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Calibration of Traffic Microsimulation Models Using the Results of intelligent Video Analysis of the Traffic 27

Calibration of Traffic Microsimulation Models Using the Resu...

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27th international Scientific conference on Transport Means 2023

作者： Sistuk, V. Monastyrskyi, Y. Maksymenko, I. Kryvyi Rih National University V.Matusevych St.11 Kryvyi Rih50027 Ukraine

ISBN: (纸本)9781713879534

Estimation of traffic state, road design, and traffic re-engineering at the sections of the road network requires data on traffic volumes, vehicle speeds, accelerations/decelerations, and other traffic parameters for the specific time horizon. However, traffic counting utilizing visual observations in the field conditions is raveled by the negative impact of the environment on traffic counter, working hours increasing, unpredictability, and unavailability of observation results verification. Overcoming these difficulties is in the realm of possibilities by using automated traffic data processing. automated traffic video analysis software with artificial intelligence (AI) technology is competing with traditional methods of field traffic data collection. In recent years, field traffic data collection methods based on intelligent video analysis of traffic are gained ground. Origin-destination matrixes and traffic dynamics measures for each vehicle can be obtained from traffic video footage post-process. that provides opportunity to develop a traffic microsimulation model more reliably and accurately. the paper presents the technique of traffic microsimulation model calibration by using parameters obtained because of traffic video analysis. © 2023 Kaunas University of Technology. All rights reserved.

关键词： Computer vision

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Machine learning-Driven Optimization of Livestock Management: Classification of Cattle Behaviors for Enhanced Monitoring Efficiency 24

Machine Learning-Driven Optimization of Livestock Management...

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7th international conference on Machine learning and Machine Intelligence, MLMI 2024

作者： Zhao, Zhuqing Shehada, Halah Ha, Dong Dos Reis, Barbara White, Robin Shin, Sook Electrical and Computer Engineering Virginia Tech Blacksburg United States Department of CREC-White Sand Unit Mississippi State University Starkville United States School of Animal Sciences Virginia Tech Blacksburg United States

ISBN: (纸本)9798400717833

Monitoring cattle health in remote and expansive pastures poses significant challenges that necessitate automated, continuous, and real-time behavior monitoring. this paper investigates the effectiveness and reliability sensor-based cattle behavior classification for such monitoring, emphasizing the impact of intelligent feature selection in enhancing classification performance. To achieve this, we developed Wireless Sensor Nodes (WSN) affixed to individual cattle, enabling the capture of 3-axis acceleration data from five cows across varying seasons, spanning from summer to winter. Initially, we extracted a comprehensive set of 52 features, representing a broad spectrum of cow behaviors alongside statistical attributes. To enhance computational efficiency, we employed the Recursive Feature Elimination (RFE) method to distill 30 critical features by discarding redundant or less significant ones. Subsequently, these optimized features were utilized to train four machine learning (ML) models: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF), and Histogram-based Gradient Boosted Decision Trees (HGBDT). Notably, the HGBDT model demonstrated superior performance, achieving remarkable F1-scores of 99.01% for'grazing', 98.74% for'ruminating', 89.62% for'lying', 84.06% for'standing', and 91.87% for'walking'. these findings underscore the potential of our approach to serve as a robust framework for precision livestock farming, offering valuable insights into enhancing cattle health monitoring in remote environments. © 2024 Copyright held by the owner/author(s).

关键词： Support vector machines

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Research on a New Flow Measurement for intelligent Injection Tools 11

Research on a New Flow Measurement for Intelligent Injection...

引用

11th Annual international conference on Material Science and Environmental engineering, MSEE 2023

作者： Wang, Min Deng, Gang Liu, Junli Yue, Qingfeng Sun, Yufei Daqing Oilfield Production Technology Institute Daqing Oilfield Limited Company Daqing163453 China Heilongjiang Provincial Key Laboratory of Oil and Gas Reservoir Stimulation Daqing163453 China Development Department of Daqing Oilfield Limited Company Daqing163453 China No.4 Oil Production Plant Daqing Oilfield Limited Company Daqing163453 China

intelligent separate injection technology can achieve continuous monitoring data of layered water Injection wells. So it can find out the inefficient water circulating formation timely. then the water injection schemes can be adjusted in real-time to ensure the high water injection pass rate. It can also increase the working efficiency of the workers' because of its automatic mode. But its prime cost is high, which limits its large-scale industrialization application in fine layered water injection wells. the key part is the intelligent water distributor in the intelligent separate injection technology. the cost of electromagnetic flow meter accounts for 38% in the intelligent water distributor. the paper intends to research on flow measurement without flow meter to reduce the high-cost. the pressure difference equation was established for flow rate and the opening ratio of the nozzle using the dimensionless analysis method. the empirical parameters of the equation were obtained through laboratory experiments. the pressure sensors are used to monitor the pressure of water injection wells while achieving the measurement of layered flow rate. the laboratory experiments results indicate that the calculated flow rate obtained by this method coincides well with the standard flow rate. the relative error is less than 12%. then, it provides a new flow measurement method for intelligent injection tools. the most significant feature is that the flow meter is no longer mandatory for intelligent water distributors. Due to the cheaper cost that it provides a technical support for the large-scale industrialization application of intelligent separate injection technology in fine layered water injection wells. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Difference equations

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Energy Consumption Analysis of Flexible Train Composition in Urban Rail Transit 11th

Energy Consumption Analysis of Flexible Train Composition in...

引用

11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Ran, Xinchen Wei, Qinwen Zou, Qingru Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System Chongqing Jiaotong University Chongqing400074 China

ISBN: (纸本)9789819796434

this paper focuses on the energy consumption of urban rail transit trains with flexible train compositions. An energy-efficient speed profile model is proposed to calculate the motion state of various train types. Considering the traction energy consumption and regenerative braking energy, an energy consumption model is established to assess the total energy consumption under a given flexible train service plan and timetable. A set of numerical experiments based on real metro line data is conducted to analyze the energy consumption of different train operation scenarios. the results show that the energy consumption of the flexible train composition mode is reduced by 11.70% compared to the fixed train composition mode, highlighting its potential for enhancing energy efficiency and sustainability in urban rail transit systems. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Urban transportation

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741922

the proceedings contain 157 papers. the special focus in this conference is on Innovative Computing. the topics include: Proactive Auto Scaling Based on Marginal Request Change Analysis for Reducing Tail Latency in Kubernetes Cluster;Integration of Generative Adversarial Networks (GAN) and AI Drawing in Criminal Sketches—Applied in Crime Scene Investigation by Law Enforcement;implementation of a High Quality Cardiopulmonary Resuscitation Teaching Intelligence Training and Assessment System;Predicting Twitter Posts from Fake Accounts Using XGBoost Model;the Interplay Between Self-regulated learning and Programming learning Achievement;a Study on the Continued Viewing Intention of YouTuber Videos: Experiential Marketing, Social Capital, and Habit Perspectives;an Exploratory Study on the Design of Human-Computer Interaction Interfaces for Augmented Reality Integrated Museum Exhibitions;communication-Aware Optimization of Microservice Scheduling on Edge Computing;Design of Computer Model for Raw Material Supplier Selection Based on Entropy Weight TOPSIS Method;exploring How Cybersecurity Competitions Can Help Improve Students’ Practical Abilities in Cybersecurity;Adversarially Residual U2Net for COVID-19 Lung Infection Segmentation from CT Images;partnerships Analysis Based on Measurements of Common Research Interest;System Design of Operating Costs of intelligent Healthcare Services for the AHP Algorithm;a Multi-party Private Set Union Protocol Against Malicious Adversary;analysis of the Current Status of Research on Unmanned Collaborative Equipment Above and Below Water;data Mining Analysis of New Energy Vehicles Based on Cluster Analysis Technology;Two-Layer Minimum Variance FIR Filter-Based DPLL Design;fuzzy Proximity Two-Dimension Inductance Gesture Recognition System Analysis and Implement;proximity Capacitive Gesture Recognition for Recursive Neighbor Memory Neural Network;application of Genetic Algorithms in automated Mechanical Design.

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Integration of Industrial Automation and IoT Devices on the Example of the Assembly Station with the Astorino Educational Robot 4th

Integration of Industrial Automation and IoT Devices on the ...

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4th international conference on intelligent Systems in Production engineering and Maintenance, ISPEM 2023

作者： Ćwikla, Grzegorz Kalinowski, Krzysztof Kciuk, Marek Gold, Michal Waluśkiewicz, Marek Sladek, Michal Tlolka, Jaroslaw Woźnica, Robert Wlazlo, Aleksander Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18A Gliwice44-100 Poland Faculty of Electrical Engineering Silesian University of Technology Akademicka 10A Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783031442810

the article presents an automated assembly station designed by students as part of Project Based learning. the task of the project was to familiarize with the issues of integration of automation and control elements operating in various standards in order to implement the idea of Industry 4.0. the main elements of the station are the Astorino educational robot, gripper, servo drive and other professional elements of industrial automation and IoT systems. Raspberry PI, microcontrollers, etc. are also used. the assembly process of the product in the configuration in accordance with the user’s order is carried out at the station, quality control is carried out during the process using a vision system and various sensors. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data acquisition

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learning Optimization for Dispatch of Interregional Power Grid under Uncertain Environment 11

Learning Optimization for Dispatch of Interregional Power Gr...

引用

11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Tang, Hao Zhang, Yan Li, Xiaoqing Lv, Kai School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China Earth observation R & D Center The 38th Research Institute of China Electronics Technology Group Corporation Hefei China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this study investigates the dispatch problem in interregional power grids with uncertain renewable energy sources and loads. A knowledge-transfer-based hierarchical learning optimization method is designed to search for the optimal dispatch strategy. First, the model of various sources, loads, and tie-lines are established according to each components characteristics in the system. the dispatch process of an interregional power grid is correspondingly divided into tie-line dispatch in the upper layer and both generator units and flexible load dispatch in the lower layer. the dispatch optimization problems of both layers are formulated with a hierarchical Markov decision process model. Finally, a learning optimization method is adopted to obtain the dispatch strategy, in which a pre-learning technique is incorporated. A case study is performed to validate the effectiveness of the proposed method, and the simulation results show that the pre-learning technique improves the learning speed and reduces the cost of relearning. © 2022 IEEE.

关键词： Renewable energy resources

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Multi-objective Multigraph A∗ Search with learning Heuristics based on Node Metrics and Graph Embedding 11

Multi-objective Multigraph A∗ Search with Learning Heuristi...

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11th IEEE international conference on intelligent Systems, IS 2022

作者： Liu, Songwei Chen, Jun Weiszer, Michal University of London School of Engineering and Materials Science Queen Mary London United Kingdom

ISBN: (纸本)9781665456562

To solve general multi-objective multigraph shortest path problems, this paper proposes an algorithm (MOMGA*) that incorporates an online likely-admissible learning-based heuristic function to accelerate the solution-finding process. MOMGA∗ is an extended and generalised version of the airport multi-objective A∗ (AMOA*) algorithm that is tailored for a specific application problem. the online heuristic function is added and developed using artificial neural networks that estimate the costs between two nodes based on their metrics. To implement this metric-based prediction, a graph embedding technique is adopted to learn node feature representations. Results on a range of benchmark multi-objective multigraphs show that (i) in the absence of heuristic information, MOMGA∗ can deliver the same Pareto optimal solutions as AMOA∗ does, while requiring less computational time, and (ii) empowered by the likely-admissible learning- based heuristics, MOMGA∗ is able to provide a set of optimal and near-optimal solutions and strike a good balance between optimality and tractability. © 2022 IEEE.

关键词： Neural networks

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A Comparative Study of Rolling Bearing Fault Classification Using CWT-CNN and STFT-CNN Methods 1

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11th international conference on Industrial Tribology, IndiaTrib-2022

作者： Joseph, thomas Keerthi Krishnan, K. Sudeep, U. NSS College of Engineering Palakkad APJ Abdul Kalam Technological University Trivandrum695019 India NSS College of Engineering Palakkad678008 India

ISBN: (数字)9789819992645

ISBN: (纸本)9789819992638

Early fault detection (EFD) of rolling bearings is critical for the healthy and long operation of rotating machinery. In addition to EFD, fault classification is also important in the overall diagnosis of rolling bearing conditions. Many researchers have focused on time- and frequency-domain-based approaches for EFD. Recently, machine learning-based methods have gained priority in automatic fault feature extraction and classification. this work is focused on the effectiveness of two different methods for EFD and fault classification. the first method consists of combining Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN) for fault diagnosis and classification, whereas the second method uses a combination of Short-Term Fourier Transform (STFT) and CNN. Alexnet is a widely used CNN for image classification that provides superior results. In this work, Alexnet is used for the classification of images of vibration signals. Initially, vibration data is acquired from healthy bearings as well as bearings with various seeded defects on the races and balls. the CWT and STFT images are generated from the vibration data, and the image data is used for training and testing in CNN. An accuracy of 95.8% is obtained for the CWT and CNN combination and 92.2% for the STFT and CNN combination. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fault detection

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Analysis of Job Processing data - Towards Large Cloud Infrastructure Operation Simulation 11th

Analysis of Job Processing Data - Towards Large Cloud Infras...

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11th international conference on Big data Analytics in Astronomy, Science, and engineering (BDA)

作者： Wrona, Zofia Ganzha, Maria Paprzycki, Marcin Krzyzanowski, Stanislaw Warsaw Univ Technol Fac Math & Informat Sci Warsaw Poland Polish Acad Sci Syst Res Inst Warsaw Poland CloudFerro Sp Zoo Warsaw Poland

ISBN: (纸本)9783031585012;9783031585029

Cloud computing is the most popular way of delivering on-demand computational resources. Recently, the research in this area has started to focus on carbon-aware clouds. Here, the most challenging aspects are related to defining strategies for efficient task scheduling and resource allocation. these strategies can be simulated and assessed using dedicated tools. However, to perform their accurate evaluation, the tests should reproduce close-to-real conditions of the actual cloud center. In particular, they require running simulations with various mixtures of tasks that replicate the actual cloud center operation. therefore, the main aim of this work was to prepare tools that will allow the generation of synthetic job streams, with mixes of realistic types of computational tasks. the core of this contribution is the analysis of actual job processing data from the CloudFerro cloud center. the proposed methodology is based on data clustering and includes a comparison between multiple algorithms. Furthermore, the resulting clusters have been categorized from the point of view of cloud center operation, in order to identify prototypical tasks' classes with respect to the resource demands. Finally, a tool that generates synthetic job streams, based on the Gaussian Mixture Model, which has been implemented, is summarized.

关键词： carbon-aware computing cloud-based processing data clustering data augmentation K-Means

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