The proceedings contain 73 papers. The special focus in this conference is on Electrical Engineering and Information Technologies for Rail Transportation. The topics include: Electromagnetic Wind Energy Harvester for ...
ISBN:
(纸本)9789819993062
The proceedings contain 73 papers. The special focus in this conference is on Electrical Engineering and Information Technologies for Rail Transportation. The topics include: Electromagnetic Wind Energy Harvester for Condition Monitoring System of High-Speed Train Bogies;an Exploration of Voltage-Type Rotor Magnetic Field Indirect Vector control;method to Detect Arc Across Pantograph-Catenary Structure Atop Train Based on Frequency Features of Entry Current;a Foreign Object Detection Method for Railway Overhead Lines Based on Few-Shot Learning;transformer-Aware Graph Convolution Networks for Relation Extraction of Railway Safety Risk;a Comprehensive Study on Train Operation Dynamics and Passenger Comfort Optimization;model-Driven Study of Intelligent Passenger Information System for Urban Rail Transit;research on Positioning of Permanent Magnet Maglev Trains Based on Weighted Adaptive Kalman Information Fusion;Real-Time Low-Light Image Enhancement Method for Train Driving Scene Based on Improved Zero-DCE;analysis of Dynamic Characteristics of Dropper in Catenary System;a Power Flow Optimization Method for Urban Rail Flexible Traction Power Supply System Considering Train Dwell Time;a Mechanism and data-Driven Hybrid Mechanical Model of Rotary Arm Positioning Rubber Joint;high Speed Train Bracket Arm Visualization Experiment System;design of Cabinet-Level Refrigeration System in Subway Station Communication Signal Room;active control of Pantograph Sliding Mode Under Fluctuating Wind Excitation;Time Synchronized Sensor Network with IEEE1588 for Vibration Measurement in Structural Health Monitoring of Railway System;modeling and Implementation of EMU Traction System;traffic Operation Status Research Based on Multi-source data Fusion;isochronous Deterministic Ethernet System Research;wave Propagation in the Overhead Conductor Rail System.
The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to prote...
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The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber and physical components, and a safety breach in the element is central to catastrophic consequences. Cyberattack recognition and mitigation techniques in CPSs include using numerous models like intrusion detection systems (IDSs), access control mechanisms, encryption, and firewalls. Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. Thus, the study proposes an automated Cyberattack Detection using Binary Metaheuristics with Deep Learning (ACAD-BMDL) method in a CPS environment. The ACAD-BMDL method mainly focuses on enhancing security in the CPS environment via the cyberattack detection process. The ACAD-BMDL method uses Z-score normalization to scale the input dataset. In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. Furthermore, the Archimedes Optimization Algorithm (AOA) model is employed to select the optimum hyperparameter for the EESNN model. The empirical analysis of the ACAD-BMDL technique is performed on a benchmark dataset. The experimental validation of the ACAD-BMDL technique portrayed a superior accuracy value of 99.12% and 99.36% under NSLKDD2015 and CICIDS2017 datasets in the CPS environment.
Froth flotation is a widely used technique for mineral separation, but it poses significant challenges for modeling due to the complex phenomena that happen during the process. To account for the implementation of opt...
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ISBN:
(数字)9798331517212
ISBN:
(纸本)9798331517229
Froth flotation is a widely used technique for mineral separation, but it poses significant challenges for modeling due to the complex phenomena that happen during the process. To account for the implementation of optimization and control-related studies in flotation systems, this work deals with the development of a digital twin or surrogate to the original model for the froth flotation system. Initially, a first-principles, three-phase model having 61 equations (7 Ordinary Differential Equations and 54 nonlinear equations) was considered and implemented. The model consists of four inputs, a set of initial conditions for feed, one primary output, and sixteen intermediate outputs. 15,000 time-series data were sampled from the physics-based model to initiate the data-driven modeling. The well-known recurrent neural network variants, namely RNN, LSTM, and GRU, were developed as potential surrogates for the three- phase model by following a multi-input and single-output approach. On performing a comparison study for all the output predictions on test data, it was found that GRU outperforms LSTM and RNN for a majority of the outputs (9 out of 17), especially for highly dynamic ones. Also, GRU consists of 25% fewer model parameters than LSTM networks, which thereby decreases the time for training and reduces the load on the optimizer. Overall, this study provides a deep understanding of froth flotation dynamics and establishes the efficacy of advanced neural network architectures in enhancing predictive modeling in mineral separation processes.
A major issue for project management, besides handling schedules and deadlines, is the process of finding and extracting the most relevant information from a variety of different software solutions used by the differe...
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ISBN:
(纸本)9781510675216;9781510675223
A major issue for project management, besides handling schedules and deadlines, is the process of finding and extracting the most relevant information from a variety of different software solutions used by the different stakeholders. This often leads to enormously large Excel sheets that try to identify the most up-to-date versions of the documents needed, which are extremely time-consuming to maintain, inefficient for finding inf ormation, and prone to err ors. The contemporary methodology we introduce represents a paradigm shift in project management, eschewing the traditional model of isolated databases for documents, such as requirements, CAD data, and Gantt chart schedules. Instead, we propose an integrated database architecture that consolidates all project management needs into a single, user-friendly repository. This, coupled with a web-based interface, facilitates the retrieval of relevant information in a straightforward and dependable manner. Illustrated through the case study of ELT-MICADO, we present the implementation of this strategy using Siemens Teamcenter, an industry standard software solution that is adapted to our specific needs. This exposition is not intended as an endorsement of the product but rather as an exemplification of one potential solution. It is acknowledged that alternative software solutions may offer comparable functionality and performance.
This paper proposes an online data splicing power flow analysis method based on sensitivity analysis. Since the step-down transformer in the whole network data is usually equivalent to the 220 kV side, the equipment b...
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This paper proposes an online data splicing power flow analysis method based on sensitivity analysis. Since the step-down transformer in the whole network data is usually equivalent to the 220 kV side, the equipment below 220 kV is not modeled in detail, and the provincial network data is modeled to 110kV and 35 kV voltage level equipment in detail. Different levels of modeling have a greater impact on the accuracy of stability analysis and calculation. Therefore, the purpose of improving data accuracy is achieved by splicing the entire network data (external network) and provincial network data (internal network). This paper is based on the sensitivity analysis method to stabilize the tie-line power by adjusting the output of the external network units, and form the whole network data that can accurately analyze the stability characteristics of the provincial network. The problem of power flow deviation caused by the voltage and angle deviation of the nodes at both ends of the regional tie line during the data splicing process is solved. Through the actual application of splicing the entire network and Hunan Provincial Network data, the effectiveness and necessity of the proposed method have been verified. (C) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
This research introduces a novel approach to software development risk management using machine learning. This approach is based on the analysis of historical data from previous software projects to predict and mitiga...
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ISBN:
(数字)9798350364507
ISBN:
(纸本)9798350364514
This research introduces a novel approach to software development risk management using machine learning. This approach is based on the analysis of historical data from previous software projects to predict and mitigate the risks in future software development efforts. This machine learning model considers various factors such as the size of development team, project complexity, software budget, and the other required factors to provide more accurate risk assessments and support the decision-making process to achieve an acceptable level of software quality development. The implementation and benefits of using a machine learning model for software development risk management are discussed, including improved efficiency, reduced risk, and increased predictability. The model is trained and showed good results. This article also highlights the potential challenges and limitations of the proposed approach and suggests areas for future research and improvement.
The proceedings contain 104 papers. The special focus in this conference is on Computer Aided Systems Theory. The topics include: Influence of Spike Encoding, Neuron Models and Quantization on SNN Perfo...
ISBN:
(纸本)9783031829598
The proceedings contain 104 papers. The special focus in this conference is on Computer Aided Systems Theory. The topics include: Influence of Spike Encoding, Neuron Models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture Models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-processing of Myoelectric Signals for the control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine Through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, Therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy Models in Digital Production Environments;efficient Classification of Live Sensor data on Low-Energy IoT Devices with Simple Machine Learning Methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude data Encoding;quantitative Trend analysis of Reinforcement Learning Algorithms in Production Systems;Using AutomationML for Advanced Simulation in Industrial Automation;AR Digital Twin Demonstrator for Industrial Robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -
The use of renewable energy sources is becoming an important direction of energy development in many countries. Currently, the most common generation is based on the use of solar and wind energy, which, although it ha...
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ISBN:
(数字)9798331517564
ISBN:
(纸本)9798331517571
The use of renewable energy sources is becoming an important direction of energy development in many countries. Currently, the most common generation is based on the use of solar and wind energy, which, although it has all its advantages, has one major disadvantage - its variable nature. In this regard, there is a need to assess the energy potential of the incoming energy: both theoretical and technical, which allows taking into account specific technological parameters of the energy converters ***, this paper considers the process of constructing a simulation model of a wind turbine generator in the SimInTech environment, which allows taking into account the real technological characteristics of the converting device, as well as using real historical data sets (such as wind speed), which makes it possible to realize the evaluation of the technological potential of wind energy. This is one of the necessary steps in determining the composition of a renewable energy system.
The article presents the results of an experimental study of the transfer characteristics of a microjet logic element "I" with a characteristic size of 400 microns for three operating modes: 1, 2 – informat...
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ISBN:
(数字)9798331517564
ISBN:
(纸本)9798331517571
The article presents the results of an experimental study of the transfer characteristics of a microjet logic element "I" with a characteristic size of 400 microns for three operating modes: 1, 2 – information signals (arguments) are fed to one of the logical inputs of the element; 3 – information signals are fed to both logical inputs. The results of a comparative analysis for two types of experimental data are also presented: 1) data obtained using a physical (full-scale) experiment; 2) data obtained using a computational experiment.
Aiming at the model of underwater vehicle, a magnetic field feature fusion method based on principal component analysis (PCA) is proposed, focusing on: Non-stationary, nonlinear and non-Gaussian magnetic field time do...
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