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

Energy Consumption Analysis of Flexible Train Composition in...

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

引用

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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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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Automation of Prediction Method for Supervised learning 11

Automation of Prediction Method for Supervised Learning

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11th international conference on Cloud Computing, data Science and engineering (Confluence)

作者： Singh, Irijendra Jaiswal, Rashi Univ Lucknow Dept Comp Sci ICT Res Lab Lucknow Uttar Pradesh India

ISBN: (纸本)9781665414517

data science helps to build the prediction model for decision making through data mining techniques. data Scientists require more time and effort in algorithm selection and data preparation for obtaining more effective results. At the present, a number of existing methods are available for the prediction purpose through supervised learning and unsupervised learning which are based on manual processes. this paper proposed the method for the automation of the prediction process for supervised learning, using prediction type selection methods with the data cleaning process through computing the missing value. It has been illustrated by examples with experimental analysis. this proposed automation methodology makes competitive prediction performance with less effort than the non-automated process.

关键词： data science supervised learning data mining decision making

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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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Construction of an intelligent Teaching Information Management Cloud Platform Based on Mobile Information Technology 11th

Construction of an Intelligent Teaching Information Manageme...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Li, Qian Liu, Kai Chen, Xiaoning Finance office Shandong Institute of Commerce and Technology Shandong Jinan250031 China

the proposal of student teaching information management conforms to the requirements of the times, which makes cloud platform technology and internet information processing particularly important. In order to keep up with the needs of the public, education and teaching management needs in-depth research. the analysis of student teaching information management can improve the monitoring of students' learning ability and physical and mental health from multiple aspects, contributing to providing a good student talent environment. this article introduced the design of a student teaching information management cloud platform based on mobile internet. this platform utilized cloud computing technology and mobile internet technology to achieve online management and sharing of student teaching information. the platform has functions such as user management, course management, grade management, and online communication, making it easy to manage and analyze students' teaching information, improving the quality and efficiency of school teaching. this article applied experimental and comparative methods to analyze the performance and functionality of the teaching information management system. the experimental data showed that the accuracy of the score query function in the system ranked first, reaching 99.92%, and the availability was 99.54%. © 2024 the Authors. Published by Elsevier B.V.

关键词： Cloud platforms

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data Security Access Control Based on Blockchain and Smart Contracts 23

Data Security Access Control Based on Blockchain and Smart C...

引用

11th international conference on Computer and Communications Management, ICCCM 2023

作者： Yan, Xiaoxue Chen, Meijuan Zhang, Yangxin Pan, Fan School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798400707735

With the development of the Internet and edge computing technology, many industrial Internet applications have emerged, followed by a large amount of data in the Industrial Internet. How to securely store and share this data has become a hot topic in current research. Firstly, a blockchain and IPFS based on-chain and off-chain storage architecture is proposed, which detects data through machine learning algorithms and stores data without anomalies. then, the roles of users were classified and various smart contracts were designed for registering users, deleting users, and managing roles and their permissions. Finally, an experimental platform was built through open-source software to verify the feasibility of the proposed scheme and realize the safe storage and sharing of normal data. © 2023 ACM.

关键词： Smart contract

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Design and Development of a Transformer Condition Diagnosis System Application to Predict Purification Time and Life of a Transformer Based on Machine learning 11

Design and Development of a Transformer Condition Diagnosis ...

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Bukit, Ferry R.A. Zulkarnain, Hendra Putra, Andre Yuanda Universitas Sumatera Utara Department of Electrical Engineering Medan Indonesia

ISBN: (纸本)9798350355314

Transformers are equipment in high-voltage engineering that often experience abnormalities originating from internal or external sources. this abnormality causes disturbances, so it is necessary to diagnose faults in the transformer as early as possible so that fault predictions and maintenance actions can be carried out to maintain the quality of work on the transformer. An application was designed to diagnose the transformer's condition and predict purification time and transformer life to maintain the quality of the transformer's work. this application is designed to help make it easier to predict transformer conditions using dissolved gas analysis test results, oil scores, furan, and tap changer. the application design is done using the Python programming language and then converted into a mobile app using the React Native library framework with a simulator using Expo and connected to a database to store user data. the predicted data is for the GT 2.1 Excitation transformer, GT 2.1 Main Transformer, and GT 2.1 UAT transformer. Based on the application design, the test results were 100% successful;the system validated all the tests from the ten tests. the GT 2.1 Excitation transformer oil purification time is predicted to be 5697 days since the last test, and the transformer lifetime is predicted to be more than 15 years. the GT 2.1 Main Trafo transformer oil purification time is predicted to be 2697 days since the last test, and the transformer lifetime is predicted to be more than 15 years. the GT 2.1 Excitation transformer oil purification time is predicted to be 1866 days since the last test, and the transformer lifetime is predicted to be more than 15 years. © 2024 IEEE.

关键词： Electric transformer testing

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PirouNet: Creating Dance through Artist-Centric Deep learning 11th

PirouNet: Creating Dance Through Artist-Centric Deep Learnin...

引用

11th international conference on Arts and Technologies Interactivity and Game Creation

作者： Papillon, Mathilde Pettee, Mariel Miolane, Nina Univ Calif Santa Barbara Dept Phys Santa Barbara CA 93106 USA Lawrence Berkeley Natl Lab Berkeley CA USA Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA USA

ISBN: (纸本)9783031289927;9783031289934

Using Artificial Intelligence (AI) to create dance choreography is still at an early stage. Methods that conditionally generate dance sequences remain limited in their ability to follow choreographer-specific creative direction, often relying on external prompts or supervised learning. In the same vein, fully annotated dance datasets are rare and labor intensive. To fill this gap and help leverage deep learning as a meaningful tool for choreographers, we propose "PirouNet", a semi-supervised conditional recurrent variational autoencoder together with a dance labeling web application. PirouNet allows dance professionals to annotate data with their own subjective creative labels and subsequently generate new bouts of choreography based on their aesthetic criteria. thanks to the proposed semi-supervised approach, PirouNet only requires a small portion of the dataset to be labeled, typically on the order of 1%. We demonstrate PirouNet's capabilities as it generates original choreography based on the "Laban Time Effort", an established dance notion describing a given intention for a movement's time dynamics. We extensively evaluate PirouNet's dance creations through a series of qualitative and quantitative metrics, validating its applicability as a tool for choreographers.

关键词： Deep learning Neural network Semi-supervised Generative Recurrent LSTM VAE Pose Laban

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11th EAI international conference on Game theory for Networks, GameNets 2022

11th EAI International Conference on Game Theory for Network...

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11th EAI international conference on Game theory for Networks, GameNets 2022

ISBN: (纸本)9783031231407

the proceedings contain 25 papers. the special focus in this conference is on Game theory for Networks. the topics include: Design and Implementation of Targeted Poverty Alleviation System Based on Blockchain Network;an Intent-Based Routing Scheme in Satellite IoT;interference Management in Terrestrial-Satellite Networks Using Stackelberg Game;optimal Resource Allocation for Computation Offloading in Maritime Communication Networks: An Energy-Efficient Design via Matching Game;game theoretic Analysis of Resource Allocation in Multi-tiered Networks;Energy-Efficient Multi-cell NOMA Design via Coalition Formation Game;the Vaccination Game in Susceptible-Infected-Susceptible (SIS) Networks with Multipopulations;a Stochastic Bandwidth Scanning Game;port Capacity Leasing Games at Internet Exchange Points;multi-service Communication Isolation of Underground Pipe Gallery Based on WiFi6;a Phase Transition in Large Network Games;dynamic Pricing for Tenants in an automated Slicing Marketplace;service Function Chain Placement in Cloud data Center Networks: A Cooperative Multi-agent Reinforcement learning Approach;the Art of Concession in General Lotto Games;providing Slowdown Information to Improve Selfish Routing;budgeted Adversarial Network Resource Utilization Games;MU-PDR: A Method of Fingerprint Passive Positioning for WiFi6 Based on MU-RTS/CTS;the Optimal Layer of User-Specific Reconfigurable intelligent Surfaces Structure for Uplink Communication System;a Sophisticated Anti-jamming Strategy for a Joint Radar and Communication System;power data Credible Decision-Making Mechanism Based on Federated learning and Blockchain;a Service Protection Mechanism of Deterministic Networking Based on Segment Routing;cloud-Edge Collaboration Based Power IoT Scene Perception Mechanism;task Allocation Mechanism of Power Internet of things Based on Edge Routing Optimization.

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