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Influence analysis of calculated horizontally layered soil parameters on grounding parameters

High Voltage 2023年第2期8卷 421-430页

作者： Yihua Dan Junjun Yin Jian Yang Huizhang Yang Department of Electronic Engineering Tsinghua UniversityBeijingChina School of Computer&Communication Engineering University of Science and Technology BeijingBeijingChina

Calculated parameters(soil layer resistivity,soil layer thickness,and the number of soil layers)of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement distances,which are significant for the design,operation,and maintenance of grounding *** existing calculation methods of soil parameters are just trying to make the calculation results approach the measurement data,ignoring the relationship among measurement data,the calculated soil parameters,and grounding parameters,which would increase the workload of the *** better balance the distance range of the measurement data and the influence of the calculated horizontally layered soil on grounding parameters,this paper systematically studies the relationship among measured apparent soil resistivity,calculated horizontally layered soil parameters,and grounding *** basic theories of apparent resistivity measurement,soil parameter calculation,and grounding parameter calculation are given,the influence of soil layer thickness on the measured apparent resistivity is studied,and the influence of the calculated soil parameters on the grounding resistance of different grounding models is *** on different scales of grounding grids,the results give a corresponding reference distance range of measured apparent soil ***,this paper can help decrease the workload of soil resistivity measurement during grounding parameters analysis,which has far-reaching engineering significance.

关键词： soil parameters apparent

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Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

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computers, Materials & Continua 2024年第8期80卷 1809-1829页

作者： Yu Zhou Yun Zhang Guowei Li Hang Yang Wei Zhang Ting Lyu Yueqiang Xu North China Institute of Computing Technology Beijing Beijing100080China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often *** is frequently assumed that vehicles can be accurately modeled during actual motion ***,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network *** into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading *** optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming *** to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and ***,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization *** results show that the algorithm proposed in this paper is able to achieve lower latency task computation ***,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

关键词： Component vehicular dynamic task offloading resource scheduling

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A Survey of Knowledge Graph Construction Using Machine Learning

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computer Modeling in engineering & sciences 2024年第4期139卷 225-257页

作者： Zhigang Zhao Xiong Luo Maojian Chen Ling Ma School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured *** framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity *** this transformative process,the advancement of artificial intelligence and intelligent information services is ***,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial *** article embarks on a comprehensive journey through the last strides in the field of KG via machine *** a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge ***,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link ***,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

关键词： Knowledge graph(KG) semantic network relation extraction entity linking knowledge reasoning

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Cryptanalysis in e-commerce: A novel arrangement procedure using DHEC-SAT upon elliptic curve cryptography

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Multimedia Tools and Applications 2025年第8期84卷 5377-5395页

作者： Fauziyah Wang, Zhaoshun School of Computer and Communication Engineering University of Science and Technology Beijing 30 Xueyuan Road Haidian District Beijing China

Cryptanalytic innovations are considered the most economical and useful method for guaranteeing information security across shared networks. Despite the tinier key magnitude of Elliptic Curve Cryptography (ECC) compared to common cryptanalysis schemes, the majority of scholars have employed cryptanalysis methods to deliver data reliability and verification. Besides, the computation attempt needed by Diffie Hellman-Elliptic Curve (DH-EC) for equal key dimensions is similar and delivers extra security advantages. The research offers a novel key arrangement procedure using DH-EC in the secure automatic transactions (SAT) setting (DHEC-SAT) to enhance data safety. The DHEC-SAT procedure effectively applied the keys in the SAT setting for online payment and was employed to decode and encode the validation data. The DHEC-SAT procedure is implemented for an entry of e-commerce payments to deliver great-degree security examination, fast verification, and the safety of the suggested Cryptanalytic scheme. The suggested DHEC-SAT generates highly secure systems regarding data size (168 bytes), computational period (4.34 ms), and communication costs (1271 bits), as contrasted to extant ECC procedures and PIN-enabled key arrangements. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic commerce

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OCRBench: on the hidden mystery of OCR in large multimodal models

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science China(Information sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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Defending Federated Learning System from Poisoning Attacks via Efficient Unlearning

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computers, Materials & Continua 2025年第4期83卷 239-258页

作者： Long Cai Ke Gu Jiaqi Lei School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China

Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed ***,the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential *** attacks turn into a major menace to federated learning on account of their concealed property and potent destructive *** altering the local model during routine machine learning training,attackers can easily contaminate the global *** detection and aggregation solutions mitigate certain threats,but they are still insufficient to completely eliminate the influence generated by ***,federated unlearning that can remove unreliable models while maintaining the accuracy of the global model has become a *** some existing federated unlearning approaches are rather difficult to be applied in large neural network models because of their high computational ***,we propose SlideFU,an efficient anti-poisoning attack federated unlearning *** primary concept of SlideFU is to employ sliding window to construct the training process,where all operations are confined within the *** design a malicious detection scheme based on principal component analysis(PCA),which calculates the trust factors between compressed models in a low-cost way to eliminate unreliable *** confirming that the global model is under attack,the system activates the federated unlearning process,calibrates the gradients based on the updated direction of the calibration *** on two public datasets demonstrate that our scheme can recover a robust model with extremely high efficiency.

关键词： Federated learning malicious client detection model recovery machine unlearning

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Faster split-based feedback network for image super-resolution

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High technology Letters 2024年第2期30卷 117-127页

作者：田澍 ZHOU Hongyang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083P.R.China

Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep *** work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation ***,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback ***,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution ***,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer *** experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art ***,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.

关键词： super-resolution(SR) split-based feedback contrastive learning

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A blockchain-based privacy-preserving and collusion-resistant scheme(PPCR)for double auctions

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Digital communications and Networks 2025年第1期11卷 116-125页

作者： Xuedan Jia Liangmin Wang Ke Cheng Pujie Jing Xiangmei Song School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China School of Cyber Science and Engineering Southeast UniversityNanjing 211102China School of Computer Science and Technology Xidian UniversityXi’an 710071China

Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general ***,most e-auction schemes involve a trusted auctioneer,which is not always credible in *** studies have applied cryptography tools to solve this problem by distributing trust,but they ignore the existence of *** this paper,a blockchain-based Privacy-Preserving and Collusion-Resistant scheme(PPCR)for double auctions is proposed by employing both cryptography and blockchain technology,which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy.A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance.A Dispute Resolution agreement(DR)is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and *** addition,a Concise Dispute Resolution protocol(CDR)is designed to handle situations where the number of accused winners is small,significantly reducing the computation cost of dispute *** experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.

关键词： Privacy protection Collusion resistance Secure protocol Blockchain-based double auction Dispute resolution

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Multi-label text classification of cardiovascular drug attributes based on BERT and BiGRU

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10683-10693页

作者： Cui, Hongzhen Zhang, Longhao Zhu, Xiaoyue Guo, Xiuping Peng, Yunfeng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China

Extracting and digitizing drug attributes from medical literature is the first step to build a knowledge computing system for precision disease treatment. In order to build a cardiovascular drug knowledge base, this paper proposes a multilabel text classification method for cardiovascular drug attributes from the Chinese drug guideline. The drug attributes are characterized by a BERT pre-trained model, and a dual-feature extraction structure is proposed based on the BiGRU neural network to capture high-level semantic information. Label categorization of cardiovascular drug attributes, such as indications and mode of administration, is accomplished. The F1 score of 0.8431 was obtained using 5-fold cross-validation. Comparing KNN and Naïve bayes, and conducting CNN and BiGRU control experiments on the basis of Word2Vec characterization of medication guidelines, the proposed multi-label text classification method is effective and the F1 value is significantly improved. Proved by analysis of ablation and crossover experiments, the proposed method can achieve a high accuracy rate averaged at 0.8339. © 2024 – IOS Press. All rights reserved.

关键词： Feature extraction

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Optimizing Stand-Alone PV Systems:A Metaheuristic-Enhanced Fuzzy Approach for Adaptive MPPT

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China communications 2025年第1期22卷 61-74页

作者： Tina Samavat Mostafa Nazari Lin Fuhong Lei Yang Department of Computer Science and Engineering University of NevadaRenoNVUSA Faculty of Mechanical Engineering Shahrood University of TechnologyShahroodIran School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.

关键词： genetic algorithm imperialist competitive algorithm invasive weed algorithm maximum power point tracking

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