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Thermo-mechanical energy storage technologies:Innovations,challenges and future directions

能源前沿（英文） 2025年第2期19卷 115-116页

作者： Yao Zhao Mingjia Li Kai Wang Adriano Sciacovelli Chris Qin Steven Lecompte André D.Thess College of Smart Energy Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Non-carbon Energy Conversion and Utilization Institute Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhou 310027China Key Laboratory of Refrigeration and Cryogenic Technology of Zhejiang Province Zhejiang UniversityHangzhou 310027China Department of Civil and Mechanical Engineering Technical University of DenmarkKongens Lyngby 2800Denmark School of Engineering and Computer Science Washington State UniversityVancouverWA 98686USA Department of Electromechanical Systems and Metal EngineeringGhent UniversityGhent 9000Belgium Institute of Engineering Thermodynamics German Aerospace Center(DLR)Stuttgart 70569Germany University of Stuttgart Stuttgart 70569Germany

Thermo-mechanical energy storage(TMES)technologies are increasingly recognized as essential solutions for enhancing the efficiency and stability of energy systems,particularly in the integration of renewable energy *** technologies leverage the conversion between thermal,electrical and mechanical energy to store and release energy,providing an effective tool to address the intermittency and variability of renewable sources such as wind and *** editorial explores the fundamental principles,innovations,challenges,and future research directions of TMES technologies,with a focus on developments in compressed-air energy storage(CAES),liquid-air energy storage(LAES),Carnot battery/pumped-thermal energy storage(PTES),and carbon dioxide energy storage(CDES).

关键词： technologies future storage energy challenges directions innovations mechanical thermo

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Optimal Strategies Trajectory with Multi-Local-Worlds Graph

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computers, Materials & Continua 2023年第4期75卷 2079-2099页

作者： Xiang Yu Chonghua Wang Xiaojing Zheng Chaoyu Zeng Brij BGupta School of Electronics and Information Engineering Taizhou UniversityTaizhouZhejiang318000China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China School of Economics and Management Weifang University of Science and TechnologyShouguangShandong262700China National Computer System Engineering Research Institute of China Beijing102699China Department of Computer Science and Information Engineering Asia UniversityTaiwanTaichung413China Research and Innovation Department Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates Staffordshire University Stoke-on-TrentST42DEUK

This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph would converge into a certain attractor if the system’s configuration isfixed. Due to the economics and management property, almost all systems aredivided into several independent Local-Worlds, and the interaction betweenagents in the system is more complex. The interaction between agents inthe same Local-World is defined as a stochastic differential cooperativegame;conversely, the interaction between agents in different Local-Worldsis defined as a stochastic differential non-cooperative game. We construct anon-cooperative/cooperative stochastic differential game model to describethe interaction between agents. The solutions of the cooperative and noncooperativegames are obtained by invoking corresponding theories, and thena nonlinear operator is constructed to couple these two solutions *** last, the optimal strategies trajectory of agents in the system is proven toconverge into a certain attractor, which means that strategies trajectory arecertainty as time tends to infinity or a large positive integer. It is concluded thatthe optimal strategy trajectory with a nonlinear operator of cooperative/noncooperativestochastic differential game between agents can make agentsin a certain Local-World coordinate and make the Local-World paymentmaximize, and can make the all Local-Worlds equilibrated;furthermore, theoptimal strategy of the coupled game can converge into a particular attractorthat decides the optimal property.

关键词： Multi-Local-world graph stochastic differential game optimal strategy attractor

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Automatic License Plate Detection Using Information Processing system Vision and Deep Learning 2

Automatic License Plate Detection Using Information Processi...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Sharma, Avinash Dwivedi, Ishita Dawar, Divya Saxena, Rini Barua, Tarkcshwar Goyel, Parul Chandigarh Engineering Chandigarh Group of Colleges Department of information processing system Science and Engineering Mohali140307 India Mohali140307 India Roorkee Institute of Technology Computer Science & Engineering Department Roorkee India M. M. Engineering College Maharishi Markandeshwar University CSE Department Mullana Haryana Ambala133207 India

ISBN: (纸本)9798350366570

The content of this probe is to overcome the current cognitive content with ALPR playacting, with a special accent on developing a dead and impelling playacting for distinguishing License plates from video film. The study attempts to ameliorate the presentation norms of ALPR technology by utilizing the powerful accumulation of optical character identification (OCR)[20] and deep learning based object detection algorithmic program. The core of this occupation is the use of the cutting-edge YOLOv8. object distinguishing hypothesis, which has been pains taking and disciplined on an unequaled dataset premeditated with License plate detection in mind. Focusing on quality and efficiency, the YOLOv8 model accomplish an amazing 99.8 quality rate when it comes to accurately localization and categorization License plates[18] inside video frames. This occupation aims to formalize the willingness and dependability of the advisable ALPR playacting through extensive scientific research. The project aims to intensify ALPR technology and determinant the forthcoming of transport and police investigation playactings[18] by breaking through current obstruction and establishing new presentation norms. © 2024 IEEE.

关键词： Deep learning

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A Novel Framework for Scalable Video Transmission Based on the Outage Probability Analysis of MIMO Coding 4

A Novel Framework for Scalable Video Transmission Based on t...

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4th Information Communication Technologies Conference, ICTC 2023

作者： Zhang, Yongjian Wang, Dongyu Zhang, Zhiqiang Wang, Shiguo University of International Relations School of Cyber Science and Engineering Beijing China North China University of Technology Department of Electronic Engineering Beijing China Shanxi Police College Department of Cyber Security Taiyuan China University of Science and Technology Computer and Communication Engineering Institute Changsha Changsha China

ISBN: (纸本)9781665462587

This work presents a radical approach for space-time codings, which could realize the goal of unequal error protection (UEP) transmitting for scalable video. This analysis leads us to develop a hybrid coding framework that combines vertical bell-labs layered space-time (V-BLAST) to ensure high-data-rate for the less crucial enhancement layer (EL) and Alamouti coding to ensure high reliability for the base layer (BL). According to those simulation findings, the suggested hybrid delivery method performs substantially better based on peak signal-to-noise ratio than either a pure special multiplexing or a pure spatial diversity delivery scheme. © 2023 IEEE.

关键词： MIMO systems

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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 264-280页

作者： Liu, Ruonan Zhang, Quanhu Han, Te Yang, Boyuan Zhang, Weidong Yin, Shen Zhou, Donghua Shanghai Jiao Tong University Department of Automation Shanghai200240 China Tianjin University College of Intelligence and Computing Tianjin300350 China Beijing Institute of Technology Center for Energy and Environmental Policy Research Beijing100081 China Beijing Institute of Technology School of Management Beijing100081 China Beijing Laboratory for System Engineering of Carbon Neutrality Beijing100081 China Nanjing University Center for Advanced Control and Smart Operations Suzhou215163 China Hainan University School of Information and Communication Engineering Haikou570228 China Shanghai Jiaotong University Department of Automation Shanghai200240 China Norwegian University of Science and Technology Trondheim7491 Norway

Industrial Cyber-Physical systems (ICPS) integrating disciplines such as computer science, communication technology, and engineering, have become a crucial component of modern manufacturing and industry. However, ICPS faces numerous challenges during long-term operation, including equipment faults, performance degradation, and security threats, etc. To achieve efficient maintenance and management, prognostics and health management (PHM) has been widely applied in the critical tasks of ICPS such as fault prediction, health monitoring, and maintenance decision-making. The emergence of large-scale foundation models (LFMs) like BERT and GPT marks a significant advancement in artificial intelligence (AI) technology, demonstrating substantial application potential in multiple fields. The accumulation of AI technology, rapid development of LFMs, and the abundance of industrial data and industrial process knowledge provide the foundational conditions for the construction and advancement of industrial LFMs. However, there is currently a lack of consensus on applying LFMs of PHM in ICPS, necessitating a systematic review and roadmap to clarify future development directions. To bridge this gap, this survey provides a comprehensive survey and understanding of the recent advances in LFMs of PHM in ICPS. It provides valuable references for decision makers and researchers in the industry, and helps to further improve the reliability, availability and safety of ICPS. © 2023 IEEE.

关键词： Cyber Physical system

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Multi-resolution Small Sample Face Recognition Based on Coupled Dictionary Learning 8

Multi-resolution Small Sample Face Recognition Based on Coup...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Fan, Xiaojin Zou, Weidong Wang, Changzhi School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Automation Beijing Institute of Technology Beijing China Fudan University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9781665450515

Multi-resolution small sample face recognition has always been a challenge in the field of face recognition. This paper proposes a multi-resolution small sample face recognition method based on coupled dictionary learning (MCDL). To expose the relationship between the representation coefficients corresponding to dictionary atoms and samples at different resolutions, MCDL introduces an analysis dictionary and jointly learns it with the synthesis dictionary. Besides, various coding coefficients are applied to each sample with varied resolutions throughout the multi-dictionary learning process so that the learnt dictionary is more realistic. Furthermore, we introduce the fractional norm into the model to remove the redundant information. Experimental results show that MCDL achieves good results and its recognition rate is higher than that of many algorithms. © 2022 IEEE.

关键词： Face recognition

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting 6

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Omar, Mas Yakub, Fitri Talip, Mohamad Sofian Abu Maslan, Mohd Nazmin Sinha, Vijay Kumar Muljono, Muljono Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Teknikal Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Melaka Malaysia Chitkara University School of Engineering & Technology Department of Computer Science & Engineering Himachal Pradesh India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350353853

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement system. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications. © 2024 IEEE.

关键词： Mean square error

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Simulation of Vision-based Tactile Sensors with Efficiency-tunable Rendering

Simulation of Vision-based Tactile Sensors with Efficiency-t...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Sun, Yuhao Zhang, Shixin Shan, Jianhua Zhao, Lei Wang, Xiangbo Sun, Fuchun Yang, Yiyong Fang, Bin Anhui University of Technology School of Mechanical Engineering China Tsinghua University Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Beijing100084 China School of Engineering and Technology Beijing100083 China Hebei University School of Quality and Technology Supervising Baoding China

ISBN: (纸本)9798350325706

Vision-based tactile sensors (VBTS) leverages visual modality to present high-resolution tactile information. The vision-based sensing mechanism has good adaptability to robot manipulation because of available information capture and data processing. However, the simulation of VBTS is a challenging problem because it deals with computer graphics and elasto-plastic deformation. In this paper, we propose a simulation approach for VBTS using a rendering approach based on path tracing. The rendering method can be integrated with existing VBTS and robotic simulators for the simulation of robotic systems. In addition, this method can control the rendering efficiency and quality by controlling the number of voxels in the fitting deformation area to meet the requirements of efficient robot training. The experimental results verify that our method can provide high-quality images at low rendering efficiency or reduce image quality to improve rendering efficiency. Our work has the potential to advance the sim-to-real research on VBTS. © 2023 IEEE.

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Task Assignment of Heterogeneous Robots Based on Large Model Prompt Learning 1

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2nd International Conference on Cognitive Computation and systems, ICCCS 2023

作者： Deng, Mingfang Wang, Ying Lu, Lingyun Zhao, Huailin Yuan, Xiaohu School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China School of Physics and Electronic Information Yantai University Shandong Yantai China Nanjing Research Institute of Electronic Engineering Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

In order to ensure that the heterogeneous robot clusters performing the task can complete the target search at the specified location of the target object that the user needs to search according to their own field of view capabilities, we propose a task assignment algorithm for heterogeneous indoor robot clusters based on the robot’s own field of view constraints. In particular, the heterogeneous robot clusters need to be parsed by linguistic commands to obtain the assignment results. Therefore, we solve the task assignment of heterogeneous robot clusters by performing cue learning on a large model to achieve the maximum utilization of heterogeneous robots while satisfying the field-of-view constraint;the simulation verifies the effectiveness of the task assignment of this method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Learning systems

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