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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chaurasiya, Ruchi Yadav, Shakshi Malik, Swagata Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This project presents the design of an automated access control system that aims to enhance security through the use of an ultrasonic motion sensor and a live heartbeat check. This system makes use of an ultrasonic sensor which detects any motion within a certain radius directing a signal to the controller based on Arduino. The controller is able to communicate with a script written in Python that queries API for the pulse of the user in question. Using these parameters,the system compares the real-time heartbeat of the pulse taken against the thresholds set *** the parameters exceed the range,the control unit within the Arduino will trigger the servo to unlock the door. Another scenario the parameters are within range,the door will remain closed. Such a system is able to increase the control measures in biological information system as it enables people to open the door while maintaining their hands free. The outcome demonstrates that by combining simple hardware components with data processing techniques, a reliable and secure automated access system can be created with minimal manual intervention, improving security while ensuring ease of use. © 2025 IEEE.

关键词： Access control

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Machine Learning Approaches for process automation: A Case Study Using Q-Learning and Supervised Learning

Machine Learning Approaches for Process Automation: A Case S...

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

作者： S.M.A.K. Azad P Suresh R Sireesha Department of Electrical and Electronics Engineering Sri venkanteswara College of Engineering

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

process automation is critical in modern industries, providing systems for precise control of variables such as temperature, pressure, and flow. Traditional control methods like PID controllers require manual tuning and often struggle with nonlinear dynamic systems. This paper presents a comparative study of two machine learning techniques namely Q -Learning and Decision Trees applied to industrial process control, specifically temperature regulation. Through a Python-based simulation, we evaluate the effectiveness of both techniques in real-time optimization. The results demonstrate that Q-Learning continuously adapts to system changes, optimizing control strategies, while Decision Trees provide reliable predictions based on historical data, reducing error margins. Our study shows that machine learning has the potential to revolutionize process automation by minimizing human intervention, enhancing efficiency, and improving system robustness.

关键词： automation Q-learning Supervised learning process control control systems Real-time systems Temperature control Decision trees Optimization Tuning

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Application and optimization of networked battery maintenance mode in power system

Application and optimization of networked battery maintenanc...

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2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Duan, Xuesong Zhang, Jinjia Li, Yuhao Li, Hongchao Wang, Wei State Grid Henan Electric Power Company State Grid Zhenping County Power Supply Company 474250 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Traditional battery maintenance methods have some problems, such as low efficiency, difficult to find potential problems in time and accurately, which affect the stability and reliability of power system. This paper studies the application and optimization of networked battery maintenance mode in power system. By integrating sensor technology, network communication and data analysis, this mode realizes real-time monitoring and maintenance automation of battery state. Aiming at the challenges of lack of real-time and accuracy, optimization strategies such as deploying 5G base stations, adopting advanced algorithms and machine learning technologies are proposed. In addition, a big data analysis platform based on cloud computing is built, and machine learning and data mining are used to predict battery performance decline, and intelligent maintenance algorithms are developed. Experiments show that this mode improves voltage stability, capacity retention and internal resistance control, reduces maintenance cost and improves efficiency. These achievements provide theoretical and practical guidance for the stable operation of power system and efficient maintenance of battery. © 2025 SPIE.

关键词： Batteries Data transmission Resistance Data analysis Data modeling Machine learning Telecommunications Cloud computing Decision making Reliability

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Structure of control computing Facilities of Data Transmission Networks and Features of Dispatch Algorithm Programs

Structure of Control Computing Facilities of Data Transmissi...

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international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

作者： Anvar, Suleymanov Rahmatillo, Khudoyberdiyev Tashkent Institute of Irrigation and Agricultural Mechanization Engineers "National Research University" Kori Niyazov Str. 39 Tashkent Uzbekistan Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Amir Temur Avenue 108 Tashkent100084 Uzbekistan

ISBN: (纸本)9789819747832

A set of basic structural circuits of control computing facilities (CCF) of data transmission networks (DTN) is defined in the article, the main features of the CCF functioning process are examined, and two classes of algorithms of dispatch program, for which a classification is proposed that makes it possible to formalize the functioning process of CCF of DTN with a limited number of mathematical models are identified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data transfer

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Resource allocation and task scheduling for computing power aware networks

Resource allocation and task scheduling for computing power ...

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2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Peng, Guozheng Zhao, Chuanqi Chen, Lingying Wang, Lanruo Beijing100089 China State Grid Shandong Electric Power Research Institute Ji’nan250003 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

As an important cornerstone of future information and communication technology, the computing power network cleverly integrates cloud resources, third-party computing devices, and various heterogeneous computing resources scattered at the edge of the network through seamless fusion of widely covered network connections. This innovative architecture aims to break the boundaries of traditional computing models and achieve precise scheduling and global optimization of computing resources by building a highly collaborative computing network resource management system. This article proposes a resource allocation and task scheduling algorithm based on deep reinforcement learning (DRL), which simulates the human decision-making process and enables the system to autonomously learn and optimize strategies in a constantly changing computing network environment. This algorithm can not only effectively cope with large-scale and high dynamic computing power demands, but also accurately predict future resource trends, pre allocate resources in advance, and ensure smooth and efficient task execution. The experimental results show that the algorithm performs well in improving the utilization of computing power resources, reducing task execution latency, enhancing system stability and flexibility, and laying a solid foundation for the further development and application promotion of computing power networks. © 2025 SPIE.

关键词： computing power

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7th international Russian automation conference, RusAutoCon 2024

7th International Russian Automation Conference, RusAutoCon ...

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7th international Russian automation conference, RusAutoCon 2024

ISBN: (纸本)9783031824937

The proceedings contain 37 papers. The special focus in this conference is on Russian automation. The topics include: Mapping and Path Planning Methods for Highly Automated Vehicles in Agriculture;morphological Analysis and Synthesis Features of Technological processes;a Modern Method of Wireless control of Unguarded Railway Crossings;duties and Obligations of the Railway Staff Concerned When the Microprocessorized Contactless controlling Gauge Device Signal Is Triggered;integration of Automated Management Systems for Enterprises’ Transport and Technical Services;development of an automation Module for Planning Trajectories for Painting Aircraft Fuselage Elements;identification of Metal Sheets in the Flow, Based on the Marking Imprint, Using Neural Networks;study of the Performance of Adaptive Sensor Networks for Collecting and processing Thermoelectric Data;development and Research of a Cartographic Model for Municipal Planning as the Basis of an Intelligent Geoinformation System;BIM Visual Programming of Bionic Architecture Construction Using Dynamo and Revit;development of a Model and Algorithms for Trigger control of Technological processes of Resource Provision;system for Statistical Assessment of Means of controlling Engineers’ Qualification for Information Support to Manage the Recruiting process in Industry;smart Enumeration Technology;comparative Analysis of C-Band Conical Horn Antenna Sparse Structures Characteristics at Different Frequencies;software Implementation of Heuristic Methods of Optimization and Integration into a Cloud Service;computer System for Modeling Fluid Flow Around Bodies and Its Potential in Industry;the Effect of Regular and Irregular Segmentations on Characteristics and Charge Distribution Densities of Microstrip Lines;reinforcement Learning with External Teacher for Building Energy Management.

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Data-Driven Modelling and Predictive control for the Geometry and Mechanical Properties of Freeform Bending processes 11

Data-Driven Modelling and Predictive Control for the Geometr...

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11th Vienna international conference on Mathematical Modelling (MATHMOD)

作者： Ismail, Ahmed Boehm, Viktor Stebner, Sophie Kong, Linghao Volk, Wolfram Muenstermann, Sebastian Lohmann, Boris Tech Univ Munich Sch Engn & Design Chair Automat Control Boltzmannstr 15 D-8570 Garching Germany Tech Univ Munich Sch Engn & Design Chair Met Forming & Casting Walther Meissner Str 4 D-85748 Garching Germany RWTH Aachen Unviers Integrityy Mat & Struct Intzestr 1 D-52072 Aachen Germany

Freeform bending is a technique used to bend different tube profiles into complex structures. In the realm of Aritificial Intelligence and Machine Learning, data-driven approaches in modeling and in optimization based control contribute to the job being executed, by increasing product quality and reducing energy consumption and material waste. In previous works, soft-sensors as well as different factors affecting the geometry and the residual stresses have been investigated and been utilized in a preliminary closed-loop control structure. Later on, this control structure has been replaced with a control strategy called Residual Strategy Algorithm, that could, based on previous deviations in the geometry, proactively react in order to reduce their effects. In this paper, the previously developed Residual Strategy Algorithm is extended to include both, the geometry as well as the residual stresses of the tube being bent. Also the modelling of both will be discussed altogether. The validation of the algorithm is done using simulation results. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Data-Driven Modelling Predictive control Free-form bending Residual Stresses Curvature process Optimization Soft sensors Closed-loop control Feed-forward control Gaussian process Regression Neural Network Artificial Intelligence Machine Learning Cyber Physical Production Systems

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Online Model-Adaptation for an Uncertain Liquid-Liquid Mixer System 11

Online Model-Adaptation for an Uncertain Liquid-Liquid Mixer...

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11th Vienna international conference on Mathematical Modelling (MATHMOD)

作者： Topalovic, Daniel Bliatsiou, Chrysoula Villwock, Joern Kraume, Matthias Knorn, Steffi Tech Univ Berlin Chair Control D-10623 Berlin Germany Tech Univ Berlin Chair Chem & Proc Engn Marchstr 23 Berlin Germany Ackerstr 76 D-10587 Berlin Germany

In this paper, a practical modeling approach for a batch-operated, stirred reactor is investigated that shows significant deviations in system behavior to the same actuation applied over multiple batches. Starting from an easily identifiable, sub-optimal, linear dynamical model to describe the basic dynamic behavior of the system, an augmented model reformulation is defined. This is used to model observed, nonlinear drift effects and static differences that can be seen in the system response over multiple batches, which can finally be estimated using an extended Kalman filter (EKF). The quality of this adaptive model approach that can be used in an online fashion is further assessed using a prediction error for all performed experiments. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： identification adaptation parameter estimation process estimation chemical industry

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A multi-threaded parallel iterative greedy algorithm for distributed flowshop group scheduling problems with preventive maintenance

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international JOURNAL OF automation AND control 2025年第3期19卷 265-286页

作者： Sun, Xiaobin Sang, Hongyan Wu, Wanzhong Zhao, Yasheng Han, Qiuyang Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China

In actual production, factories not only pursue productivity, but also pay attention to the reliability and stability of the production process. For the continuity of production machines, this paper investigates the distributed flow shop group scheduling problem with preventive maintenance (DFGSP/PM). In order to minimise the makespan, a mathematical model of DFGSP/PM is developed and a multi-threaded parallel iterative greedy (MPIG) algorithm is proposed. A greedy NEH (GNEH) method is designed to generate the initial solution. In order to couple the two subproblems of DFGSP/PM, a two-stage destruction and reconstruction is designed. A multi-threaded parallel local search strategy (MPLS) is introduced to improve the search efficiency of the MPIG, so that the optimal insertion positions of the groups in the sequence can be searched faster and the two subproblems can be coupled effectively. Effectiveness analysis has demonstrated that the proposed MPIG significantly reduces computation time and expands the search space.

关键词： distributed flowshop scheduling preventive maintenance iterative greedy algorithm group scheduling parallel computing

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Layered consensus mechanism of comprehensive value based on primary-secondary chain in industrial edge computing environment

Layered consensus mechanism of comprehensive value based on ...

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2024 international conference on Computer Application and Information Security, ICCAIS 2024

作者： Zhang, Juanjuan Liu, Zhongbo Wei, Min Luan, Yan China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781510689312

In the open industrial network environment, in order to ensure the safety and reliability of the accessed industrial devices, blockchain technology is introduced into the identity authentication mechanism in the industrial edge computing environment, which realizes the trusted authentication of the industrial devices through decentralization and consensus. With the proliferation of the number of accessed devices, the authentication process will cause a large amount of communication consumption and computational load, which seriously affects the availability of authentication. Therefore, how to safely and efficiently realize the consensus communication and storage consumption is large is the current problem that needs to be solved urgently. In order to solve the above problems, this paper designs a hierarchical PBFT consensus mechanism based on primary-secondary chain with integrated value to elect consensus nodes. For the problem of large PBFT consensus communication and storage consumption under the primary-secondary chain structure, the reputation value and performance value of the edge nodes are considered comprehensively in this mechanism, and the comprehensive value is calculated by using the triangular fuzzy hierarchical analysis method, and the high-scoring edge nodes are selected as the consensus nodes. Finally, the mechanism is verified and analyzed by the constructed test system. The results verify that the proposed consensus mechanism can reduce the communication consumption of the consensus process. © The Authors.

关键词： Edge detection

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