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IoT Empowered Early Warning of Transmission Line Galloping Based on Integrated Optical Fiber Sensing and Weather Forecast Time Series Data

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Computers, Materials & Continua 2025年第1期82卷 1171-1192页

作者： Zhe Li Yun Liang Jinyu Wang Yang Gao State Grid Henan Electric Power Company Electric Power scientific research Institute State Grid Henan Electric Power CompanyZhengzhou450052China

Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power *** early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false *** address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather ***,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused ***,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network ***,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this ***,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.

关键词： Optical fiber sensing multi-source data fusion early warning of galloping time series data IoT adaptive weighted learning irregular time series perception closed-loop attention mechanism

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Market Drivers in India’s Smart Grid:Responsibilities and Roles of Stakeholders

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Energy Engineering 2025年第1期122卷 101-128页

作者： Abhay Sanatan Satapathy Suresh Kumar Sahoo Asit Mohanty Yasser Fouad Manzoore ElahiMohammad Soudagar Erdem Cuce Faculty of Management Studies Sri Sri UniversityCuttack754006India Institute of Power Engineering National Energy UniversityJalan Ikram-UnitenKajangSelangor43000Malaysia centre of research Impact and Outcome Chitkara UniversityRajpura140417PunjabIndia Department of applied Mechanical Engineering College of Applied EngineeringMuzahimiyah BranchKing Saud UniversityRiyadh11421Saudi Arabia College of Engineering Lishui UniversityLishui323000China Department of Mechanical Engineering Graphic Era(Deemed to be University)Dehradun248002UttarakhandIndia Department of Mechanical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia Department of Mechanical Engineering Faculty of Engineering and ArchitectureRecep Tayyip Erdogan UniversityZihni Derin CampusRize53100Turkey Center for research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia University centre for research and Development Chandigarh UniversityMohali140413PunjabIndia

The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder *** study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and *** activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic *** firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy *** companies offer the essential hardware and software solutions,which stimulate creativity and enhance *** actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric *** study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.

关键词： Smart grid stakeholders smart grid technology market drivers

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A quantum-search-based multi-classifier for image recognition

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 21-31页

作者： Lu Liu Xingyu Wu Lufan Zhang Chuan Wang School of Artificial Intelligence Beijing Normal University applied Optics Beijing Area Major Laboratory Beijing Normal University

The multi-class classification of images is a pivotal challenge within the realm of image processing. As the volume of visual data continues to expand, there is a burgeoning interest in harnessing the unique capabilities of quantum computation to augment the efficiency of classification tasks. However, many existing methods for training quantum image multi-classifiers parallel classical machine learning techniques, where the requisite circuit measurements increase linearly with the volume of training data. This work introduces a novel approach for training a quantum image multi-classifier based on the quantum search algorithm. We have meticulously conducted rigorous experiments on a handwritten digit dataset, a classic benchmark in the field. The results have been meticulously compared with previous works, and the comparative analysis not only validates the efficiency of our proposed approach, requiring only O(N/b) measurements during training, but also highlights a significant quadratic speedup of the algorithm.

关键词： quantum computation image recognition quantum search algorithm

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Adaptive-basis decomposition-based low-rank network for efficient non-uniform motion deblurring

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Optoelectronics Letters 2025年第1期21卷 43-50页

作者： CHEN Lei XIONG Qingbo ZHANG Wei LI Runde School of Software Henan University School of applied Technology China University of Labor Relations

In this study, we present a unified sparsity-driven framework that significantly enhances motion deblurring performance by integrating two key components: a custom-designed dataset and a low-rank module(LRM). This framework leverages the inherent sparsity of per-pixel blur kernels to bolster both deblurring accuracy and model interpretability. Firstly, we propose an adaptive-basis decomposition-based deblurring(ADD) approach, which constructs a tailored training dataset to enhance the generalization capacity of the deblurring network. The ADD framework adaptively decomposes motion blur into sparse basis elements, effectively addressing the intricacies associated with non-uniform blurs. Secondly, an LRM is proposed to improve the interpretability of deblurring models as a plug-and-play module, primarily designed to identify and harness the intrinsic sparse features in sharp images. A series of ablation studies have been conducted to substantiate the synergistic advantages of combining the proposed ADD with the LRM for overall improvement in deblurring efficacy. Subsequently, we empirically demonstrate through rigorous experimentation that incorporating the LRM into an existing Uformer network leads to substantial enhancement in reconstruction performance. This integration yields a sparsity-guided low-rank network(SGLRN). Operating under the overarching principle of sparsity, SGLRN consistently outperforms state-of-the-art methods across multiple standard deblurring benchmarks. Comprehensive experimental results, assessed through quantitative metrics and qualitative visual evaluations, provide compelling evidence of its effectiveness. The overall deblurring results are available at Google Drive.

关键词： Image enhancement

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Electrochemical conversion of methane to bridge the gap in the artificial carbon cycle

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Journal of Energy Chemistry 2025年第1期100卷 286-308页

作者： Yuhao Peng Yuefeng Song Ihar Razanau Juanxiu Xiao Wei Xiao Di Hu Guoxiong Wang College of Chemistry and Molecular Sciences Hubei Key Laboratory of Electrochemical Power Sources Wuhan University State Key Laboratory of Catalysis Dalian National Laboratory for Clean Energy iChEM (Collaborative Innovation Center of Chemistry for Energy Materials) Dalian Institute of Chemical Physics Chinese Academy of Sciences Laboratory of Physical-Chemical Technologies SSPA Scientific and Practical Materials Research Centre of NAS of Belarus State Key Laboratory of Marine Resources Utilization in South China Sea Collaborative Innovation Center of Marine Science and Technology School of Marine Science and Engineering Hainan University Department of Chemical and Environmental Engineering University of Nottingham Ningbo China

Methane, an abundant one-carbon（C1） resource, is extensively used in the industrial production of vital fuels and value-added chemicals. However, current industrial methane conversion technologies are energy-and carbon-intensive, mainly due to the high activation energy required to break the inert C–H bond, low selectivity, and problematic side reactions, including CO2emissions and coke deposition. Electrochemical conversion of methane（ECM） using intermittent renewable energy offers an attractive solution, due to its modular reactor design and operational flexibility across a broad spectrum of temperatures and pressures. This review emphasizes conversion pathways of methane in various reaction systems, highlighting the significance and advantages of ECM in facilitating a sustainable artificial carbon cycle. This work provides a comprehensive overview of conventional methane activation mechanisms and delineates the complete pathways of methane conversion in electrolysis contexts. Based on surface/interface chemistry, this work systematically analyzes proposed reaction pathways and corresponding strategies to enhance ECM efficiency towards various target products, including syngas, hydrocarbons, oxygenates, and advanced carbon materials. The discussion also encompasses opportunities and challenges for the ECM process, including insights into ECM pathways, rational electrocatalyst design, establishment of benchmarking protocols, electrolyte engineering, enhancement of CH4conversion rates, and minimization of CO2emission.

关键词： Activation energy

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Multi boron-doping effects in hard carbon toward enhanced sodium ion storage

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Journal of Energy Chemistry 2025年第1期100卷 730-738页

作者： Peng Zheng Wang Zhou Ying Mo Biao Zheng Miaomiao Han Qin Zhong Wenwen Yang Peng Gao Lezhi Yang Jilei Liu College of Materials Science and Engineering Hunan Joint International Laboratory of Advanced Materials and Technology for Clean Energy Hunan Province Key Laboratory for Advanced Carbon Materials and Applied Technology Hunan University Greater Bay Area Institute for Innovation Hunan University Changsha research Institute of Mining & Metallurgy Co. LTD.

Hard carbon （HC） has been considered as promising anode material for sodium-ion batteries （SIBs）.The optimization of hard carbon’s microstructure and solid electrolyte interface （SEI） property are demonstrated effective in enhancing the Na+storage capability,however,a one-step regulation strategy to achieve simultaneous multi-scale structures optimization is highly ***,we have systematically investigated the effects of boron doping on hard carbon’s microstructure and interface chemistry.A variety of structure characterizations show that appropriate amount of boron doping can increase the size of closed pores via rearrangement of carbon layers with improved graphitization degree,which provides more Na+storage ***-situ Fourier transform infrared spectroscopy/electrochemical impedance spectroscopy （FTIR/EIS） and X-ray photoelectron spectroscopy （XPS） analysis demonstrate the presence of more BC3and less B–C–O structures that result in enhanced ion diffusion kinetics and the formation of inorganic rich and robust SEI,which leads to facilitated charge transfer and excellent rate *** a result,the hard carbon anode with optimized boron doping content exhibits enhanced rate and cycling *** general,this work unravels the critical role of boron doping in optimizing the pore structure,interface chemistry and diffusion kinetics of hard carbon,which enables rational design of sodium-ion battery anode with enhanced Na+storage performance.

关键词： X ray photoelectron spectroscopy

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PREDICTION OF LOADING DISTRIBUTION AND HYDRODYNAMIC MEASUREMENTS FOR PROPELLER BLADES IN A RIM DRIVEN THRU-STER

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Journal of Hydrodynamics 2012年第1期24卷 50-57页

作者： CAO Qing-ming HONG Fang-wen TANG Deng-hai HU Fang-lin LU Lin-zhang China Ship scientific research centre Wuxi 214082China

This article presents an approach which employs a commercial Reynolds-Averaged Navier-Stokes（RANS）solver to predict the steady wake field and loading distributions for a rim driven *** different cases of propeller blades are chosen to be calculated with the presented *** propeller blade radial circulation and chordwise circulation density distributions are *** maximum radial circulation is found at the blade tip,which is different from conventional shaft driven *** numerical results indicate that there is no tip leakage vortex in rim driven *** there exist the tip joint vortex and the root region *** characteristics are calculated by taking rim surface effect into *** the predicted results the second case in this paper is selected as the final one to perform hydrodynamic *** calculation results with empirical rim surface corrections are compared with the *** shows that the developed numerical method can well predict hydrodynamic performances of the rim driven thruster.

关键词： rim driven thruster Reynolds-Averaged Navier-Stokes(RANS)solver wake field radial circulation chordwise circulation density hydrodynamic experiment

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ON THE THEORETICAL PREDICTION OF CAVITATION INCEPTION

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Journal of Hydrodynamics 1992年第1期4卷 74-82页

作者： Pan, Sensen Xu, Weixin Peng, Xiaoxing Xu, Jie China Ship scientific research Centre Wuxi China

This paper presents a new prediction method called 'fuzzy prediction method', which is different from traditional bubble dynamics method and pressure parameters method. The idea and method of artificial intelligence technology are used to predict the cavitation inception. New prediction model consists of two predicting ways (analytic and empirical way) and one scale-effect gate. New prediction results are compared with the experiments of ITTC body, NSRDC body, Schiebe body, etc.. The results compared indicates that new prediction method is satisfactory and may be a very powerful tool to predict the cavitation.

关键词： Mathematical Techniques Fuzzy Sets

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A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data

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Big Earth Data 2023年第2期7卷 231-250页

作者： Stelios Karozis Iraklis A.Klampanos Athanasios Sfetsos Diamando Vlachogiannis Environmental research Laboratory(EREL) INRaSTESNational Centre for Scientific Research“Demokritos”AthensGreece Institute of Informatics and Telecommunications National Centre for Scientific Research“Demokritos”AthensGreece

Numerical Weather Prediction(NWP)simulations produce meteorological data in various spatial and temporal scales,depending on the application *** the current study,a deep learning approach,based on convolutional autoencoders,is explored to effectively correct the error of the NWP *** undercomplete convolutional autoencoder(CAE)is applied as part of the dynamic error correction of NWP *** work is an attempt to improve the seasonal forecast(3-6 months ahead)data accuracy for Greece using a global reanalysis dataset(that incorporates observations,satellite imaging,etc.)of higher spatial *** specifically,the publically available Meteo France Seasonal(Copernicus platform)and the National Centers for Environmental Prediction(NCEP)Final Analysis(FNL)(NOAA)datasets are *** addition,external information is used as evidence transfer,concerning the time conditions(month,day,and season)and the simulation characteristics(initialization of simulation).It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead ***,the month evidence yields the best agreement indicating a seasonal dependence of the performance.

关键词： Seasonal weather prediction neural networks convolutional autoencoder evidence transfer

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research progress in metal-organic frameworks and their derivatives in electrochemistry

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Science China Chemistry 2025年

作者： Lingling Chen Yuxin Liu Yichun Su Wenchen Zhu Guangyu Du Huan Pang School of Chemistry and Chemical Engineering Yangzhou University Department of applied Physics The Hong Kong Polytechnic University Interdisciplinary Materials research Center Institute for Advanced Study Chengdu University State Key Laboratory of Coordination Chemistry Nanjing University

Metal-organic frameworks (MOFs) are distinguished by their unique porosity and meticulously ordered framework structures,setting them apart from conventional complexes. These materials are defined by their highly organized crystalline and pore structures, extensive surface areas, robust porosity, and superior adsorption capabilities. Owing to their exceptional structural and functional attributes, MOFs and their derivatives are leveraged across a broad spectrum of applications, demonstrating significant potential across diverse sectors. In recent times, the scientific community has increasingly focused on MOFs, underscoring their growing importance in research. Despite the promising array of applications for MOFs and their derivatives,challenges persist in practical deployments, and the pathway to research advancement remains daunting. This document delves into the synthesis and various applications of MOFs and their derivatives, particularly in the domains of energy storage, catalysis,sensing, and water treatment. It also highlights the ongoing research progress in the field of electrochemistry, including developments in lithium ion batteries (LIBs), lithium-sulfur batteries (LSBs), zinc-ion batteries (ZIBs), aqueous nickel-zinc batteries (NZBs), and supercapacitors. Finally, it provides an outlook on the future prospects and potential hurdles facing the development of MOF materials.

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