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Space science Reviews 2023年第6期219卷 1-2页

作者： Englert, Christoph R. Harlander, John M. Marr, Kenneth D. Harding, Brian J. Makela, Jonathan J. Fae, Tori Brown, Charles M. Ratnam, M. Venkat Rao, S. Vijaya Bhaskara Immel, Thomas J. Space Science Division U.S. Naval Research Laboratory Washington USA Space Systems Research Corporation Alexandria USA Space Sciences Laboratory University of California Berkeley Berkeley USA Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana USA National Atmospheric Research Laboratory Pakala India Department of Physics Sri Venkateswara University Tirupati India

Applications of MXenes in human-like sensors and actuators

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Nano Research 2023年第4期16卷 5767-5795页

作者： Jinbo Pang Songang Peng Chongyang Hou Xiao Wang Ting Wang Yu Cao Weijia Zhou Ding Sun Kai Wang Mark H.Rümmeli Gianaurelio Cuniberti Hong Liu Institute for Advanced Interdisciplinary Research(iAIR) Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of ShandongUniversity of JinanJinan 250022China Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics Dresden Technische Universität DresdenDresden 01069Germany Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of TechnologyShandong Academy of SciencesJinan 250353China School of Bioengineering Qilu University of TechnologyShandong Academy of ScienceJinan 250353China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Ministry of Education) Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266000China School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchun 130118China Institute for Complex Materials Leibniz Institute for Solid State and Materials Research Dresden(IFW Dresden)20 Helmholtz StrasseDresden 01069Germany College of Energy Soochow Institute for Energy and Materials Innovations Soochow UniversitySuzhou 215006China Key L

Human beings perceive the world through the senses of sight,hearing,smell,taste,touch,space,and *** first five senses are prerequisites for people to *** sensing organs upload information to the nervous systems,including the brain,for interpreting the surrounding ***,the brain sends commands to muscles reflexively to react to stimuli,including light,gas,chemicals,sound,and ***,as an emerging two-dimensional material,has been intensively adopted in the applications of various sensors and *** this review,we update the sensors to mimic five primary senses and actuators for stimulating muscles,which employ MXene-based film,membrane,and composite with other functional ***,a brief introduction is delivered for the structure,properties,and synthesis methods of ***,we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas,gas sensors,chemical biosensors,acoustic devices,and tactile sensors for electronic ***,the actuators of MXene-based composite are ***,future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot,which may induce prospects in accompanying healthcare and biomedical engineering applications.

关键词： MXenes sensors actuators gas sensors biosensors tactile sensors sound sensors artificial retina

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

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arXiv 2023年

作者： Li, Zilinghan He, Shilan Chaturvedi, Pranshu Hoang, Trung-Hieu Ryu, Minseok Huerta, E.A. Kindratenko, Volodymyr Fuhrman, Jordan Giger, Maryellen Chard, Ryan Kim, Kibaek Madduri, Ravi Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States University of Chicago Consortium for Advanced Science and Engineering ChicagoIL60637 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Department of Radiology University of Chicago Hyde ParkIL60616 United States

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://*** Index Terms—Federated learning, privacy preserving, federation as a service, AI for science, science as a service, HPC, IAM Copyright © 2023, The Authors. All rights reserved.

关键词： Life cycle

Coupling between automatic generation control and automatic voltage regulator while considering generation rate constraint of two areas 2

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Coupling between automatic generation control and automatic ...

2nd International Conference on advanced electrical and Energy Systems, AEES 2021

作者： Elmukhtar, Omer Zhang, Jianhua Department of computer science and control engineering North China Electric Power University Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

This paper describes the implementation of automatic generation control (AGC) and automatic voltage regulator (AVR) under generation rate constraint (GRC) and coupling between automatic generation control and automatic voltage regulator while considering generation rate constrain of two areas for improve and better performance characteristic of load frequency control (LFC) problem. our control aim has to normalize the area control error (ACE), frequency error and tie-line power error in spite of the presences of system uncertainties and external load disturbance, So that the required frequency and power interchange with adjacent structures are maintained in order to reduce the transient deviations and to provide zero steady state error in proper short time, the behavior of the planned is checked by MATLAB SIMULINK software. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Voltage regulators

Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises

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Design and Application of a One-Stop Mobile Application Shar...

Chinese Control Conference (CCC)

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz 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 AB Canada

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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Application of Weighted Chebyshev Approximation in Pulse Design for Quantum Gates

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Application of Weighted Chebyshev Approximation in Pulse Des...

IEEE Workshop on Signal Processing Systems (SIPS)

作者： Qi Ding Alan V. Oppenheim Petros T. Boufounos Simon Gustavsson Jeffrey A. Grover Thomas A. Baran William D. Oliver Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA Mitsubishi Electric Research Laboratories Cambridge MA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Atlantic Quantum Cambridge MA USA

ISBN: (数字)9798350373752

ISBN: (纸本)9798350373769

Chebyshev approximation problems are fundamen-tal in approximation theory and are integral to various engi-neering applications. In this study, we suggest that weighted Chebyshev approximation (WCA) can be effectively applied to designing quantum gates. Specifically, we examine a class of two-qubit gates in superconducting qubits known as controlled-phase (CPHASE) gates, which leverage interactions between higher levels controlled by baseband flux pulses. We frame the design of CPHASE gates as a pulse design problem and propose Chebyshev pulses, generated through WCA, as an alternative solution. We utilize an illustrative example to demonstrate our process of designing a Chebyshev pulse with WCA, then contrast it with a benchmark Slepian pulse for comparison. We find that Chebyshev pulses can facilitate faster CPHASE gates while ensuring leakage errors remain below the desired threshold.

关键词： Baseband Simulation Design methodology Conferences Qubit Logic gates Chebyshev approximation Benchmark testing Signal processing Iterative methods

YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction

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arXiv 2025年

作者： Lin, Ziyu Wu, Yunfan Ma, Yuhang Chen, Junzhou Zhang, Ronghui Wu, Jiaming Yin, Guodong Lin, Liang Guangdong Key Laboratory of Intelligent Transportation System School of intelligent systems engineering Sun Yat-sen University Guangzhou510275 China Department of Architecture and Civil Engineering Chalmers University of Technology Sven Hultins gata 6 GothenburgSE-412 96 Sweden School of Mechanical Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China

Detecting traffic signs effectively under low-light conditions remains a significant challenge. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. Firstly, we introduce the High-Resolution Feature Map for Small Object Detection (HRFM-TOD) module to address indistinct small-object features in low-light scenarios. By leveraging high-resolution feature maps, HRFM-TOD effectively mitigates the feature dilution problem encountered in conventional PANet frameworks, thereby enhancing both detection accuracy and inference speed. Secondly, we develop the Multi-branch Feature Interaction Attention (MFIA) module, which facilitates deep feature interaction across multiple receptive fields in both channel and spatial dimensions, significantly improving the model’s information extraction capabilities. Finally, we propose the Prior-Guided Enhancement Module (PGFE) to tackle common image quality challenges in low-light environments, such as noise, low contrast, and blurriness. This module employs prior knowledge to enrich image details and enhance visibility, substantially boosting detection performance. To support this research, we construct a novel dataset, the Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios, including urban, highway, and rural environments under varying weather conditions. Experimental evaluations demonstrate that YOLO-LLTS achieves state-of-the-art performance, outperforming the previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, and achieving superior results on the CCTSDB2021 dataset. Moreover, deployment experiments on edge devices confirm the real-time applicability and effectiveness of our proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

Automating Theory of Mind Assessment with a LLaMA-3-Powered Chatbot: Enhancing Faux Pas Detection in Autism

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Automating Theory of Mind Assessment with a LLaMA-3-Powered ...

International eConference on computer and Knowledge engineering (ICCKE)

作者： Avisa Fallah Ali Keramati Mohammad Ali Nazari Fatemeh Sadat Mirfazeli School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran National Brain Centre Iran University of Medical Sciences Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Neuroscience Advanced Technologies in Medicine Iran University of Medical Sciences Image Brain Institute Tehran Iran Department of Psychiatry Mental Health Research Center Advanced Technologies in Medicine National Brain Centre School of Medicine Psychosocial Health Research InstituteIran University of Medical Sciences Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

The Theory of Mind (ToM) is critical for social interactions, enabling individuals to understand and predict others' mental states. Deficits in ToM, particularly in detecting faux pas, are common among individuals with Autism Spectrum Disorder (ASD). Traditional methods for assessing ToM, such as the Faux Pas Recognition Test (FPRT), require significant human involvement, which can limit accessibility and scalability. This study aims to explore how large language model (LLM)-powered chatbots, specifically utilizing the LLaMA-3 model, can enhance the assessment of ToM by automating and improving the administration of the FPRT. The methodology involved creating a dual-phase process where the chatbot first assesses user responses and then provides rehabilitative guidance to correct misunderstandings. Additionally, the system employs a scoring mechanism combining automated evaluation with human oversight to ensure accuracy and consistency, with system prompts crafted based on chain-of-thought prompting to enhance the depth and reliability of the assessments. Our findings demonstrate the effectiveness of LLM-powered chatbots in delivering dynamic and supportive ToM assessments, suggesting its potential for broader application for both artificial intelligence and psychological practices.

关键词： Autism Accuracy Target recognition Scalability Large language models Mental health Reliability theory Chatbots Real-time systems Testing

Confederated Learning: Federated Learning with Decentralized Edge Servers

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arXiv 2022年

作者： Wang, Bin Fang, Jun Li, Hongbin Yuan, Xiaojun Ling, Qing The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States The School of Computer Science and Engineering Sun Yat-Sen University Guangdong Guangzhou510006 China Peng Cheng Laboratory Guangdong Shenzhen518066 China

Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner. Due to stringent communication and bandwidth constraints, such a centralized framework has limited scalability as the number of devices grows. To address this issue, in this paper, we propose a ConFederated Learning (CFL) framework. The proposed CFL consists of multiple servers, in which each server is connected with an individual set of devices as in the conventional FL framework, and decentralized collaboration is leveraged among servers to make full use of the data dispersed throughout the network. We develop an alternating direction method of multipliers (ADMM) algorithm for CFL. The proposed algorithm employs a random scheduling policy which randomly selects a subset of devices to access their respective servers at each iteration, thus alleviating the need of uploading a huge amount of information from devices to servers. Theoretical analysis is presented to justify the proposed method. Numerical results show that the proposed method can converge to a decent solution significantly faster than gradient-based FL algorithms, thus boasting a substantial advantage in terms of communication efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Model

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arXiv 2024年

作者： Zhang, Xuanchang Zhang, Zhuosheng Zhao, Hai College of Zhiyuan Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

Despite the rapid progress of large language models (LLMs), their task performance remains sensitive to prompt design. Recent studies have explored leveraging the LLM itself as an optimizer to identify optimal prompts that maximize task accuracy. However, when evaluating prompts, such approaches heavily rely on elusive manually annotated gold labels to calculate task accuracy for each candidate prompt, which hinders its generality. To overcome the limitation, this work proposes GLaPE, a gold label-agnostic prompt evaluation method to alleviate dependence on gold labels. GLaPE is composed of two critical aspects: self-consistency evaluation of a single prompt and mutual-consistency refinement across multiple prompts. Experimental results on 8 widely-recognized reasoning tasks demonstrate that GLaPE can produce more effective prompts, achieving performance comparable to those derived from manually annotated gold labels. Analysis shows that GLaPE provides reliable evaluations aligned with accuracy, even in the absence of gold labels. Code is publicly available at https://***/thunderous77/GLaPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Gold