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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Xi Zhu, Qixuan Texas A&m University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

ISBN: (纸本)9798350351255

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support communications among massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements in 6G. Correspondingly, the integrated sensing and communications (ISAC) technique can improve various QoS performances through spectrum/hardware sharing between target-sensing and data-communications. In addition, the massive multiple-input multiple-output (massive MIMO) and finite blocklength coding (FBC) have been also widely recognized as the powerful 6G techniques to efficiently support mURLLC. However, how to apply the ISAC technique to accurately sense and estimate the wireless channel state while transmitting the information from the base station to MUs to support mURLLC using massive MIMO and FBC has imposed many new difficulties not encountered before. To conquer these challenges, in this paper we propose the new ISAC performance metrics for statistical-QoS guaranteed mURLLC over 6G massive MIMO mobile networks using FBC. First, we establish system models for ISAC-based architectures. Second, we detect targeted MUs and estimate the channel state using the radar sensing echo. Third, we develop a new ISAC performance metric as a function of the data-communication rate and channel state estimation distortion. Finally, we use the numerical analyses to validate and evaluate our developed ISAC performances in supporting mURLLC-oriented QoS requirements. © 2024 IEEE.

关键词： Channel estimation

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Low Power Network-on-Chip Architecture Design Technique 32

Low Power Network-on-Chip Architecture Design Technique

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Musale, Tejas Ganti, Arun Gogoi, Ankur Manna, Kanchan Bits Pilani Department of Electrical and Electronics Engineering India Bits Pilani Department of Computer Science and Information Systems India Galgotias University Uttar Pradesh India

ISBN: (纸本)9798331539672

In the evolving landscape of many-core architectures, the Network on Chip (NoC) emerges as a pivotal inter-connection framework, accommodating the escalating number of cores on a single chip. Despite its widespread adoption, power consumption remains a critical challenge, significantly influenced by factors such as topology, bit toggling, and routing algorithms, with bit-switching power being a predominant concern. In this work, we have proposed an innovative technique in the cores aimed at reducing the power consumption of the NoC. To calculate our method's area, time, and power consumption, we have synthesized it on a Xilinx Virtex-7 FPGA board. We have modified the Noxim open-source simulator to validate our approach using synthetic traffic. Empirical results demonstrate a 29.64% reduction in dynamic power consumption and a 29.09% improvement in switching activity for the average case (Mode 1) traffic scenario. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Moreau Envelope Approach for LQR Meta-Policy Estimation 63

A Moreau Envelope Approach for LQR Meta-Policy Estimation

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Aravind, Ashwin Toghani, Mohammad Taha Uribe, Cesar A. Iit Bombay Centre for Systems and Control MH Mumbai India Rice University Department of Electrical and Computer Engineering HoustonTX United States

ISBN: (纸本)9798350316339

We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear timeinvariant uncertain dynamical systems. We propose a Moreau Envelope-based surrogate LQR cost, built from a finite set of realizations of the uncertain system, to define a meta-policy efficiently adjustable to new realizations. Moreover, we design an algorithm to find an approximate first-order stationary point of the meta-LQR cost function. Numerical results show that the proposed approach outperforms naive averaging of controllers on new realizations of the linear system. We also provide empirical evidence that our method has better sample complexity than Model-Agnostic Meta-Learning (MAML) approaches. © 2024 IEEE.

关键词： Cost functions

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural Computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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ZCD Circuit Improvement and Novel Signal Processing Method for GaN-Based CRM PWM Converters

ZCD Circuit Improvement and Novel Signal Processing Method f...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Chen, Xingyu Yu, Gibum Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9798350376067

This paper introduces a design guidance for zero current detection (ZCD) circuit in Gallium-Nitride (GaN) device based critical conduction mode (CRM) pulse-width-modulation (PWM) converters to reduce the sensing delay, avoid false trigger and prevent trigger missing. A ZCD signal processing logic is also proposed for CRM PWM converters to limit the maximum switching frequency while keeping a simple closed loop control structure. The proposed ZCD circuit design and ZCD signal processing method are verified in the simulation and on a GaN based CRM buck converter. © 2024 IEEE.

关键词： Pulse width modulation

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Explainability Spectrum Analysis of Weather Sequences in Short-Term Load Forecasting 14th

Explainability Spectrum Analysis of Weather Sequences in Sho...

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14th International Conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Kontogiannis, Dimitrios Bargiotas, Dimitrios Daskalopulu, Aspassia Tsoukalas, Lefteri H. Department of Electrical and Computer Engineering University of Thessaly Volos Greece Center for Intelligent Energy Systems Purdue University West Lafayette United States

ISBN: (纸本)9783031674259

Short-term load forecasting tasks utilize a plethora of weather sequences in order to study meaningful features that describe complex relationships between environmental parameters and the variable of load, as well as the occurrence of impactful events, thus reinforcing the predictive power of estimators. It is evident that the inclusion of those additional features increases the input dimensions and decreases the overall explainability of forecasting models, since not all of the extracted features are equally compatible with the assumptions of the regressors, resulting in models where the justification of predictive potency, the connection between the variance of the target variable and the variance of predictors, as well as the explanation of the computational flow become increasingly difficult due to ambiguity. Therefore, this analysis investigates the representativeness of weather variables and examines the explainability of interpretable short-term load forecasting structures that utilize environmental sequences towards the selection of important feature subsets that maximize the explained variance of load time series as well as the seasonal, trend and residual load components during training. This methodology considers linear regression and prominent tree-based estimators coupled with model-dependent feature importance frameworks utilizing game theoretic, permutative and impurity-based approaches for the derivation of optimal feature sets and feature set combinations in terms of explained variance scores. Additionally, two structural approaches of explainable feature set generation from the joint feature importance scores through the utilization of hierarchical and majority inclusion are compared, their execution behavior towards explainability maximization is examined, and the main observations from each stage of this analysis are discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Weather forecasting

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Data Fusion-based Hybrid Framework for Cyber-physical Security of the Smart Grid

Data Fusion-based Hybrid Framework for Cyber-physical Securi...

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2024 Resilience Week, RWS 2024

作者： Boamah, Sharon A. Mathieu, Reynold Faillace, Victor Agnew, Dennis McNair, Janise Bretas, Arturo University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States Sandia National Laboratory Distributed Systems Group United States

ISBN: (纸本)9798350388985

The integration of information communication technology with the power grid exposes it to cyber threats. The network state estimation process provides stability to the smart grid. The communication network plays a major role in ensuring the successful transmission of state information. However, these network measurements are vulnerable to malicious attacks. This subsequently affects the network measurement such as associated high transmission delays and packet losses affecting the reliability of the smart grid. In this work, we propose a hybrid physics-based data-driven model that uses data fusion from the state-of-the-art physics-based Network State Estimation model and a data-driven model to detect false data injection attacks in the communication network layer of the smart grid. The performance of the data fusion method is evaluated and the simulation results show that the proposed model outperforms the standalone approaches in the detection of bad data. This shows that the proposed scheme is able to improve the cyber-physical security of the communication network layer of the smart grid. © 2024 IEEE.

关键词： Cyber attacks

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Symmetric Series-Capacitor Buck in 48V-to-12V Regulated Conversion for High-Performance Server Boards

Symmetric Series-Capacitor Buck in 48V-to-12V Regulated Conv...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Xu, Xinmiao Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798350376067

This paper presents an extended-conversion-ratio modulation for a two-phase symmetric series-capacitor buck (SSCB) converter. With the proposed scheme, highly efficient and regulated 48V-to-12V conversion can be realized with the SSCB topology. A versatile regulated first stage streamlines power delivery architectures for server boards in data centers, because regulated 12-V buses not only serve loads requiring well-controlled 12-V input but also simplify design and optimization of a 12V-to-1V second stage for processors. Based on this idea, a highly efficient and regulated power architecture is proposed for 48-V server boards. A prototype is built with gallium nitride (GaN) transistors and minimal high-frequency loop inductance, operating with a relatively wide input voltage range from 40 V to 60 V and generating a regulated 12-V output. In 48V-to-12V regulated conversion, the GaN-based prototype achieves 98.88% peak efficiency, 98.11% full-load efficiency (including gating loss) and 787 W/inch2 power density with 80-A output current, which is beyond the performance of state-of-the-art commercially available solutions. © 2024 IEEE.

关键词： Gallium nitride

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A large-area bionic skin for high-temperature energy harvesting applications

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Nano Research 2023年第7期16卷 10245-10255页

作者： Zhaojun Liu Bian Tian Yao Li Jiaming Lei Zhongkai Zhang Jiangjiang Liu Qijing Lin Chengkuo Lee Zhuangde Jiang State Key Laboratory for Mechanical Manufacturing Systems Engineering School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117576Singapore Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China

For the large amount of waste heat wasted in daily life and industrial production,we propose a new type of flexible thermoelectric generators(F-TEGs)which can be used as a large area bionic skin to achieve energy harvesting of thermal *** reference to biological structures such as pinecone,succulent,and feathers,we have designed and fabricated a biomimetic flexible TEG that can be applied in a wide temperature range which has the highest temperature energy harvesting capability *** laminated free structure of the bionic F-TEG dramatically increases the efficiency and density of energy *** F-TEGs(single TEG only 101.2 mg in weight),without an additional heat sink,demonstrates the highest output voltage density of 286.1 mV/cm^(2)and the maximum power density is 66.5 mW/m^(2) at a temperature difference of nearly 1000℃.The flexible characteristics of F-TEGs make it possible to collect the diffused thermal energy by flexible attachment to the outer walls of high-temperature pipes and vessels of different diameters and *** work shows a new design and application concept for flexible thermal energy collectors,which fills the gap of flexible energy harvesting in high-temperature environment.

关键词： bionic structure high-temperature flexible thermoelectric generators(TEGs)

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