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10th IEEE data Driven control and Learning Systems conference, DDCLS 2021

作者： Wang, Xuemei Wu, Ping Lou, Siwei Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9781665424233

How to use existing technology to improve product quality while ensuring production stability and safety has become a hot topic in various fields. Being one of the most powerful multivariate analysis tools in process monitoring, canonical correlation analysis (CCA) is an exploratory statistical method that emphasizes the correlation between a set of poly dimensional data obtained at the same experimental level. For overcoming the drawbacks of CCA, a concurrent canonical correlation analysis (CCCA) modeling method was put forward, which decomposes the input and output variables into five segments. However, CCCA based quality-related process monitoring scheme would make ambiguous decisions due to the decomposition is incomplete. It is intended that this paper will explore an improved concurrent canonical correlation analysis (ICCCA) technique used for process monitoring. Specifically, a CCA model is established where the latent structure of process data is extracted. Subsequently, an algorithm of total principal component regression (TPCR) is implemented grounded on the established CCA model. Complete decomposition of the input and output variables into six parts was performed for quality-related process monitoring. Taking the Tennessee Eastman process (TEP) as an industry benchmark, the superiority of ICCCA is verified by comparing it with other monitoring methods. © 2021 IEEE.

关键词： process monitoring

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

43rd IBIMA Conference on Artificial intelligence and Machine...

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

ISBN: (纸本)9783031790850

The proceedings contain 62 papers. The special focus in this conference is on Artificial intelligence and Machine Learning. The topics include: Comprehensive analysis of Football Player Performance with WorldfootballR: data Insights and Key Variables;Decision Making process for a Sustainable Horticulture Using AI;drug Delivery System – The Construction and Use of the Graphical Interface in modeling the controlled Release of Drugs in the Human Body;Enabling German SMEs and Crafts Through data-Driven Innovation: Developing a Scoring Model and Chronological Framework for Enhanced Decision-Makin;Exploring the Impact of AI-Driven Gamification on Visitor Engagement in Museums: A Critical Review;framework for Automating Bank Transactions and Invoice Mapping Using Multi-criteria Optimization and Neural Networks;measurements of Clothing Circumference Using Vision Methods;mechatronic Mannequin as A Way to Reduce the Number of Returns in Fashion E-Commerce;ontological modeling of the Financial Intermediation Models for Creating a System of Texts’ Semantic analysis;optimizing Delivery Routes with Spatially Distributed Orders;review of Artificial Intelligence Models for Constructing a Sales Forecasting Module to Enhance Decision-Making;Stock Price Prediction Using Sentiment analysis and LSTM Networks;unveiling Online Shopping Quality: Taxonomy-Based Multi-label Classification of Shopping App Reviews;unveiling the Dynamics in Shopping Behaviors: A Review;control of Technical Parameters of Industrial Products Through Machine Vision;automatic Coffee Roasting process Assistance System Based on Fuzzy Logic Techniques;a Simulation Study on the Influence of Prioritized Multicast Connections on Traffic Effectiveness Network Nodes;Advantages of Introducing the DVB-T2 Standard in Terrestrial Television;consensus Mechanisms in Blockchain Networks;Self-optimizing SD-WAN.

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

43rd IBIMA Conference on Artificial intelligence and Machine...

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

ISBN: (纸本)9783031774928

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Multi-scale analysis for solidification of phase change materials (PCMs): Experiments and modeling

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2023年 212卷

作者： Xu, Minghan Hanawa, Yosuke Akhtar, Saad Sakuma, Atsushi Zhang, Jianliang Yoshida, Junichi Sanada, Masakazu Sasaki, Yuta Sasmito, Agus P. McGill Univ Dept Min & Mat Engn Montreal PQ Canada SCREEEN Holdings Co Ltd Kyoto Japan Kyoto Inst Technol Div Text Sci Kyoto Japan Polytech Montreal GERAD & Dept Math & Ind Engn Montreal PQ Canada

We present a multi-scale solidification framework for pure substances using laboratory experiments and mathematical modeling. The multi-scale phenomena of solidification are experimentally captured by the state-of-the-art thermal control chamber and optical devices. In particular, the transient temperature over five solidification stages is observed under various cooling rates, while the nucleation site and crystal evo-lution are photographed and further processed via image analysis. A unified mathematical model is also developed to quantitatively examine the solidification process at three scales: (i) the supercooling, freez-ing, and subcooling stages with a time-dependent boundary at the macroscale are analytically solved by Duhamel's theorem and perturbation method;(ii) the two-dimensional (2D) dendritic growth at the mesoscale is computed by the Allen-Cahn equation through phase field modeling;and (iii) the hetero-geneous nucleation at the microscale is numerically simulated. The results demonstrated that the ex-perimental data and modeling were in close agreement with the freezing curve, nucleation temperature, nucleation time, 2D crystal evolution, and freezing *** Copyright (c) 2023 Published by Elsevier Ltd. All rights reserved.

关键词： Solidification Multi-scale Phase change heat transfer Crystal growth Phase field Experiment Mathematical model

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Built in Power Status Detection Component Based on Deep Learning in Multi source data Acquisition Devices

Built in Power Status Detection Component Based on Deep Lear...

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Power, Electrical Engineering, Electronics and control (PEEEC), International conference on

作者： Yifeng Zhu Renxin Li Hualu Yuan Chengliang Zhang China Southern Power Grid Co. Ltd. EHV Transmission Company Guangzhou Guangdong China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

Today, multi-source data acquisition devices play an increasingly important role in industry, environmental monitoring and health surveillance. However, the long-term stable operation and reliability of these devices rely heavily on the stability and continuity of the built-in power supply. Therefore, it is particularly important to accurately and efficiently monitor and predict the status of power supplies. In this study, we propose a deep learning-based built-in power supply state detection method to automatically learn and extract key features of power supply performance from data collected by multiple sensors by designing and implementing an end-to-end neural network model. This paper combines a convolutional neural network (CNN) and a long short-term memory network (LSTM) in order to process time-series data and predict the immediate state of the power supply in real time. The system also contains an adaptive learning mechanism that automatically adjusts the prediction model according to environmental changes and equipment aging to maintain high sensitivity and accuracy of detection. Even in the case of $\mathbf{2 5 \%}$ data loss, the accuracy of the model remains around 93.5%, which indicates the robustness of the model in dealing with incomplete data. This study not only optimizes the power state detection of multi-source data acquisition devices, but also provides a valuable empirical foundation and theoretical support for the application of deep learning technology in the field of energy management, demonstrating its wide application prospects and practical value.

关键词： Deep learning Adaptation models Accuracy Power supplies Computational modeling data acquisition Power system stability data models Stability analysis Load modeling

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Motion control of Railway Machinery Room Inspection Robot Based on Deep Learning

Motion Control of Railway Machinery Room Inspection Robot Ba...

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Intelligent Technologies (ACOIT), Asian conference on

作者： Dehua Zhu Guoneng Shuohuang Railway Development Co. Ltd China

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In this study, the motion control process of the inspection robot in the railway machinery room is described, and the effective training and deployment of the deep learning model are used to achieve the ultimate goal. Firstly, after repeated comparative experiments, CNN was determined to be the core model of this study, and the advantages of the model in time series data and image data processing were used to develop the research objectives. In this paper, the cross-validation method is used to continuously optimize the network structure and hyperparameters and improve the generalization ability of the model under complex environmental conditions. In addition, mature attention mechanism and data augmentation technology are introduced to enhance the accuracy and robustness of the model’s prediction. Finally, the optimized CNN model is directly deployed to the embedded computing unit of the inspection robot system in the railway machinery room, so that it can collect equipment data in real time and realize important decisions such as effective fault prediction and remaining service life estimation. In order to ensure the reliability of decision-making, a special fault diagnosis module is designed in this paper, which can confirm the model output twice. It can be seen in practice that this method can significantly improve the maintenance efficiency and prolong the service life of the equipment.

关键词： Deep learning Training Computational modeling Time series analysis Inspection Predictive models Rail transportation data models Machinery Robots

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Comparison of Turbulence Approaches Modelling the Flow Around the NACA 0012 Profile

Comparison of Turbulence Approaches Modelling the Flow Aroun...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Soufinae Elouardi Bouchaib Radi Rabii El Maani Laidi Zahiri Laboratory of Modeling and Simulation of intelligent industrial systems (MS2IS) Hassan II University of Casablanca Higher Normal School of Technical Education Mohammedia Morocco Laboratory of Engineering Industrial Management and Innovation (IMII) FST Settat Hassan 1st University Settat Morocco Laboratory of Computer and Mathematical Process Engineering ENSA Khouribga Sultan Moulay Slimane University Khouribga Morocco

Turbulence refers to the instability of air movements in which the speed has a whirling character at all points. This is a phenomenon often observed and generates sometimes violent movements that can disrupt flight in the case of aerodynamic interactions with aircraft. Bodily accidents due to turbulence are numerous, but they can be avoided with good control and prediction of the fluid behavior around this structure. In order to do this, designers are increasingly using numerical modeling to boost these aircraft's efficiency. We carried out a numerical analysis of the two-dimensional transonic flow on a NACA 0012 airfoil using the ANSYS/FLUENT software in order to compare the capacity of different turbulence models to predict results that are similar to those of experimental studies. The results were compared to the provided experimental data in the high Reynolds Number range of 9.10 6 . Then, a comparison based on the RANS, LES and Hybrid approach in order to obtain a better control of the profile performance. We demonstrated that Spalart-Allmaras model of Dettached Eddy Simulation gave good results for unsteady flow over the wing.

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IFIP WG 5.7 International conference on Advances in Production Management Systems, APMS 2022

IFIP WG 5.7 International Conference on Advances in Producti...

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IFIP WG 5.7 International conference on Advances in Production Management Systems, APMS 2022

ISBN: (纸本)9783031164101

The proceedings contain 69 papers. The special focus in this conference is on Advances in Production Management Systems. The topics include: Digital Transformation in the Milk Production Chain;supplying School Canteens with Organic and Local Products: Comparative analysis;school Feeding and Family Farming: Partnership for the Generation of Employment and Income;simulation-Based Game Theoretical analysis of Japanese Milk Supply Chain for Food Waste Reduction;assessing Energy Efficiency in processes of the Agri-Food Sector: From Delivery of Natural Resources to Finished Products;technologies Used for Animal Welfare Monitoring;Managing Technological Obsolescence in a Digitally Transformed SME;expense and Revenue Factors of Smart Factories: analysis of the Economic Effects of Condition Monitoring;procurement 4.0: A Systematic Review of Its Technological Evolution;The Digital Twin Application for Micro-Tool Wear Monitoring with Open-Source CAD System;introducing a Fast Lane to Multi-Project Environments in Factories to Focus on Digital Transformation;business process Digitalization Tracking and Monitoring: An Heuristic Software-Based Approach;crowdsourced Sentiment-Driven process Re-design with SentiProMoWeb: Towards Enterprise Social Information Systems;digital Technologies as an Essential Part of Smart Factories and Their Impact on Productivity;KNOWO: A Tool for Generation of Semantic Knowledge Graphs from Maintenance Workorders data;digital Transformation in the Engineering Research Area: Scientific Performance and Strategic Themes;a Proposal of data-Driven and Multi-scale modeling Approach for Material Flow Simulation;distributed Optimization for Supply Chain Planning for Multiple Companies Using Subgradient Method and Consensus control;an Ant Colony Optimization with Turn-Around-Time Reduction Mechanism for the Robust Aircraft Maintenance Routing Problem;exploring a Commercial Game for Adoption to Logistics Training;towards Cognitive Intelligence-Enabled Manufacturi

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data-Driven modeling of Ground Effect For UAV Landing on a Vertical Oscillating Platform

Data-Driven Modeling of Ground Effect For UAV Landing on a V...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Binglin He Heng Zhang Baisheng Lai Song Liu Yang Wang School of Information Science and Technology ShanghaiTech University Shanghai China Alibaba Group Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Landing on a vertically oscillating platform poses a significant challenge for multi-rotor unmanned aerial vehicle (UAVs) due to the time-varying ground effect (GE). In this work, we formulated a data-driven GE dynamic model that accurately describes the complex interactions between UAVs and both stationary and oscillating platforms. Integrating this model with a feedforward controller effectively compensates for GE, resulting in improved landing performance. The proposed GE model elucidates the relationship between GE and factors such as UAVs’ velocity, throttle magnitude, and the motion of the landing platform. It highlights that the GE experienced during the landing process of UAVs is not only contingent on the current state but also related to past states. The resulting GE model is parsimonious and suitable for onboard computers with limited computational power, and its accuracy has been confirmed through a series of flight experiments. To demonstrate the effectiveness of the developed UAVs landing scheme, we compared our approach with robust control and internal model control methods. Experimental results indicate that the proposed landing strategy achieves faster and smoother landings, with at least a 22% improvement in smoothness and a 25% reduction in landing time.

关键词： Robust control Accuracy Computational modeling Germanium Autonomous aerial vehicles Real-time systems Stability analysis Vehicle dynamics Feedforward systems Intelligent robots

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modeling the process of Frequency Estimation and Stabilization in a Radio Engineering System

Modeling the Process of Frequency Estimation and Stabilizati...

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International Russian Automation conference (RusAutoCon)

作者： Olga Safaryan Irina Alferova Department of cybersecurity of information systems Don State Technikal University Rostov-ov-Don Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The article presents a mathematical model and results of numerical modeling of the process of estimating and stabilizing the frequency of simultaneously and independently operating generators in a radio engineering system. The developed model is based on assumptions about the independence of the processes of frequency deviation generated by each signal generator. In this case, in general, the law of distribution of the deviation of the frequency of each generator from the nominal value is unknown. Under these conditions, the approach to estimating and stabilizing the frequency of generators based on finding the maximum of the likelihood function is untenable. To obtain estimates of the frequency deviation of each of the generators, it is proposed to introduce hypotheses about the assumed law of distribution of frequency deviations of each of the generators. Based on these hypotheses, an ordered set of frequency deviation values is formed for each of the generators. Using a quadratic functional calculated as the sum of squared differences between the measured and generated values, the parameters of the frequency deviation distribution law for each of the generators are determined. Based on the obtained data, the current frequency value of each of the generators is estimated and its subsequent stabilization is performed. The results of mathematical modeling are presented, confirming the correctness of the theoretical results obtained and the main regularities noted.

关键词： Phase measurement Current measurement Estimation Signal generators Generators Frequency estimation Mathematical models Stability analysis Numerical models Frequency control

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