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2023 3rd International Conference on Electrical engineering and Computer Technology, ICEECT 2023

作者： Gong, Jing Zhong, Siyu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

When using wavelet for voltage interruption detection, improper selection can lead to detection failure due to the differences in wavelet characteristics and the diversity of the generating function. Based on the analysis of wavelet characteristics, the modulus maximum principle is used to compare the detail coefficients after wavelet decomposition, and a method that should be followed for selecting the generating function is proposed. Here are four important points to consider: First, choose orthogonal wavelets that can decrease redundancy. Second, symmetry does not have a major effect. Third, higher-order vanishing moments and longer support lengths result in better singularity detection. Finally, the order of vanishing moments is more important than the support length. We establish a disturbance signal model with interruption occurring at the time of voltage zero crossing and select six types of wavelets: db1, db3, db6, db8, coif2, and coif3 for four-scale wavelet decomposition. The experimental results demonstrate the accuracy of the proposed method in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Wavelet decomposition

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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Generative Zero-shot Learning Compound Fault Diagnosis of Bearings 2

Generative Zero-shot Learning Compound Fault Diagnosis of Be...

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2nd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2021

作者： Xu, Juan Li, Kang Hefei University of Technology School of Computer and Information Key Laboratory of Knowledge Engineering with Big Data Hefei China

ISBN: (纸本)9781665427470

Diagnosis of compound faults remains a challenge during fault diagnosis of bearings, owing to the different fault parameters coupling, fault characteristics diversity, and the exponential increasement of the number of possible failure modes. Current compound faults diagnostic methods, which are usually based on supervised or semi-supervised learning, require sufficient labeled or unlabeled training data for each compound faults. In industrial scenarios, neither labeled nor unlabeled training data of compound faults are usually difficult to collect and sometimes even inaccessible, whereas single faults samples are easy to obtain. Based on these issues, we construct a novel generative zero-shot learning (ZSL) compound faults diagnosis model identifies unseen compound faults using only single faults samples as training set. This model comprises several modules, namely semantic vector definition, feature extractor, generative adversarial modules. Firstly, we devise a unified semantic vector definition method for expressing single and compound faults based on theoretical correlation of characteristics between single fault and compound faults vibration data. Secondly, a CNN-based feature extractor is designed for extraction the fault features from the time-frequency domain of vibration data. Thirdly, a generative adversarial module performs adversarial training of semantic vectors and fault features of single faults to learn the mapping relationship between the fault features and the fault semantic vectors. Once trained, the generator is able to generate compound fault features using the compound fault semantic vectors, rather than any compound fault samples. Finally, the K-nearest neighbor method is adopted to identify the unseen compound faults by measuring the distance between the extracted feature from the testing compound fault samples and the generated features. The effectiveness of the proposed method is verified on a self-built bearing test stand. The results show

关键词： Semantics

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer Science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Resource Allocation for Underwater Acoustic Sensor Networks With Partial Spectrum Sharing: When Optimization Meets Deep Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2715-2730页

作者： Tang, Rui Zhang, Ruizhi Xu, Yongjun Liu, Chuan Huang, Chongwen Yuen, Chau China West Normal University School of Electronic Information Engineering Key Laboratory of Optimization Theory and Applications Internet of Things Perception and Big Data Analysis Key Laboratory of Nanchong City Sichuan Nanchong637002 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400064 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Nanyang Technological University School of Electrical and Electronics Engineering Jurong West 639798 Singapore

To utilize the limited acoustic spectrum while combating the harsh underwater propagation, we incorporate partial spectrum sharing into an underwater acoustic sensor network and aim to maximize the minimum data collection rate among all underwater sensor nodes through joint power allocation and spectrum assignment. To cope with the non-convex optimization problem, we propose a Hybrid Model-based and data-based Resource Allocation (HMDRA) scheme: 1) Under any given spectrum assignment strategy, we analyze the impact of the partial spectrum sharing and imperfect successive interference cancellation on baseband signal processing, and formulate a power allocation problem that is solved by the bisection method and Lagrange dual theory. 2) Based on the optimal power allocation strategy, the gradient-free genetic algorithm (GA) is first adopted to approach the optimal solution of the model-less spectrum assignment problem by nearly enumerating the solution space. To reduce complexity, we further propose a deep reinforcement learning (DRL)-based algorithm and obtain an efficient solution by traversing a deep neural network-based policy learned from the training stage. Simulation results show that compared with the GA-based algorithm, the average execution time of the DRL-based algorithm is substantially reduced by 5 orders of magnitude to 0.7076 seconds at the cost of approximately 6 percent performance loss. © 2004-2012 IEEE.

关键词： Deep reinforcement learning

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Comprehensive recovery of W,V,and Ti from spent selective reduction catalysts

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Rare Metals 2023年第10期42卷 3518-3531页

作者： Li-Wen Ma Xiao-Li Xi Jia-Peng Chen Fan Guo Zi-Jie Yang Zuo-Ren Nie Key Laboratory of Advanced Functional Materials Ministry of EducationFaculty of Materials and ManufacturingBeijing University of TechnologyBeijing100124China Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of TechnologyBeijing100124China National Engineering Laboratory for Industrial Big-Data Application Technology Beijing University of TechnologyBeijing100124China

In this study,spent WO_(3)/V_(2)O_(5)-TiO_(2) catalysts used for selective catalytic reduction were treated by a hydrometallurgical process to comprehensively recover valuable metallic elements,such as W,V,and *** and Si impurities were preferentially removed by selective micro wave-assisted alkali leaching.W and V were leached by enhanced high-pressure leaching with efficiencies estimated at 95% and 81%.The leaching of W and V followed the nuclear shrinkage model controlled by the combination of product layer diffusion and interfacial chemical reaction.A synergistic extraction was applied to separate W and V using an extractant mixture of di-(2-ethylhexyl)phosphoric acid P204 and the primary amine *** extraction efficiencies of V and W reached 86.5% and 6.3%,respectively,with a separation coefficient(V/W) of *** product was precipitated after extraction to yield ammonium paratung state(APT) and NH_(4)VO_(3).The TiO_(2)catalyst carrier residue meets commercial specifications for *** comprehensive recovery process with the characteristics of high-pressure leaching and synergistic extraction realizes the resourceful utilization of the spent catalysts.

关键词： Spent selective catalytic reduction(SCR)catalysts Microwaveleaching Pressure leaching Synergistic extraction Comprehensive recovery

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Efficient Homomorphic Approximation of Max Pooling for Privacy-Preserving Deep Learning 6th

Efficient Homomorphic Approximation of Max Pooling for Pri...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Zhang, Peng Qiu, Dongyan Duan, Ao Liu, Hongwei The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen518118 China

ISBN: (纸本)9789819645657

Privacy-Preserving Deep Learning (PPDL) using Fully Homomorphic Encryption (FHE) addresses potential data privacy exposure risks associated with deploying deep learning models in untrusted cloud environments. FHE-based PPDL enables users to encrypt their data locally, allowing cloud service providers to perform computations directly on the encrypted data without ever accessing it. However, FHE faces challenges in efficiently handling nonlinear computations, which are essential to deep learning frameworks, particularly in implementing activation functions and max pooling layers. To address this challenge, we improve the homomorphic max pooling approximation scheme HMaxPool, and propose an efficient homomorphic max pooling algorithm, denoted as HMaxPool+. Specifically, we first redefine the approximation of the maximum function, and then design sub-algorithm to address the potential output expansion issue. The experimental results validate that both the homomorphic max pooling algorithm itself and the corresponding homomorphic SqueezeNet neural network demonstrate enhancements in computational efficiency and accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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A Hybrid Max-Min Ant System Algorithm for Electric Capacitated Vehicle Routing Problem

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第3期51卷 195-203页

作者： Hou, Yan-e Wang, Congran Zhang, Chunyang Dang, Lanxue Xiao, Chunjing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

Electric vehicles have become the first choice for products delivery in logistics, due to their environmental friendliness and low cost compared to the traditional fuel vehicles. This paper deals with an electric capacitated vehicle routing problem with variable energy consumption rate (E-CVRP), which aims to minimize the total cost including vehicle fixed cost, traveled distance cost and energy cost. First, a mixed integer linear programming model is constructed based on the fact that the energy consumption is affected by the vehicle load. Second, a hybrid Max-Min Ant System (HMMAS) algorithm is proposed to solve this problem. Three neighborhood structures are introduced and executed in a Variable Neighborhood Descent (VND) algorithm to improve the current solution. In addition, an effective charging station adjustment strategy is adopted to further improve the solution obtained by the local search process. Finally, some experiments have been conducted on public instances, and the results demonstrate the effectiveness and stability of the algorithm. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Energy utilization

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Face micro-expression recognition algorithm based on ResNet depth model

Face micro-expression recognition algorithm based on ResNet ...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Liquan Wang Shu Zhan School of Computer and Information Engineering Hefei University of Technology Key Laboratory of Big Data Knowledge Engineering Ministry of Education Hefei China

Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small number of samples and uneven distribution of different categories, we propose a model framework to improve the accuracy of micro-expression recognition. The peak frames containing more key expression information in the micro-expression video sequences are extracted; SE-ResNeXt-50, an improved residual network with SE module, is used to extract features from the peak frames of micro-expressions, where the SE module can better learn the key information in the features, and ResNeXt simplifies the structure by replacing the dense structure with the sparse structure through group convolution, which improves the recognition efficiency. The recognition efficiency is improved by replacing the dense structure with the sparse structure by group convolution. At the same time, the Focal Loss loss function can better solve the model performance problem caused by the imbalance of micro-expression data. Simulation experiments are conducted on the micro-expression dataset CASMEⅡ, and it is found that the improved residual network and peak frame improve the accuracy and F1 value of micro-expression recognition. The improved residual network and peak frame can reduce the effect of small data set, make the model have good fitting effect, and improve the performance of different categories, improve the recognition accuracy of micro-expressions, and have better recognition performance for micro-expression recognition.

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