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Research on the Transient Potential Rise Characteristics of High - speed Railway Rail Lightning Strike and Its Impact on Trackside Signal Equipment 37

Research on the Transient Potential Rise Characteristics of ...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Wang, Shulai Xiang, Nianwen Wang, Chaoqun Lyu, Zengwei Chen, Zhiwei School of Electrical Engineering and Automation Hefei University of Technology Hefei China Institute of Energy Hefei Comprehensive National Science Center Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783800764174

Lightning strikes on the catenary will cause a sharp rise in rail potential, which directly threatens the safety of the signal equipment connected to the rail along the trackside. It is urgent to carry out research on the propagation law of transient overvoltage on rails caused by lightning strikes on the catenary. A rail lightning electromagnetic transient calculation model considering the influence of integrated grounding system and catenary is established, and the influence law of different lightning strike point positions, different pier heights, and different lightning current waveforms on the distribution of rail potential rise is calculated. The specific conclusions are as follows: when lightning strikes different positions within a catenary span, the rail potential within 700m near the lightning strike point will exceed the lightning strike tolerance level of the trackside signal equipment, causing equipment damage. The potential rise increases by about 9 kV;for every 5 m increase in pier height, the rail potential rise increases by about 16%. Based on the law of the rail potential rise, the common-mode protection methods for the trackside signal equipment are proposed by appropriately reducing the horizontal connection distance of uplink and downlink ground wires to decrease the rail potential and installing common-mode protection devices. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Signaling equipment

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Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

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computers, Materials & Continua 2022年第5期71卷 2497-2513页

作者： Mohammad Alamgeer Fahd N.Al-Wesabi Huda G.Iskandar Imran Khan Nadhem Nemri Mohammad Medani Mohammed Abdullah Al-Hagery Ali Mohammed Al-Sharafi Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication *** this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed *** researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English ***,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any *** has been implemented using PHP with VS code *** robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and *** SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

关键词： Watermarking soft computing text analysis hidden Markov model content authentication

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Innovative Approaches in Microstrip Antennas: A Comparative Study of Enhanced Designs for 5G Communication Systems 5

Innovative Approaches in Microstrip Antennas: A Comparative ...

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5th IEEE International Conference on Recent Trends in computer science and technology, ICRTCST 2024

作者： Raotole, Atharv Shirodkar, Sailee Shah, Tanish Gwalani, Aryan Joshi, Aaditya Bothra, Aayush Sardar Patel Institute of Technology Information Technology Mumbai India Veermata Jijabai Technological Institute Electronics Engineering Mumbai India Thadomal Shahani Engineering College Computer Science Mumbai India

ISBN: (纸本)9798350351378

This study compares microstrip antennas, microstrip antenna arrays, and slotted microstrip antenna arrays for 5G communication. Emphasizing the compact size and compatibility with 5G devices, microstrip antennas are explored. The research delves into the specialized designs of slotted microstrip arrays for improved signal quality, considering factors like gain and radiation patterns. The study highlights the advantages of antenna arrays over individual microstrip antennas and suggests areas for future research. Overall, the research contributes to enhancing 5G systems, emphasizing the benefits of these antennas for faster, more capable, and dependable communication. © 2024 IEEE.

关键词： Microstrip antennas

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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Impact Estimation of Security Attack and Mitigation Strategy using Goal-Based Threat Modeling in a Peer-to-Peer Cloud System

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Journal of The Institution of Engineers (India): Series B 2022年第5期103卷 1845-1862页

作者： De, Sanghamitra Barik, Mridul Sankar Banerjee, Indrajit Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology West Bengal Kolkata700023 India Department of Computer Science Engineering Jadavpur University West Bengal Kolkata700032 India Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur West Bengal Howrah711103 India

Addressing non-functional requirements is essential in Requirements engineering and this also covers security requirements of any system. Threat modeling forms an important way of identifying security requirements. This paper considers an existing Non-Functional Requirement-based (using Softgoal Interdependency Graph or SIG) approach to perform threat modeling on Peer-to-Peer Cloud Systems (P2PCS) for Zeus bot attack. For an attack of Zeus or its variant, a method for impact calculation in case an attack materializes is proposed. The paper also suggests a weighted method of impact calculation of operationalization goals which are mitigation measures to avoid security attacks. Other contribution of this paper is finding minimum set of softgoals using causal logic. Finally, the paper also lists high level security requirements and testing sequences for a given attack scenario. The paper is case study based and provides an exploratory approach towards establishing the practice of goal-based threat modeling as a method for security requirements engineering in a Peer-to-Peer Cloud system. © 2022, The Institution of Engineers (India).

关键词： Cryptography

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Mongolian Compound Word Semantic Web Word Vector Evaluation Research 4

Mongolian Compound Word Semantic Web Word Vector Evaluation ...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： La, Du Hasi Ding, Yang Inner Mongolia Normal University College of Computer Science and Technology Hohhot China College of Computer Science and Information Engineering Hohhot Minzu College Hohhot China

ISBN: (纸本)9798350324303

Semantic dictionaries are the basis of natural language processing tasks. With the construction of WordNet, CCD, HowNet and other semantic dictionaries, the semantic resources of Chinese and English have been greatly improved, and so has the construction of Mongolian lexical semantic network. As an important part of the lexical system, the construction project of Mongolian lexical semantic network is an essential step to fill in the perfect compound words. In this study, the Mongolian compound word semantic web based on the optimal word vector model is automatically generated by comparing the Word2Vec model, Glove model and FastText model for Mongolian word vectors in different dimensions and performing similarity and similarity on the test set. © 2023 IEEE.

关键词： Semantic Web

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Optimizing Content-Based Image Retrieval System Using Convolutional Neural Network Models 8th

Optimizing Content-Based Image Retrieval System Using Convol...

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8th International Conference on information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Kashid, Shraddha S. Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Sule, Bipin Deshpande, Arati V. Salve, Bhausaheb S. Department of Computer Engineering MIT ADT University Pune India Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India Computer Engineering Department Sandip University Mahiravani Trimbak Road Maharashtra Nashik India

ISBN: (纸本)9789819766741

This study focuses on improving "Content-Based Image Retrieval" (CBIR) systems through the utilization of optimized convolutional neural network (CNN) models. Traditionally, CBIR relied on text-based approaches, which proved inefficient in the digital era. In this research, we propose a novel visual content-based retrieval method, leveraging key image features like color, texture, and shape. To expedite the process, we employ pre-trained CNN models, such as the ResNet architecture, trained on extensive datasets like Image-Net. Our experiments, conducted on the "Paris 6K and Oxford 5K datasets, demonstrate the superior performance" of the ResNet model compared to the state-of-the-art AlexNet. This is attributed to ResNet’s ability to reduce training time without sacrificing accuracy. The evaluation, based on average mean precision, yielded promising results of 92.12% for Paris 6K and 84.81% for Oxford 5K, showcasing the efficacy of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural network models

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Frequency-informed transformer for real-time water pipeline leak detection

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Autonomous Intelligent Systems 2025年第1期5卷 1-10页

作者： Liu, Fengnian Wang, Ding Tang, Junya Wang, Lei College of Electronic and Information Engineering Tongji University Shanghai201804 China School of Computer Science and Technology Tongji University Shanghai201804 China Shanghai KARON Eco-valve Manufacturing Co. ltd Shanghai201804 China

Water pipeline leaks pose significant risks to urban infrastructure, leading to water wastage and potential structural damage. Existing leak detection methods often face challenges, such as heavily relying on the manual selection of frequency bands or complex feature extraction, which can be both labour-intensive and less effective. To address these limitations, this paper introduces a Frequency-Informed Transformer model, which integrates the Fast Fourier Transform and self-attention mechanisms to enhance water pipe leak detection accuracy. Experimental results show that FiT achieves 99.9% accuracy in leak detection and 98.7% in leak type classification, surpassing other models in both accuracy and processing speed, with an efficient response time of 0.25 seconds. By significantly simplifying key features and frequency band selection and improving accuracy and response time, the proposed method offers a potential solution for real-time water leak detection, enabling timely interventions and more effective pipeline safety management. © The Author(s) 2025.

关键词： Discriminant analysis

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Simplifying Structure and Pattern in Complex Social Networks Using Graph Isomorphism 14

Simplifying Structure and Pattern in Complex Social Networks...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Arunakumari, B.N. Kshama, S.B. Swasti, S. Sameeksha, B.H. Puri, Sonia Rubini, P.A. Bms Institute of Technology and Management Dept. of Computer Science and Engineering Bengaluru India Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

In recent years, networks have grown in size and complexity, traditional network testing methods have become time-consuming and inefficient. As a result, there is a need for more efficient and reliable network testing methods. One important topic in the field of network testing is graph pattern matching. Network analysis relies on graph pattern matching. The paper discusses the use of graph isomorphism for network testing. This paper also gives an overview of recent advances in isomorphism testing, which determines if two networks are structurally identical despite potential labeling discrepancies between them. The combination of isomorphism and pattern matching in network analysis is used to identify equivalent structures in different networks. This approach is used to identify and compare common patterns in complex network structures, such as those found in social networks. © 2023 IEEE.

关键词： Pattern matching

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A Systematic Review of Intrusion Detection Systems in Internet of Things Using ML and DL 4

A Systematic Review of Intrusion Detection Systems in Intern...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Hussain, Ayaz Sharif, Hanan Rehman, Faisal Kirn, Hina Sadiq, Ashina Khan, Muhammad Shahzad Riaz, Amjad Ali, Chaudhry Nouman Chandio, Adil Hussain Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan Quaid e Awam University of Engineering Science and Technology Department of Electronic Engineering Larkano Pakistan

ISBN: (纸本)9798350335316

The rapid expansion of IoT technologies like smart sensors and home appliances is likely to have far-reaching consequences. Connectivity, ubiquitous presence, and low computational power are the hallmarks of IoT gadgets. The global tally of Internet of Things gadgets is on the rise. There has been an increase in IoT -based cyber-attack incidents due to the proliferation of these devices, which is much easier to do than with desktop computers. New methods of identifying attacks launched from compromised IoT devices are needed to help address this issue. In this scenario, the most effective method of detective control against attacks originating from IoT devices is the application of ML and DL techniques. The main aim of this research is to provide an overview of intrusion detection models as well as a comprehensive review of threats to IoT systems caused by compromised IoT devices. © 2023 IEEE.

关键词： Intrusion detection

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