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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Rajanish Kumar Kaushal Shubhi Jain R Gowri Shankar Rao Babu Thirumalaimuthu Ruchira Rawat Natrayan L Department of Electrical Engineering Chandigarh University Gharuan Mohali Punjab India Department of Electronics and Communication Swami Keshvanand Institute of Technology Management and Gramothan Jaipur Rajasthan India Department of Physics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Instrumentation and Control Engineering St. Joseph’s College of Engineering Chennai Tamil Nandu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The popularity of vehicles based on electricity is increasing as they are considered a sustainable mode of transportation. The charging infrastructure for electric vehicles are suffering various issues. There are limited availability of public charging stations, the high costs associated with installing private charging stations, and the high variability of electricity prices. The peer-to-peer (P2P) energy stability will be seen as a potential solution to facilitate electric vehicle (EV) charging in a manner that is both cost-effective and environmentally sustainable. The paper presents a novel system energy trading and charging payment for electric vehicles (EVs). The desired system enables EV owners to exchange surplus electricity with other EV owners in their location. The system being proposed utilizes blockchain technology to guarantee secure and transparent transactions. Additionally, it incorporates a smart contract to automate the trading and charging process. The system utilizes a dynamic pricing mechanism that adjusts in real-time to reflect the current electricity prices. This feature is designed to encourage electric vehicle owners to engage in energy stabilization during maximum usage hours. The Optimistic results shows that the described system has the efficacy on minimising the cost of vehicle charging and to decrease peak demand on the grid. Additionally, the system encourages the utilization of renewable energy sources.

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A Joint Matrix Factorization and Clustering Scheme for Irregular Time Series Data

SSRN

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SSRN 2022年

作者： He, Shiming Guo, Meng Li, Zhuozhou Zhou, Siyuan Xie, Kun Xiong, Naixue Key Laboratory of Safety Control of Bridge Engineering Ministry of Education School of Computer and Communication Engineering Changsha University of Science and Technology Hunan Changsha410114 China College of Computer Science and Electronics Engineering Hunan University Hunan Changsha410082 China Department of Mathematics and Computer Science Northeastern State University TahlequahOK74464 United States

Key Performance Indicator (KPI) clustering plays an important role in Artificial Intelligence for IT Operations when facing large-scale KPIs. It can effectively reduce the overhead by dividing KPIs into several classes, then applying the same anomaly detection or prediction model to all KPIs in a class. However, KPI sampling strategies vary according to the various environments, which produces irregular KPIs. Few existing works have considered the clustering of KPIs with irregular sampling. Matrix factorization (MF) is widely applied in low-rank data recovery and can be used to align and fill the irregular KPIs. However, the clustering performance after recovering and filling by MF remains unknown. These two problems interact with each other and should be solved together. Therefore, we formulate the joint MF and clustering problem for irregular KPIs and design an iterative clustering scheme based on MF. The iterative clustering scheme comprises alignment and pre-filling, the loop of clustering and subclass filling by MF, which can work with two pre-filling methods and any type of clustering algorithms. Extensive experiments on two real-world datasets show that the iterative clustering scheme can obtain higher normalized mutual information (NMI) than non-iterative clustering, and moreover consumes less computational time than Dynamic Time Warping. © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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A double-ended contactless current traveling waves scheme for fault location in overhead transmission lines 4

A double-ended contactless current traveling waves scheme fo...

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4th IEEE Conference on Energy Internet and Energy System Integration, EI2 2020

作者： Ayambire, Patrick Nyaaba Huang, Qi Bamisile, Olusola Anane, Paul Oswald Kwasi Awopone, Albert K. Sichuan Provincial Key Lab of Power System Wid-Area Measurement and Control School of Mechanical and Electrical Engineering Chengdu China University of Education College of Technology P.O. Box 1277 Winneba Kumasi Ghana

ISBN: (纸本)9781728196060

This study presents a contactless current traveling wave fault location scheme for a two-terminal overhead transmission line based on a tunnel Magnetoresistive sensor. The proposed method is implemented by installing a tunnel Magnetoresistive sensor array at both ends of the line for current traveling wave measurement. The measured data is transmitted to a remote monitoring unit (RMU) for analysis. The performance of the proposed method is evaluated for different fault situations using an experimental set-up. The proposed exhibited high efficiency for current traveling wave fault location. The results were analyzed for fault location using the differentiator smoother to reduce the time of arrival errors. The proposed scheme is compared with a general traveling wave-based scheme for transmission line fault distance location. In all test cases, the proposed scheme offered very efficient results for all categories of faults with an average relative error of 0.024%. © 2020 IEEE.

关键词： Location

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Raspberry-like Cu 2 O/BiOBr S-scheme heterojunction photocatalyst for selective CO 2 photoreduction to CO

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Journal of Alloys and Compounds 2025年 1035卷

作者： Chenyu Liang Junzhu Wang Kai Zhang Jie Zhao Na Liu Shutao Wang College of Metrology Measurement and Instrument China Jiliang University Hangzhou Zhejiang 310018 China School of Communication Engineering Hangzhou Dianzi University Hangzhou Zhejiang 310018 China Measurement Technology and Instrumentation Key Lab of Hebei Province Yanshan University Qinhuangdao Hebei 066004 China

Developing efficient and stable photocatalysts for CO 2 reduction is crucial for mitigating greenhouse gas emissions and addressing energy challenges. However, low photogenerated carriers separation efficiency and poor photocatalyst stability hinder their practical applications. In this work, a raspberry-like Cu 2 O/BiOBr (COB) S-scheme heterojunction photocatalyst is successfully synthesized via a hydrothermal method. Structural characterizations reveal that BiOBr uniformly encapsulates Cu 2 O, forming a tightly bonded interface conducive to charge transfer. Band structure analysis combined with density functional theory (DFT) calculations indicated that an internal electric field (IEF) is generated at the Cu 2 O/BiOBr interface, establishing an S-scheme charge transfer pathway. This configuration not only enhances the separation of photogenerated carriers but also significantly improves the photostability of Cu 2 O. As a result, the optimized COB-5 photocatalyst exhibits excellent CO 2 photoreduction performance with a CO production rate of 47.0 μmol·g −1 ·h −1 , along with superior cycling stability. Furthermore, in-situ infrared spectroscopy and Gibbs free energy calculations revealed that COB-5 effectively lowers the energy barrier for the formation of the key intermediate COOH*, facilitating the selective generation of CO. This study provides a promising strategy for the rational design of high-performance photocatalysts for efficient and stable CO 2 reduction.

关键词： Charge carrier separation CO2 photoreduction Density Functional Theory (DFT) calculation Internal electric field S-scheme heterojunction

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Emerging contaminants:A One Health perspective

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The Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan Hongwen Sun Taicheng An Guangguo Ying Bryan WBrooks Deyi Hou Damian EHelbling Jianqiang Sun Hao Qiu Timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong Tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene Harindintwali Xiankui Zeng Haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran Huang Japhet Cheuk-Fung Law Chenggang Gu Huizhong Shen Yanpeng Gao Chao Qin Hao Li Tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi Hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye Teresa Rocha-Santos Huan He Yi Yang Meiping Tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed AHashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.Tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward Topp Xinde Cao Xin Jiang Minghui Zheng Taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.Tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina HKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina Technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany School of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of Environmental Science&Engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of Environmental Theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of Environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA School of Environment Tsinghua UniversityBeijing 100084China School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

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A Deep Reinforcement Learning-Based Multi-Objective Charging Scheduling Method for Electric Vehicle Charging Station

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IEEE Internet of Things Journal 2025年

作者： Zhao, Qiang Wu, Wenwei Cheng, Ningyu Guo, Ge Han, Yinghua Northeastern University at Qinhuangdao School of Control Engineering Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinghuangdao066004 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Northeastern University at Qinhuangdao School of Computer and Communication Engineering Hebei Qinhuangdao066004 China

This paper investigates the charging scheduling problem within an Electric Vehicle Charging Station (EVCS), aiming to coordinate multiple charging piles to optimize the charging process. The objectives are to increase the profit for the Charging Station Operator (CSO) and enhance user satisfaction while mitigating significant fluctuations in grid load. However, since these objectives originate from different stakeholders, they are inherently conflicting;a strategy trained using fixed objective weights cannot satisfy all of them simultaneously. Additionally, uncertainties such as vehicle arrival and departure times, power requirements, and grid tariffs complicate the modeling of this process. To address these challenges, this paper reformulates the scheduling process as a Markov Decision Process (MDP) and proposes a novel model-free reinforcement learning method. By integrating randomly varying objective preferences into the inputs of agents, a set of policies corresponding to different objective preferences can be learned through a single training session. As a result, agents can execute various policies to satisfy the stakeholders’ needs based on these preferences. Moreover, a sampling adjustment strategy is introduced to improve the uniformity of the final non-dominated solution set and enhance the ability of the agent to explore the objective space. Experiments show that the proposed method can effectively solve the multi-objective problem of cooperative charging within an EVCS. Compared to the baseline method, the proposed method can achieve superior results while simultaneously reducing the computational cost to a significant extent. © 2014 IEEE.

关键词： Deep reinforcement learning

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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A Novel Multi-Points Laser Monitoring of Building Settlement and Its Risk Evaluation

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Wuhan University Journal of Natural Sciences 2019年第5期24卷 442-454页

作者： CHAI Caiping WU Zejian WANG Huifeng GUAN Limin HUANG He MU Kenan CUI Wenfen HAO Ting School of Highway & Railway Engineering Shaanxi College of Communication Technology Xi'an 714018 ShaanxiChina School of Electronic & Control Engineering Chang'an University Xi'an 710064 Shaanxi China

Aiming at the defects of routine settlement measurement methods, such as complicated procedures, time-consuming and labor-intensive, high cost and low measurement accuracy, based on the analysis of existing engineering measurement technical requirements and specifications, a multi-point high precision and high efficiency based on laser reference is proposed. The automatic building settlement real-time monitoring system program gives the principle and system model of single-point settlement observation. The model of multi-point scanning settlement monitoring system and the model of multi-point network settlement monitoring system are designed, and their advantages and disadvantages are analyzed. We focus on the networked multi-point settlement monitoring system for network cumulative error analysis, and propose related evaluation and correction methods. The hardware schematic and software block diagram of the laser reference measurement and measurement system of the single point settlement acquisition system are given. Finally, the risk of subsidence state is quantitatively evaluated based on multi-point settlement monitoring data. The measurement error of this method is less than 300 μm, which can realize the monitoring and evaluation of the overall settlement.

关键词： measurement technology laser reference settlement chain linear array Charge-Coupled Device(CCD)

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Short Interval Solar Power Prediction for Energy Harvesting with Low Computation Cost on Edge Computation Network

Short Interval Solar Power Prediction for Energy Harvesting ...

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IEEE Annual Computing and communication Workshop and Conference (CCWC)

作者： Yue Chen Xing Cheng Yanxin Yao Bei Wan School of Information and Communication Engineering Beijing Information Science and Technology University Beijng China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science & Technology University Advanced Equipment Intelligent Perception and Control Beijing International Cooperation Base for Science and Technology Beijing Information Science & Technology University

ISBN: (数字)9781665414906

ISBN: (纸本)9781665430586

Solar energy forecasting methods including artificial neural network (ANN) are mostly for planning large or medium scale grid connected photovoltaic (PV) systems. The small prediction step will cause expensive computational cost with ANN. Using the concept of edge computing (EC), the paper proposes an effective short time step PV energy predicting model I based on ANN and wavelet transformation to reduce the computation costs. Sparse coefficients, which are tried by both different wavelet types and similar day classification, and a few forecast meteorological data are used as inputs to reduces the scale of ANN sharply. The simulation results demonstrate that, when the proposed method processes the whole year data, the proposed ANN model I has low computation complexity and superior precision.

关键词： Training Photovoltaic systems Computational modeling Artificial neural networks Solar energy Predictive models Wavelet coefficients

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Tailoring the Anomalous Hall Effect in Van Der Waals Ferromagnet Fe3gete2

SSRN

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SSRN 2024年

作者： Wu, Kaile Wang, Qin Wang, Junshan Jiang, Jiaolin Zhao, Tian Zhao, Dong Han, Dan Quan, Zhiyong Ma, Zongmin Wang, Haomin Sang, Shengbo Ge, Yang Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education Taiyuan University of Technology Taiyuan030024 China College of Materials Science and Engineering Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education School of Chemistry and Materials Science Shanxi Normal University Taiyuan030031 China State Key Laboratory of Dynamic Measurement Technology North University of China Shanxi Taiyuan030051 China School of Semiconductor and Physics North University of China Shanxi Taiyuan030051 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China

Two-dimensional van der Waals magnetic materials have shown fascinating natural ferromagnetic properties at the atomic level, which has great potential in fields of low power spintronics, quantum computing and optical communication. Herein, the ferromagnetism and magnetic anisotropy of bulk Fe3GeTe2 (FGT) were investigated, and the testing results indicate a pronounced vertical magnetic anisotropy below the Curie temperature (Tc=165 K) of FGT. In addition, the anomalous Hall effect of FGT nanoflakes was studied at various temperatures and thicknesses by standard Hall bar device in detail. The results show the anomalous Hall resistance (0.764 Ω at 20 K decreased to 0.332 Ω at 140 K) and the obvious rectangular magnetic hysteresis rings below Tc (165 K). Furthermore, we calculated the carrier concentration (2.5×1022 cm-3 at 20K) and mobility (13.3 cm2/(v·s) at 20K) under large magnetic field (-6 T~6 T). Particularly, we demonstrated that the strong tailoring ability of the anomalous Hall effect for FGT-based magnetoelectric transport devices comes form the regulation of magnetic moment by sample thickness and magnetic field angle. The above experimental results verify that FGT is expected to be a prime candidate material for ultrathin 2D spintronic devices to the next generation. © 2024, The Authors. All rights reserved.

关键词： Germanium alloys

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