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3rd International conference on Rail Transportation, ICRT 2024

作者： Zhu, Yingmou Luo, Xianguang Liao, Zhiyuan Yang, Tianzhi Deng, Jiangmin State Key Laboratory of Heavy-Duty and Express High-Power Electric Locomotive CRRC Zhuzhou Locomotive Co. Ltd. Zhuzhou China Co. Ltd. Beijing China

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

The ventilation and cooling system constitutes a vital component of electric locomotives, significantly influencing the operational integrity of the locomotive's traction system. This study is grounded in acquired health monitoring data from the electric locomotive's ventilation and cooling system. The data encompass parameters such as the front and rear bearing temperatures and bearing vibration speeds of the traction cooling fans, cooling tower fans, and auxiliary converter cooling fans. Furthermore, it encompasses details regarding the internal wind speeds within various ducts of the ventilation and cooling system. In addition, operational data including locomotive speed, traction force, motor temperature, fan control frequency, longitude, latitude, and altitude data of Beidou positioning are integrated. The paper undertakes a thorough correlation analysis between the health monitoring data of the ventilation and cooling system and the locomotive's operational data. It delves into the intricate coupling relationships and dynamic patterns between these datasets. Employing correlation analysis and machine learning methodologies, the research investigates predictive aspects of the ventilation and cooling system's performance, such as the condition of fan bearings and the presence of duct blockages. The objective of this study is to facilitate informed assessments of the operational state of the ventilation and cooling system, thereby furnishing a solid theoretical foundation for the formulation of maintenance and servicing strategies. © ASCE.

关键词： Ventilation

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Design and Comparative analysis of SRM Drive based on Two Novel Power Converter Topologies

Design and Comparative Analysis of SRM Drive based on Two No...

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Power, control and Computing Technologies (ICPC2T), conference on

作者： Sandeep Kumar Dehuri Makarand M. Lokhande Dept of Electrical Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The paper presents, two-novel current control converter topologies for 4-phase Switched Reluctance Motors (SRMs). The first converter is based on a reduced switch model, significantly decreasing the number of switching devices by inducting six IGBT power transistors and three modes of operation for minimizing torque ripple, thus providing a compact, lightweight, and inexpensive design. The second design is based on a MOSFET-switching enhanced four-level converter providing four modes of operation for minimizing torque ripple. Using MATLAB/SIMULINK Software, a 4-phase 8/6 SRM is simulated using the proposed converters. The simulation data validates the torque ripple-reducing capability of the suggested converters in the control process of SRM. Based on simulation outcomes a comparative analysis is performed.

关键词： Torque Computational modeling Switches Switched reluctance motors Mathematical models Topology Torque measurement Voltage control Load modeling Switching circuits

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Combining Knowledge Graphs and NLP to Analyze Instant Messaging data in Criminal Investigations 25th

Combining Knowledge Graphs and NLP to Analyze Instant Mess...

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25th International conference on Web Information Systems Engineering, WISE 2024

作者： Pozzi, Riccardo Barbera, Valentina Alva Principe, Renzo Giardini, Davide Rubini, Riccardo Palmonari, Matteo University of Milano-Bicocca Milan Italy Rome Italy

ISBN: (纸本)9789819605668

Criminal investigations often involve the analysis of messages exchanged through instant messaging apps such as WhatsApp, which can be an extremely effort-consuming task. Our approach integrates knowledge graphs and NLP models to support this analysis by semantically enriching data collected from suspects’ mobile phones, and help prosecutors and investigators search into the data and get valuable insights. Our semantic enrichment process involves extracting message data and modeling it using a knowledge graph, generating transcriptions of voice messages, and annotating the data using an end-to-end entity extraction approach. We adopt two different solutions to help users get insights into the data, one based on querying and visualizing the graph, and one based on semantic search. The proposed approach ensures that users can verify the information by accessing the original data. While we report about early results and prototypes developed in the context of an ongoing project, our proposal has undergone practical applications with real investigation data. As a consequence, we had the chance to interact closely with prosecutors, collecting positive feedback but also identifying interesting opportunities as well as promising research directions to share with the research community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Research on Causal Mechanisms and Visualization for Causal Inference Based on Drug Crimes 24

Research on Causal Mechanisms and Visualization for Causal I...

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Proceedings of the 2024 4th International conference on Computational modeling, Simulation and data analysis

作者： Shaobing Wu Key Laboratory of Intelligent Drug Control Institute of Information Security Yunnan Police College Kunming Yunnan China

ISBN: (纸本)9798400711831

Causal reasoning is a statistical paradigm that quantifies causal effects using observational data. This is a complex process that requires multiple steps, iterations, and collaboration with domain experts. Visualization is often relied upon to evaluate the accuracy of each step during analysis. However, existing visualization toolkits are not designed to support the entire causal inference process in the computing environment familiar to analysts. In this article, we use the Python visualization package Causalvis for causal reasoning to address the issue. On the basis of collecting relevant data on drug crimes, conduct causal relationship visualization and causal mechanism research on drug crimes.

关键词： Causal Mechanisms

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A Novel Sequence-to-Sequence-Based Deep Learning Model for Multistep Load Forecasting

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 638-652页

作者： Lu, Renzhi Bai, Ruichang Li, Ruidong Zhu, Lijun Sun, Mingyang Xiao, Feng Wang, Dong Wu, Huaming Ding, Yuemin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Shanghai Elect Grp Co Ltd Cent Acad Shanghai 200070 Peoples R China Kanazawa Univ Inst Sci & Engn Kakuma Kanazawa Ishikawa 9201192 Japan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Univ Navarra Tecnun Sch Engn San Sebastian 20018 Spain

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs. There are many approaches used for load forecasting such as the support vector regression (SVR), the autoregressive integrated moving average (ARIMA), and neural networks, but most of these methods focus on single-step load forecasting, whereas multistep load forecasting can provide better insights for optimizing the energy resource allocation and assisting the decision-making process. In this work, a novel sequence-to-sequence (Seq2Seq)-based deep learning model based on a time series decomposition strategy for multistep load forecasting is proposed. The model consists of a series of basic blocks, each of which includes one encoder and two decoders;and all basic blocks are connected by residuals. In the inner of each basic block, the encoder is realized by temporal convolution network (TCN) for its benefit of parallel computing, and the decoder is implemented by long short-term memory (LSTM) neural network to predict and estimate time series. During the forecasting process, each basic block is forecasted individually. The final forecasted result is the aggregation of the predicted results in all basic blocks. Several cases within multiple real-world datasets are conducted to evaluate the performance of the proposed model. The results demonstrate that the proposed model achieves the best accuracy compared with several benchmark models.

关键词： Load modeling Predictive models Load forecasting Forecasting Autoregressive processes data models Time series analysis Decomposition strategy long short-term memory (LSTM) neural network multistep load forecasting sequence-to-sequence (Seq2Seq) model temporal convolution network (TCN)

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Simulation and Risk Assessment of Rolling Stone Disaster on Highway Slope Based on Big data

Simulation and Risk Assessment of Rolling Stone Disaster on ...

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Dou, Xuefeng Zou, Huian Hu, Li Li, Wenbiao Ma, Xiaofei Chongqing Yuxiang Double Line Expressway Co. Ltd. Chongqing400067 China China Merchants Chongqing Communication Research & Design Institute Co. Ltd. Chongqing400067 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Rockfall, as a more common form of geological disaster in mountainous areas, is caused by geological structure, formation lithology, topography and other factors, and usually appears in natural slopes, artificially excavated road slope and other areas. According to the accumulated experience of research on rolling stone disaster movement of highway slope in recent years, in order to reduce the adverse impact of falling rocks on highway traffic safety, people will set up protective facilities around the slope to effectively intercept the stones falling down the hillside. From a practical point of view, the design must understand the falling speed, height and movement trajectory of the falling rock, and propose effective prevention and control measures according to the movement characteristics of the falling rock, so as to reduce the adverse effects caused by the rolling stone on the highway slope. Therefore, after understanding the characteristics of highway slope rockfall movement and the risk identification process of slope engineering, this paper constructs a highway rockfall risk assessment system based on big data, takes a certain highway slope rockfall movement as an example for simulation analysis, and finally puts forward effective prevention and control management measures. © 2025 SPIE.

关键词： Lithology

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2nd International conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

2nd International Conference on Security and Information Tec...

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2nd International conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

ISBN: (纸本)9789819777853

The proceedings contain 46 papers. The special focus in this conference is on Security and Information Technologies with AI. The topics include: Authentication of Image Fidelity in Watermarking-QR-Code Approving Copyright Systems;a Zero-Watermarking Image Scheme in Normalized Cross-Correlation with Robust Copyright Protections;a Novel Approach for Detecting and Analyzing Cyber-Attacks in Cyber-Physical Systems;white-Box Penetration Testing for Hash Collision Attack on Web Applications;towards Zero Trust for Financial Sectors: A Proposed Framework on Trust Evaluation;FiT-DPKI: Decentralized Public Key Infrastructure with Flexibility and Transparency for IoT Networks;Revolutionizing Healthcare: Case Studies of AI Algorithms Transforming the Field of Medicine;analysis and Detection of Abnormal Transactions on Ethereum;feasibility analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models;Securing NLP Systems: A Comprehensive AI-Based Approach;A New Scheme modeling Gym Membership Transactions with NFT Systems;A Text to Human-Like Speech Using Tacotron-Based TTS Model;A Novel Malware Classification Using CNN-SVM Deep Learning Method Based on Transfer Learning Architecture;enhancing Project Programming Hour Prediction with Regression analysis Techniques—A Case Study of Company D;a New Traceable One-Time Address Scheme Secure Against Privilege Escalation Attack;An Object-Based Multi-level Authentication Framework for AR e-Book;a Door Lock System Based on Visual Cryptography;Automatic APT Attack Reconstruction Supporting Lateral Movement;using Open-Source Intelligence to Archive Criminal Organizations;FIDO-Based Access control Mechanism in Named data Networking;empirical Approach to a Fine-Tuning Using Forgetting in Large Language Models;automatic Wound Segmentation with Deep Convolutional Neural Networks;Prediction of the Prevalence of COVID-19 Using Epidemic Differential Equations and Deep Learning Network.

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6th International conference on Intelligent Communication, control and Devices, ICICCD 2024

6th International Conference on Intelligent Communication, C...

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6th International conference on Intelligent Communication, control and Devices, ICICCD 2024

ISBN: (纸本)9789819783281

The proceedings contain 71 papers. The special focus in this conference is on Intelligent Communication, control and Devices. The topics include: Treatment of Mood Swings Using AI;Detection of Lung Cancer with YOLO-NAS: A Novel Approach for Enhanced Diagnostic Accuracy;Optimised Grid Integration of Solar PV and Electric Vehicle (EV): A Dynamic State-Based Energy Management Strategy;Multiple Intrusion Detection in Complex Cloud Environments Using Random Forest and Deep Learning on the UNSW-NB15 Benchmark datasets;Real-Time Power Estimation and Monitoring of an IoT Node Using MQTT;Exploring MIMO-NOMA: Enhancing Multi-user Communication with Power Allocation;a Framework Utilizing Deep Learning for Detecting Multiple Cancers in Medical Imaging;Revolutionizing Medical Applications: IoT-Based IVF Monitoring;AI Virtual Mouse: Revolutionizing Human–computer Interaction;A Wideband Monolayered Graphene-Based Microstrip Patch Antenna for THz-Frequency Applications;A Monolayered Graphene-Based Microstrip Patch Antenna for Optimal Performance in the Mid-THz Band;kinematic analysis and modeling of the Gait by Parametrization of the Body Trajectories of 18 Degree-of-Freedom Hexapod Robots Using Reinforcement Learning;modernized Ration Distribution System with Integrated Mobile Accessibility;Multiple Device-Based Geo-Position Spoofing Detection in Instant Messaging Platform with Residual Noise Extraction Using DRN;compact Circular Patch Grid Array Antenna for Medical Imaging Applications;driver Drowsiness Detection System: An Integrated Vision-Based Approach;development of Bit Synchronizer for Remote Sensing Satellite data;design and Optimisation of Compact Wideband Yagi-Uda Antenna for V2V Communication;Advancing Object Discovery: Unveiling the Power of YOLO in Computer Vision Applications;ambulance Precedence Integrated Traffic Management System;Performance analysis of Crossbar in NOC Through Booksim Simulator.

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Fine Characterization of Transient Operation Characteristics of Multiphase Drive System Based on Joint modeling of data and Knowledge 1st

Fine Characterization of Transient Operation Characteristics...

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1st Electrical Artificial Intelligence conference, EAIC 2024

作者： Yan, Xu Jin, Xu Xingfa, Sun Yajie, Zhi yang, Xu National Key Laboratory of Electromagnetic Energy Navel University of Engineering Wuhan43000 China

ISBN: (纸本)9789819640584

The polyphase motor has good fault tolerant operation due to its inherent multi-phase structure. In order to improve its control performance in transient operation conditions, accurate motor models and parameters are essential. In this paper, a mathematical modeling and parameter identification method for the whole link of multiphase drive system is proposed. Taking a six-phase motor as an example, a unified state-space model of the six-phase motor in the stationary αβ coordinate system is derived. Then, based on the hardware architecture of the control system and the software execution timing, the control delay from the instruction voltage to the actual voltage and the sampling delay characteristics from the actual current to the feedback current are analyzed. On this basis, a full link response model from command voltage to feedback current in the control system is established. Finally, an off-line parameter identification method for data-driven state-space model based on improved particle swarm optimization algorithm is proposed. The experimental data analysis shows that the overall model error is within 5% and the maximum error is not more than 10%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Application of data-Driven Techniques in 3D Soil Characterization for Top-Hole Design

Application of Data-Driven Techniques in 3D Soil Characteriz...

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Offshore Technology conference, OTC 2025

作者： Filho, C. A. F. Várady Tenório, J.K.F. Junior, E. T. Lima Santos, J.P.L. Dias, R. Cutrim, F.S. Laboratory of Scientific Computing and Visualization Federal University of Alagoas Maceió Alagoas Brazil Petrobras Rio de Janeiro Riode Janeiro Brazil

ISBN: (纸本)9781959025610

Present work investigated the application of 3D Sparse Bayesian Learning (SBL) and Gaussian processRegression (GPR) for data-driven offshore site characterization focusing specifically on estimating critical soil parameters for top-hole design in oil and gas wells, such as undrained shear resistance and specific unit weight. These parameters are essential for designing conductor and surface casing sections in drilling operations and ensuring the structural integrity of wellhead systems and casing strings. This study used datasets of piezocone penetration tests (CPTu) conducted in the Campos basin in Brazil and supplied by the partner operator. Input data is used to generate 2D and 3D modeling of the spatial trend of undrained shear resistance and specific weight using the test data. The trend is assessed using Sparse Bayesian Learning and Gaussian process Regression, which are probabilistic-based methods capable of dealing with data uncertainties. These methods, which represent the latest advancements in the field according to current literature, provide accurate and cost-effective estimates of soil properties. analysis procedure consisted of assessing the plausibility of the models regarding the CPTu data used as input. The comparative analysis revealed that both SBL and GPR improved soil characterization compared to traditional methods. Both methods show significant capabilities to model spatial trends, and accuracy and performance issues are addressed for 3D modeling with different soil types. Depending on the initial data, both methods can present interesting results with similar computational cost but, in general, SBL takes longer time to be *** evidence is used as metric to compare both models as it is shown that is equivalent to a cross-validation procedure using the log posterior probability function as scoring function. The probabilistic nature of both techniques supports robust evaluation of uncertainties associated with spatial heterogeneit

关键词： Gaussian distribution

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