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IEEE Conference on Decision and Control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control.

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

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Physical Review Applied 2024年第2期22卷 024063页

作者： Henri Tertilt Jonas Mensing Marlon Becker Wilfred G. van der Wiel Peter A. Bobbert Andreas Heuer Institute for Physical Chemistry Department of Physics NanoElectronics Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) Department of Applied Physics Center for Computational Energy Research (CCER)

Nonlinear behavior in the hopping transport of interacting charges enables reconfigurable logic in disordered dopant network devices, where voltages applied at control electrodes tune the relation between voltages applied at input electrodes and the current measured at an output electrode. From kinetic Monte Carlo simulations we analyze the critical nonlinear aspects of variable-range hopping transport for realizing Boolean logic gates in these devices on three levels. First, we quantify the occurrence of individual gates for random choices of control voltages. We find that linearly inseparable gates such as the xor gate are less likely to occur than linearly separable gates such as the and gate, despite the fact that the number of different regions in the multidimensional control voltage space for which and or xor gates occur is comparable. Second, we use principal-component analysis to characterize the distribution of the output current vectors for the (00,10,01,11) logic input combinations in terms of eigenvectors and eigenvalues of the output covariance matrix. This allows a simple and direct comparison of the behavior of different simulated devices and a comparison to experimental devices. Third, we quantify the nonlinearity in the distribution of the output current vectors necessary for realizing Boolean functionality by introducing three nonlinearity indicators. The analysis provides a physical interpretation of the effects of changing the hopping distance and temperature and is used in a comparison with data generated by a deep neural network trained on a physical device.

关键词： Carrier dynamics Dynamics of networks electrical conductivity Fitness Hopping transport Network optimization Optimization problems Temperature

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

arXiv

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arXiv 2024年

作者： Chen, Yuzong AbouElhamayed, Ahmed F. Dai, Xilai Wang, Yang Andronic, Marta Constantinides, George A. Abdelfattah, Mohamed S. Computer Systems Lab Cornell University United States Systems and Networking Research Group Microsoft Research United States Department of Electrical and Electronic Engineering Imperial College London United Kingdom

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD1, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bit-serial processing element to easily support multiple numerical precisions and data types;our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4-bit with © 2024, CC BY-NC-ND.

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Revolutionizing Road Safety: YOLOv8-Powered Driver Fatigue Detection

Revolutionizing Road Safety: YOLOv8-Powered Driver Fatigue D...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Xie, Shuaiyue Chuah, Joong Huang Chai, Grace Mei Ting Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia Smart Manufacturing Systems Research Group University of Southampton Malaysia Johor Iskandar Puteri79100 Malaysia

ISBN: (纸本)9798350341072

Driver fatigue is a significant cause of road accidents. Effective real-time fatigue detection systems are necessary to improve road safety. Utilizing a lightweight and fast model and creating an effective fatigue judgment rule is important in detecting fatigue in real-time. In this research, we propose a fatigue detection model based on You Look Only Once version 8 (YOLOv8) model to detect driver's facial features and use percentage of eyelid closure over the pupil over time (PERCLOS) and Percentage of Open Mouth (POM) indicators to identify fatigue. This research is divided into two parts. The first part involves creating, labeling, and preprocessing the dataset. We use the YawDD dataset as the original dataset and manually label a new dataset using the Roboflow platform. We then preprocessed the new dataset to train the YOLOv8 model for face features detection. In the second part, we established a multi-indicator fatigue determination rule that uses PERCLOS and POM indicators and set a threshold to determine whether the driver is fatigue. By combining the YOLOv8 model with the fatigue determination rules based on PERCLOS and POM, we successfully built a model to identify a driver's fatigue. Finally, the fatigue detection model based on YOLOv8 achieves a high accuracy rate of 96.9% and a high speed of 11.4 ms/frame in face features detection, surpassing other traditional models and previous generations of YOLO models. This model can effectively detect fatigue in both driver-driving pictures and videos. © 2023 IEEE.

关键词： Roads and streets

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Black Box-Based Incremental Reduced-Order Modeling Framework of Inverter-Based Power systems

TechRxiv

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TechRxiv 2023年

作者： Zhou, Weihua Beerten, Jef The ELECTA Group Department of Electrical Engineering KU Leuven Leuven3000 Belgium EnergyVille Genk3600 Belgium The Department of Electrical and Computer Systems Engineering Monash University Victoria3800 Australia

Due to the capability to perform participation factor analysis and oscillation origin location, the state-space model (SSM)-based eigenvalue method has been widely used for stability assessment of inverter-penetrated power systems. However, possible internal confidentiality of inverters impedes the derivation of their SSMs. In addition, conventional derivation procedure of system SSM can be tedious when complicated transmission network topology and various transmission cables are involved, which may result in a high-order system SSM. To this end, this article presents a black box-based incremental reduced-order modeling framework. The reduced-order SSMs of the inverters and transmission cables are extracted from their dq-domain admittance frequency responses and abc-domain impedance frequency responses, respectively, by the matrix fitting algorithm. Then, the SSM operators proposed in this article recursively assemble the fitted components’ SSMs in the similar manner as the impedance model operator-based recursive components’ impedance aggregation, while preserving the dynamics of individual components. Simulation results show that the presented state-space modeling framework can properly identify the state-space models of black-box devices at component modeling stage, simplify assembling procedure at subsystems/components integration stage, and release computational burden at system participation factor analysis stage. © 2023, CC BY.

关键词： Factor analysis

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Machine learning for battery quality classification and lifetime prediction using formation data

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Energy and AI 2024年第4期18卷 518-530页

作者： Jiayu Zou Yingbo Gao Moritz H.Frieges Martin F.Börner Achim Kampker Weihan Li Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives(ISEA)RWTH Aachen UniversityCampus-Boulevard 8952074 AachenGermany Center for Ageing Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems(CARL)RWTH Aachen UniversityCampus-Boulevard 89AachenGermany Department of Computer Science RWTH Aachen University52056 AachenGermany Chair of Production Engineering of E-Mobility Components(PEM) RWTH Aachen UniversityBohr 1252072 AachenGermany Juelich Aachen Research Alliance JARA-EnergyGermany

Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety ***,we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation *** extract three classes of features from the raw formation data,considering the statistical aspects,differential analysis,and electro-chemical *** correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing *** learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery *** validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25℃ and 45℃,***,the lifetime prediction model is able to predict the battery end-of-life with mean per-centage errors of 6.50% and 5.45%for the batteries aged at 25℃ and 45℃,*** work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.

关键词： Battery Formation Quality classification Life prediction Machine learning

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Equilibrium-Independent Control of Continuous-Time Nonlinear systems via the LPV Framework - Extended Version

arXiv

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arXiv 2023年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free" concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified in a simulation study and also experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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Distributed Software-Defined Network Architecture for Smart Grid Resilience to Denial-of-Service Attacks

Distributed Software-Defined Network Architecture for Smart ...

引用

IEEE General Meeting Power& Energy Society

作者： Dennis Agnew Sharon Boamah Reynold Mathieu Austin Cooper Janise McNair Arturo Bretas Department of Electrical and Computer Engineering University of Florida Gainesville FL Distributed Systems Group Pacific Northwest National Laboratory Richland WA

An important challenge for smart grid security is designing a secure and robust smart grid communications architecture to protect against cyber-threats, such as Denial-of-Service (DoS) attacks, that can adversely impact the operation of the power grid. Researchers have proposed using Software Defined Network frameworks to enhance cybersecurity of the smart grid, but there is a lack of benchmarking and comparative analyses among the many techniques. In this work, a distributed three-controller software-defined networking (D3-SDN) architecture, benchmarking, and comparative analysis with other techniques is presented. The selected distributed flat SDN architecture divides the network horizontally into multiple areas or clusters, where each cluster is handled by a single Open Network Operating System (ONOS) controller. A case study using the IEEE 118-bus system is provided to compare the performance of the presented ONOS-managed D3-SDN, against the POX controller. In addition, the proposed architecture outperforms a single SDN controller framework by a tenfold increase in throughput; a reduction in latency of > 20%; and an increase in throughput of approximately 11% during the DoS attack scenarios.

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