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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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A Deployment of Ensemble-Based IDS on Raspberry Pi 13

A Deployment of Ensemble-Based IDS on Raspberry Pi

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Musthafa, Muhammad Bisri Huda, Samsul Shamim, S.M. Kodera, Yuta Mwaura, Jedidah Araki, Shunsuke Nogami, Yasuyuki Okayama University Graduate School of Environmental Life Natural Science and Technology Japan Okayama University Green Innovation Center Japan Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350355079

Intrusion Detection Systems (IDS) are essential for safeguarding IoT networks against various attacks. Our previously developed ensemble-based IDS model, which combines stacked Long Short-Term Memory (LSTM) networks with ANOVA feature selection, achieved a remarkable accuracy of 97.46% and an overfitting value of 0.22% in classifying network attacks. However, deploying this powerful model on resource-constrained devices like Raspberry Pi poses challenges due to limited computational resources. In this paper, we tackle these challenges by converting the original model to TensorFlow Lite (TFLite) and applying quantization techniques to optimize the model size for deployment on a Raspberry Pi 3b+. Our approach significantly reduces the model size by 90.9% while maintaining an impressive accuracy of 96.96%, only a 0.5% reduction from the original model. The optimized model demonstrates efficient performance on the Raspberry Pi, with a loading time of 100.2ms and an inference time of 1.2ms. © 2024 IEEE.

关键词： Network intrusion

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Enhanced Experts with Uncertainty-Aware Routing for Multimodal Sentiment Analysis 24

Enhanced Experts with Uncertainty-Aware Routing for Multimod...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Gao, Zixian Hu, Disen Jiang, Xun Lu, Huimin Shen, Heng Tao Xu, Xing Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Multimodal sentiment analysis, which has garnered widespread attention in recent years, aims to predict human emotional states using multimodal data. Previous studies have primarily focused on enhancing multimodal fusion and integrating information across different modalities, while overlooking the impact of noisy data on the internal features of each single modality. In this paper, we propose the Enhanced experts with Uncertainty-Aware Routing (EUAR) method to address the influence of noisy data on multimodal sentiment analysis by capturing uncertainty and dynamically altering the network. Specifically, we introduce the Mixture of Experts approach into multimodal sentiment analysis for the first time, leveraging its properties under conditional computation to dynamically alter the network in response to different types of noisy data. Particularly, we refine the experts within the MoE framework to capture uncertainty in the data and extract clearer features. Additionally, a novel routing mechanism is introduced. Through our proposed U-loss, which utilizes the quantified uncertainty by experts, the network learns to route different samples to experts with lower uncertainty for processing, thus obtaining clearer, noise-free features. Experimental results demonstrate that our method achieves state-of-the-art performance on three widely used multimodal sentiment analysis datasets. Moreover, experiments on noisy datasets show that our approach outperforms existing methods in handling noisy data. © 2024 ACM.

关键词： Data assimilation

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Identification of Ischemic Stroke Origin Using Machine Learning Techniques

Identification of Ischemic Stroke Origin Using Machine Learn...

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Prasad, Rajan Shukla, Praveen Kumar Artificial Intelligence Research Center Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (纸本)9789819909803

An ischemic stroke arises as a result of atherosclerosis. It promotes tissue and cholesterol accumulation in the vascular system. When a large amount of plaque collects in one region, it has the potential to reduce blood supply to crucial organs. Carotid arteries are blood vessels that carry blood to the brain and are found in the neck. A stroke may develop if plaque plugs a carotid artery (carotid artery disease). Atherosclerosis is mostly asymptomatic. As a result, many people are unaware they have atherosclerosis until they have a stroke or suffer the additional repercussions of clogged arteries near other organs, such as a heart attack. The primary goal of this project is to identify cases of ischemic stroke by utilizing blood clot imaging datasets with CNN and CatBoost classifiers. The experimental results show that the CNN achieved 97.5% accuracy, whereas the CatBoost achieved 73.5% accuracy. This study will aid healthcare providers in properly detecting the sources of blood clots in fatal strokes, allowing doctors to deliver the best post-stroke treatment and minimize the risk of a second stroke. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Enhancing Banking Security: An Integrated Approach to IoT Threat Mitigation with Artificial Intelligence 5th

Enhancing Banking Security: An Integrated Approach to IoT Th...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Rhaif Alsahlanee, Abbas Thajeel Singh, Jagendra Garg, Neha Rajpoot, Abha Kiran Tiwari, Mohit Elangovan, Muniyandy Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9789819788644

This research presents a composite approach to bank security issues by integrating the Internet of Things (IoT) threat reduction and artificial intelligence (AI) into systems. In this research, machine learning (ML) techniques are deployed across key banking operations such as ATM false alarms, health monitoring of hardware components, and adding security to locker rooms. Through the utilization of ML models such as CNN, SVM, KNN, and RNN, significant advancements are achieved in detecting fraudulent activities within ATM transactions. ML-driven approaches have high precision, recall and accuracy rates in bolstering ATM network security. In addition, predictive maintenance models trained on sensor data from ATM hardware components enable proactive maintenance interventions. This in turn leads to improved ATM availability, reduced downtime, and cost savings. Biometric authentication systems based on ML models appear to have promising effects in locker room security. It is aimed at enabling security measures to be improved intelligently. Overall, this research shows how banks can use AI to tackle threats such as those maxing home machines or video-recordings by infiltrating introductory courses. By making the most of IoT-linked data and ML algorithms, the ability of banks to detect and prevent fraudulent activities can be improved in addition to having a preventive maintenance program where fees are paid in ATM withdrawals. These advancements contribute not only to safeguarding the financial assets and customer data but also to fostering trust and confidence in the banking systems. Continual research and innovation in AI and IoT technologies are crucial in order to strengthen banking security measures against the digital challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive maintenance

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Editorial:special issue on frontiers of control and automation,dedicated to *** *** 60th birthday

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Control Theory and Technology 2023年第3期21卷 263-264页

作者： Jie Chen Jie Huang Lihua Xie Department of Control Science and Engineering Tongji UniversityShanghaiChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Center for Advanced Robotics Technology Innovation School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

It is our great pleasure and honor to organize this special issue"Frontiers of Control and Automation"in honor of the 60th birthday of our long-time colleague and friend Professor Ben *** Chen obtained his *** in mathemat-ics and computer science in 1983 from Xiamen University,China;*** in electrical engineering in 1988 from Gonzaga University,Spokane,Washington,USA;and *** in electrical and computer engineering in 1991 from Washington State University,Pullman,Washington,USA.

关键词： computer Xiamen frontier

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Biological Classification System Knowledge Graph and Semi-automatic Construction of Its Invertebrate Fossil Branches

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Journal of Earth science 2024年第6期35卷 2119-2128页

作者： Shaochun Dong Yukun Shi Yizao Ran Haijun Wu Yiying Deng Junxuan Fan Xinyu Dai School of Earth Sciences and Engineering Frontiers Science Center for Critical Earth Material CyclingNanjing UniversityNanjing210046China Jiangsu Deep-Time Digital Earth Research Center for Excellence Suzhou215004China School of Computer Science Nanjing UniversityNanjing210046China School of Resources and Environmental Engineering Hefei University of TechnologyHefei230009China State Key Laboratory for Mineral Deposits Research Nanjing210046China National Key Laboratory for Novel Software Technology Nanjing210046China

Biological classification is the foundation of biology and paleontology,as it arranges all the organisms in a hierarchy that humans can easily follow and *** is further used to reconstruct the evolution of life.A biological classification system(BCS)that includes all the established fossil taxa would be both useful and challenging for uncovering the life *** fossil taxa were originally recorded in various published books and articles written by natural languages,the primary step is to organize all those taxa information in a manner that can be deciphered by a computer system.A Knowledge Graph(KG)is a formalized description framework of semantic knowledge,which represents and retrieves knowledge in a machine-understandable way,and therefore provides an eligible method to represent the *** this paper,a model of the BCS KG including the ontology and fact layers is *** put it into practice,the ontology layer of the invertebrate fossil branches was manually developed,while the fact layer was automatically constructed by extracting information from 46 volumes of the Treatise of Invertebrate Paleontology series with the help of natural language processing *** a result,27348 taxa nodes spanning fourteen taxonomic ranks were extracted with high accuracy and high efficiency,and the invertebrate fossil branches of the BCS KG was thus *** study demonstrates that a properly designed KG model and its automatic construction with the help of natural language processing are reliable and efficient.

关键词： biological classification system knowledge graph ontology invertebrate fossil big data

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Learning a generalizable re-identification model from unlabelled data with domain-agnostic expert

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Visual Intelligence 2024年第1期2卷 337-349页

作者： Fangyi Liu Mang Ye Bo Du National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication EngineeringSchool of Computer ScienceHubei Luojia LaboratoryWuhan UniversityWuhan430072China

In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly different from the source domains,remains ***,the performance of current DG ReID relies heavily on labor-intensive source domain *** the potential of unlabeled data,we investigate unsupervised domain generalization(UDG)in *** goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target *** address this,we propose a new approach that trains a domain-agnostic expert(DaE)for unsupervised domain-generalizable person *** involves independently training multiple experts to account for label space inconsistencies between source *** the same time,the DaE captures domain-generalizable information for *** experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG *** results demonstrate the superiority of our method over state-of-the-art *** will make our code and models available for public use.

关键词： Domain generalization(DG) Unlabeled source domains Label space inconsistencies Domain-agnostic expert(DaE)

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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