © 1986-2012 IEEE.

关键词： Data privacy

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

arXiv

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arXiv 2024年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J.P.C. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System The National Telemedicine Center The National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of School of Software Nanjing University of Information Science and Technology Nanjing210044 China Amazonas State University AM Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11%, increasing the final user utility function value by 0.24%, and decreasing the variance of results by 95.45%. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Hardware-Aware In Situ Learning Based on Stochastic Magnetic Tunnel Junctions

引用

Physical Review Applied 2022年第1期17卷 014016-014016页

作者： Jan Kaiser William A. Borders Kerem Y. Camsari Shunsuke Fukami Hideo Ohno Supriyo Datta Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47906 USA Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University Sendai Japan Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara California 93106 USA Center for Innovative Integrated Electronic Systems Tohoku University Sendai Japan Center for Spintronics Research Network Tohoku University Sendai Japan Center for Science and Innovation in Spintronics Tohoku University Sendai Japan WPI-Advanced Institute for Materials Research Tohoku University Sendai Japan

One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities of nanosecond operation and compatibility with existing microelectronics. Considering large-scale, viable neuromorphic systems however, variability of device properties is a serious concern. In this paper, we show an autonomously operating circuit that performs hardware-aware machine learning utilizing probabilistic neurons built with stochastic magnetic tunnel junctions. We show that in situ learning of weights and biases in a Boltzmann machine can counter device-to-device variations and learn the probability distribution of meaningful operations such as a full adder. This scalable autonomously operating learning circuit using spintronics-based neurons could be especially of interest for standalone artificial-intelligence devices capable of fast and efficient learning at the edge.

关键词： Spintronics Stochastic inference Synapses Artificial neural networks Magnetic tunnel junctions Stochastic dynamical systems Machine learning

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