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Evaluating transient behaviour of large-scale photovoltaic systems during lightning events using enhanced finite difference time domain method with variable cell size approach

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High Voltage 2024年第3期9卷 636-647页

作者： Ibrahim Hetita Diaa-Eldin A.Mansour Yang Han Ping Yang Congling Wang Mohamed M.F.Darwish Matti Lehtonen Amr S.Zalhaf School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China(UESTC)ChengduChina Electrical Power and Machines Engineering Department Faculty of EngineeringHelwan UniversityCairoEgypt Electrical Power and Machines Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Department of Electrical Power Engineering Faculty of EngineeringEgypt-Japan University of Science and TechnologyAlexandriaEgypt Department of Electrical Engineering Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Electrical Engineering and Automation School of Electrical EngineeringAalto UniversityEspooFinland

Photovoltaic(PV)arrays are usually installed in open areas;hence,they are vulnerable to lightning strikes that can result in cell degradation,complete damage,service disruption,and increased maintenance *** a result,it is imperative to develop an effective and efficient lightning protection system by evaluating the transient behaviour of PV arrays during lightning *** aim is to evaluate the transient analysis of large-scale PV systems when subjected to lightning strikes using the finite difference time domain(FDTD)*** overvoltages are calculated at various points within the mounting *** optimise the FDTD method's execution time and make it more suitable for less powerful hardware,a variable cell size approach is ***,larger cell dimensions are used in the earthing system and smaller cell dimensions are used in the mounting *** FDTD method is utilised to calculate the temporal variation of transient overvoltages for large-scale PV systems under different scenarios,including variations in the striking point,soil resistivity,and the presence of a metal *** results indicate that the highest transient overvoltages occur at the striking point,and these values increase with the presence of a PV metal frame as well as with higher soil ***,a comparison is performed between the overvoltage results obtained from the FDTD approach and the partial element equivalent circuit(PEEC)method at the four corner points of the mounting systems to demonstrate the superior accuracy of the FDTD ***,a laboratory experiment is conducted on a small-scale PV system to validate the simulation *** calculated overvoltages obtained from the FDTD and PEEC methods are compared with the measured values,yielding a mean absolute error of 5%and 11%for the FDTD and PEEC methods,respectively,thereby confirming the accuracy of the FDTD simulation model.

关键词： transient behaviour finite

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Create a crowd emotion detection framework with ecological validity

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IEEE Transactions on Affective Computing 2024年第2期16卷 1087-1103页

作者： Chen, Xiao Liu, Zhen Liu, Tingting Xiao, Jiangjian The Faculty of Electrical Engineering and Computer Science Ningbo University China

Ecological validity remains essential for generalizing scientific research into real-world applications. However, current methods for crowd emotion detection lack ecological validity due to limited diversity samples in datasets. This paper proposes a crowd emotion detection framework that improves the ecological validity of models from both dataset and methodological perspectives. Firstly, we develop an Emotional Crowd Generator script within Grand Theft Auto V to generate a large-scale and diverse synthetic emotional crowd dataset, named Emotional-GTA (E-GTA). Secondly, we utilize prior features to enhance the model's generalization ability, especially for rare samples. Building on this, we introduce the dual-driven Graph-based Prior feature and Image Fusion Network (GPIFN), which further strengthens our model's ecological validity from a methodological perspective. We propose a graphical representation that effectively constructs the Crowd Image Graph (CIG) and the Crowd Prior Features Graph (CPG), which represent crowds from the perspectives of image and prior features, respectively. We then design a dual-stream network GPIFN that extracts image features from the CIG and prior features from the CPG. Additionally, we design an Image and Prior features Fusion Module (IPFM) that efficiently merges image and prior features while preserving the original stream features. Our experiments demonstrate that both E-GTA and GPIFN greatly enhance ecological validity in real-world scenarios. Our framework achieves state-of-the-art results on real-world datasets: UMN and Violent-Flows. © 2010-2012 IEEE.

关键词： Emotion Recognition

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Statistical Process Monitoring Based on Ensemble Structure Analysis

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1889-1891页

作者： Likang Shi Chudong Tong Ting Lan Xuhua Shi Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Dear Editor,This letter presents a novel process monitoring model based on ensemble structure analysis(ESA).The ESA model takes advantage of principal component analysis(PCA),locality preserving projections(LPP),and multi-manifold projections(MMP)models,and then combines the multiple solutions within an ensemble result through Bayesian *** the developed ESA model,different structure features of the given dataset are taken into account simultaneously,the suitability and reliability of the ESA-based monitoring model are then illustrated through ***:The requirement for ensuring safe operation and improving process efficiency has led to increased research activity in the field of process monitoring.

关键词： ensemble preserving letter

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Vortex analysis of water flow through gates by different vortex identification methods

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Journal of Hydrodynamics 2023年第1期35卷 112-124页

作者： Chun-ying Shen Rui-guo Yang Shan Qing Shi-hua He Faculty of Electrical Power Engineering Kunming University of Science and TechnologyKunming650500China

The pre-gate suction vortex,gate-bottom-edge transverse vortex,gate-slot vertical vortex,and downstream-of-gate return vortex are important factors affecting the flow instability of flat gates,which may lead to fatigue failure in severe *** study used the volume of fluid(VOF)model and large eddy simulation(LES)method to accurately capture the transient turbulence characteristics of flow under different water flow conditions and reveal the flow field and vortex *** Q—criterion,Omega(Ω)method,and latest third-generation Liutex vortex identification method were used to analyze and compare the pre-gate suction vortex,gate-slot vertical vortex,and downstream-of-gate return vortex,focusing on the ability of each vortex identification method to capture the flow field information and vortex *** results reveal that theΩmethod and Liutex method are less dependent on the threshold value,and the Liutex method captures a wide range of pre-gate *** flow conditions cause changes in the vortex structure of over-gate *** the relative opening of the gate is smaller,the intensity of the vortices in the flow field around the gate is greater,the return vortices downstream of the gate are more disordered,and the vortex changes are more violent,which in turn affects the efficient and stable operation of the gate.

关键词： Vortex features vortex identification methods Liutex Ωmethod Q-criterion large eddy simulation(LES)

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A Semi-Tensor Product Based All Solutions Boolean Satisfiability Solver

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Journal of computer science & Technology 2023年第3期38卷 702-713页

作者：潘鸿洋储飞 Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Boolean satisfiability (SAT) is widely used as a solver engine in electronic design automation (EDA). Typically, SAT is used to determine whether one or more groups of variables can be combined to form a true formula. All solutions SAT (AllSAT) is a variant of the SAT problem. In the fields of formal verification and pattern generation, AllSAT is particularly useful because it efficiently enumerates all possible solutions. In this paper, a semi-tensor product (STP) based AllSAT solver is proposed. The solver can solve instances described in both the conjunctive normal form (CNF) and circuit form. The implementation of our method differs from incremental enumeration because we do not add blocking conditions for existing solutions, but rather compute the matrices to obtain all the solutions in one pass. Additionally, the logical matrices support a variety of logic operations. Results from experiments with MCNC benchmarks using CNF-based and circuit-based forms show that our method can accelerate CPU time by 8.1x (238x maximum) and 19.9x (72x maximum), respectively.

关键词： all solutions Boolean satisfiability(AllSAT) semi-tensor product of matrices conjunctive normal form(CNF)satisfiability circuit satisfiability

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A new technique for fault diagnosis in transformer insulating oil based on infrared spectroscopy measurements

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High Voltage 2024年第2期9卷 319-335页

作者： Mohamed M.F.Darwish Mohamed H.A.Hassan Nagat M.K.Abdel-Gawad Matti Lehtonen Diaa-Eldin A.Mansour Department of Electrical Engineering Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Electrical Engineering and Automation School of Electrical EngineeringAalto UniversityEspooFinland Department of Electrical Power Engineering Egypt-Japan University of Science and Technology(E-JUST)New Borg El-Arab CityEgypt Department of Electrical Power and Machines Engineering Faculty of EngineeringTanta UniversityTantaEgypt

Condition monitoring of the insulating system within power transformers has a massive importance according to the electrical *** gas analysis(DGA)is frequently used for this ***,DGA lacks the necessary level of accuracy to identify all equipment faults,particularly in their initial stages of ***,it does not have the capability for real-time monitoring and relies on manual sampling and laboratory testing,causing potential delays in fault ***,the interpretation of DGA data necessitates specialised expertise,which may pose difficulties for smaller entities that have limited access to ***,the contribution of this research is to use infrared spectroscopy measurements as a new effective technique substituting the DGA method for fault diagnosis in insulating *** inception faults that were considered in this study were the electrical fault(discharges of high energy)and the thermal fault(300°C

关键词： fault diagnosis spectroscopy

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S+E+O-band luminescence and enhancement in Nd3+/Tm3+/Ce3+ doped tellurite glass

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Journal of the Optical Society of America B: Optical Physics 2024年第6期41卷 1432-1442页

作者： Zhou, Dexing Wang, Shuhong Zhao, Dongyi Zhu, Liqiao Zhou, Yaxun Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China

A new, to our knowledge, doped combination of Nd3+, Tm3+, and Ce3+ ions was developed in tellurite glass with a fundamental composition of TeO2-ZnO-WO3-Bi2O3, and the structural, thermal, and especially near-infrared (NIR) luminescent properties in O-, E-, and S-bands were explored. The XRD pattern confirmed the amorphous nature of synthesized tellurite glass, the Raman spectrum disclosed different structural units that make up the glass network, the DSC curve confirmed good host thermal stability, while XPS measurement revealed that cerium mainly exists in the trivalent form in a glass network. Upon being pumped with an 808 nm laser, a 1.34 µm band fluorescence from Nd3+ : 4F3/2 level → 4I13/2 level transition and a 1.46 µm band fluorescence from Tm3+ : 3H4 level → 3F4 level transition were observed, which in turn formed a wide luminescence band from 1260 to 1560 nm in the tellurite glass containing both Nd3+ and Tm3+ ions owing to their spectral overlapping. The measured full width at half maximum (FWHM) for the aforementioned wideband luminescence was about 203 nm with Nd2O3 and Tm2O3 concentrations of 0.015 mol. % and 0.5 mol. %, respectively, and exhibited a further ∼60% increase in peak intensity after the incorporation of 0.4 mol. % amount of CeO2, which arises from the phenomena of cross relaxation and energy transfer among the three trivalent rare earth ions. The experimental evidence presented in this work shows that tellurite glass containing Nd3+, Tm3+, and Ce3+ has potential as an active material for S-, E-, and O-band wideband luminescence. © 2024 Optica Publishing Group.

关键词： II-VI semiconductors

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Microwave Through Wall Imaging via An Induced Current Learning Method

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Journal of Beijing Institute of Technology 2024年第3期33卷 248-254页

作者： Pengjin Lan Jianguo Huang Tianyi Zhou Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

In this paper,an induced current learning method(ICLM)for microwave through wall imaging(TWI),named as TWI-ICLM,is *** the inversion of induced current,the unknown object along with the enclosed walls are treated as a combination of ***,a non-iterative method called distorted-Born backpropagation(DB-BP)is utilized to generate the initial *** the training stage,several convolutional neural networks(CNNs)are cascaded to improve the estimated induced *** addition,a hybrid loss function consisting of the induced current error and the permittivity error is used to optimize the network ***,the relative permittivity images are conducted analytically using the predicted current based on *** the numerical and experimental TWI tests prove that,the proposed method can achieve better imaging accuracy compared to traditional distorted-Born iterative method(DBIM).

关键词： through wall imaging inverse scattering problem convolutional neural network(CNN)

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Image classification of lotus in Nong Han Chaloem Phrakiat Lotus Park using convolutional neural networks

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Artificial Intelligence in Agriculture 2024年第1期11卷 23-33页

作者： Thanawat Phattaraworamet Sawinee Sangsuriyun Phoempol Kutchomsri Susama Chokphoemphun Department of Electrical and Computer Engineering Faculty of Science and EngineeringKasetsart University Chalermphrakiat Sakonnakhon Province Campus SakonnakhonThailand Department of Computer and Information Science Faculty of Science and EngineeringKasetsart University Chalermphrakiat Sakonnakhon Province Campus SakonnakhonThailand

The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus ***,as a training area,it lacks appeal and learning motivation due to its conventional presentation of information regarding lotus *** current study introduced the concept of smart learning in this setting to increase interest and motivation for *** neural networks(CNNs)were used for the classification of lotus plant species,for use in the development of a mobile application to display details about each *** scope of the study was to classify 11 species of lotus plants using the proposed CNN model based on different techniques(augmentation,dropout,and L2)and hyper parameters(dropout and epoch number).The expected outcome was to obtain a high-performance CNN model with reduced total parameters compared to using three different pre-trained CNN models(Inception V3,VGG16,and VGG19)as *** performance of the model was presented in terms of accuracy,F1-score,precision,and recall *** results showed that the CNN model with the augmentation,dropout,and L2 techniques at a dropout value of 0.4 and an epoch number of 30 provided the highest testing accuracy of *** best proposed model was more accurate than the pre-trained CNN models,especially compared to Inception *** addition,the number of total parameters was reduced by approximately 1.80–2.19 *** findings demonstrated that the proposed model with a small number of total parameters had a satisfactory degree of classification accuracy.

关键词： Lotus Park Classification Smart learning park Convolutional neural network

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