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arXiv 2024年

作者： Chakraborty, Kushal Dutta, Aritra Kumar Hussain, Mohammad Avesh Mohiuddin, Syed Raafay Choudhury, Nikumani Matam, Rakesh Mukherjee, Mithun Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Hyderabad Pilani India Dept. of Computer Science & Engineering Indian Institute of Information Technology Guwahati India Nanjing University of Information Science and Technology Nanjing China

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the Time Slotted Channel Hopping mode of IEEE 802.15.4e) as an enabling technology facilitates low-power and low-latency communication between IoT devices in industrial environments. The Routing Protocol for Low power and lossy networks (RPL), which is used as the de-facto routing protocol for 6TiSCH networks is observed to suffer from several limitations, especially during congestion in the network. Therefore, there is an immediate need for some modifications to the RPL to deal with this problem. Under traffic load which keeps on changing continuously at different instants of time, the proposed mechanism aims at finding the appropriate parent for a node that can forward the packet to the destination through the least congested path with minimal packet loss. This facilitates congestion management under dynamic traffic loads. For this, a new metric for routing using the concept of exponential weighting has been proposed, which takes the number of packets present in the queue of the node into account when choosing the parent at a particular instance of time. Additionally, the paper proposes a parent selection and swapping mechanism for congested networks. Performance evaluations are carried out in order to validate the proposed work. The results show an improvement in the performance of RPL under heavy and dynamic traffic loads. © 2024, CC BY.

关键词： Internet of things

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Model-free Optimal control of a Thin-Walled Rotating Cylinder with Piezoelectric Patches using a Particle Swarm Optimization Approach

Model-free Optimal Control of a Thin-Walled Rotating Cylinde...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Amir Hatav Ziv Brand Matthew O.T. Cole Dept. of Mechanical Engineering Control Research Center Shamoon College of Engineering Be'er Sheva Israel Dept. of Mechanical Engineering Center for Mechatronic Systems and Innovation Chiang Mai University Chiang Mai Thailand

This paper describes a computational investigation on the use of a standard Particle Swarm Optimization (PSO) algorithm to design a model-free optimal feedback control law based only on input-output data. The considered case study involves vibration suppression of a thin-walled rotating cylinder with collocated piezoelectric actuator/sensor patches. To assess the optimization algorithm performance, comparisons were made with a model-based optimal feedback control law for linear quadratic regulation, obtained by solving the associated algebraic Riccati equation. The simulations include examining the effect of different rotational speeds and parameter uncertainty. The results show a good match between the PSO-based feedback control law and the model-based optimal feedback control law. At the same time, an informed choice of parameter ranges for the PSO algorithm should be made.

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Study of the Shock Loads Effect on the Operation of Multi-Motor Electric Drives of a Rolling Mill with a Second-order Elastic Coupling 5

Study of the Shock Loads Effect on the Operation of Multi-Mo...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Tytiuk, Valerii Kalinin, Alexey Druzhinin, Valeriy Kuznetsov, Vitalii Chornyi, Oleksii Busher, Victor Kryvyi Rih National University Department of Electromechanics Kryvyi Rih Ukraine Abylkas Saginov Karaganda Technical University Npjsc Department of Informative Computer Systems Karaganda Kazakhstan Karaganda Industrial University Npjsc Department of Power Engineering Temirtau Kazakhstan Ukrainian State University of Science and Technologies Department of the Electrical Engineering Dnipro Ukraine National University Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrogradskyi Kremenchuk Ukraine National University "odessa Maritime Academy" Dept. of Electrical Engineering and Electronics Odessa Ukraine

ISBN: (纸本)9798350359787

This paper presents a study for optimizing a hot rolling mill. The paper is devoted to the study of ways to reduce the effect of shock loads of synchronous electric drives of the roughing mill group on the relative lengthening of the metal strip in the inter-stand gap of the rolling stands of the finishing mill group. The elasticity of the metal strip in the inter-stand gap of the rolling mill has a significant impact on the movement of intercoupled multi-motor electric drives of the finishing mill group. Another crucial route of exposure that influences the motion of the electric drives within the finishing mill group involves the electromagnetic connection between the drives in the roughing and finishing mill groups. The impact of the electromagnetically connected circuits is evident through the effect of shock loads from synchronous electric drives on the angular velocity of the direct current electric drives within the finishing mill group. The characteristics of the electromagnetic communication circuit are established by the power grid arrangement and equipment specifications. To assess the quality of work of electric drives of the finishing mill group, it is proposed to use the relative lengthening of the metal strip in the inter-stand gap, which is an important indirect indicator of the quality of rolling. The mathematical model of a hot rolling mill that takes into account both the elasticity of the metal strip that connects the DC electric drives of the finishing group, and the electromagnetic coupling circuit that takes into account the impact loads of synchronous electric drives of the roughing group on the example of the mill 1700 of ArcelorMittal Temirtau JSC was proposed in this paper. Using the mathematical model developed in Simulink software using the methods of planning the experiment, regression models were obtained to establish the dependence of the parameters of overexcitation of a synchronous motor on the magnitude of the voltage sag in the no

关键词： Electric loads

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Implementation of Predictive control System with PID-Algorithm and Autotuning Module Using Owen PLC

Implementation of Predictive Control System with PID-Algorit...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Elena Merzlikina Georgiy Sviridov Georgiy Farafonov Dept. of Automated Control Systems for Thermal Processes Moscow Power Engineering Institute (National Research University) Moscow Russia

This paper considers in general the problem of control system performance improvement, and, particularly, using predictive control systems. Various ways of improvement are described and the predictive system is considered in detail. This system is implemented in a laboratory workbench equipped with a physical model of an electric heater and a modern PLC. The system consists of a PID-algorithm, a prediction module and an auto-tuning module. The prediction algorithm is linear. The auto-tuning module allows to refresh the plant model and to re- tune the PID-algorithm if necessary. The possibility to implement the control system described using a modern PLC is estimated and the performance of this system is analyzed. Influence of the prediction time value is analyzed and recommendation on the prediction time value selection is given.

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Partial Discharge Classification With 1D Convolutional Neural Network

Partial Discharge Classification With 1D Convolutional Neura...

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International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： Thepjit Cheypoca Wiboon Promphanich Aung Ye Thway Angelina Phimpissada Hankae Siwakorn Jeenmuang Norasage Pattanadech Instrumentation and Control Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Computer Engineering Dept. School of EngineeringKing Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Mechatronics Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350374605

ISBN: (纸本)9798350386165

This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed.

关键词： Partial discharges Partial discharge measurement Surface discharges Neurons Training data Discharges (electric) Data models Convolutional neural networks Integrated circuit modeling Testing

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Jumping behavior Analysis after Identification of Daycare Children's Activities utilizing YOLOv8 Algorithm

Jumping behavior Analysis after Identification of Daycare Ch...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Tiya Bisla Ritik Shukla Mansi Dhawan Md. Rahatul Islam Keiichi Horio Dept. of Computer Science & Engineering Dronacharya Group of Institutions Greater Noida India Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Monitoring human activities to ensure the safety and welfare of humans is essential in the present age of mindfulness. Human Activity Recognition (HAR) offers a technical method to detect and identify human actions in real-time. We used YOLOv8s, a deep learning system, to recognize a kindergartener's behaviors and positions. After recognizing the student's movements, an assessment determines jumps per second and jump duration. We utilized our dataset, named Kid Action Recognition (KAR). The KAR (Kid Action Recognition) dataset consisted of 6489 photos. We manually labeled all photos using ***. We analyzed and trained the data in a Google Colaboratory setting with an A-100 GPU. The model demonstrates an overall accuracy of 88.7% and an F1 score of 86.6%. The recall score is 89.5%, and the precision is 83.9%. The model excelled in all parameters and can effectively identify student activities, making it suitable for potential use in institutions such as daycare centers and schools.

关键词： Training Obesity Accuracy Prevention and mitigation Graphics processing units Real-time systems Internet

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AutoMediVision:A Deep Learning System for Accurate Pharmaceutical pill identification

AutoMediVision:A Deep Learning System for Accurate Pharmaceu...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： G Rajeswarappa B. Venkata Sivaiah M. Navya Y. Lasya Shaik Mohammad Sameer V. Varun Kumar Department of CSE G Pulla Reddy Engineering College Kurnool Dept. of Data Science School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India Dept. of Computer Science and Systems Engineering(CSSE) Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The goal of this project is to build a high performance system that offers prediction of pill information from images, the pill name, medical use and recommended dosage by using MobileNet which is a lightweight and efficient deep learning model. This system serves as an important tool for healthcare professionals and patients as it offers a means of reducing risk because of the large number of medications out there. For this task, MobileNet was chosen for its high accuracy and good balance between computational efficiency and high accuracy, and hence can be deployed on mobile devices. Having trained the model on a wide variety of pills with class wise annotations, it generalized well for diverse pills. The output of the system is the pill name, primary medical indications and common dosage instructions that help in safer medication management by supporting healthcare providers in providing better care and patient safety.

关键词： Deep learning Accuracy Computational modeling Medical services Mobile handsets Safety Standards Research and development Biomedical imaging Pharmaceuticals

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Taxonomy of Proactive Detection Methods of Drunk Driving for Enhancing Traffic Safety

Taxonomy of Proactive Detection Methods of Drunk Driving for...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Richard Preston Swiley Razan Alsulieman Ahmed Sherif Mohamed Elsersy Kasem Khalil School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA Computer and Information Systems Dept. Higher Colleges of Technology AlAin Abu Dhabi UAE Electrical and Computer Engineering Department University of Mississippi MS USA

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

Drunk driving continues to be a substantial public health issue, leading to a multitude of accidents and deaths on a global scale. Conventional techniques for identifying drunk driving, such as breathalyzers and field sobriety tests, tend to be reactive rather than proactive. This paper surveys the existing methods for detecting drunk driving to enhance traffic safety. The technologies studied can proactively detect impaired driving behaviors by combining machine learning (ML) approaches with real-time data processing. The study outlines various classifications based on measured metrics and driving behavior.

关键词： Surveys Visualization Technological innovation Accuracy Face recognition Taxonomy Road safety Sensor systems Driver behavior Vehicles

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Partial Discharge Classification with Transformer Neural Networks

Partial Discharge Classification with Transformer Neural Net...

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International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： Thepjit Cheypoca Wiboon Promphanich Aung Ye Thway Angelina Phimpissada Hankae Siwakorn Jeenmuang Norasage Pattanadech Instrumentation and Control Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Computer Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Mechatronics Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350374605

ISBN: (纸本)9798350386165

This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed

关键词： Partial discharges Phased arrays Partial discharge measurement Surface discharges Training data Pattern classification Transformers Discharges (electric) Biological neural networks Testing

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Decentralized Spectrum Sharing Networks Based on Blockchains

Decentralized Spectrum Sharing Networks Based on Blockchains

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Shuyue Ai Yuna Jiang Qiang Li Xiaohu Ge Aduwati Sali School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Dept. of Computer and Communication Systems Engineering Faculty of EngineeringUPM WiPNET Research Centre

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The dynamic spectrum sharing technology in cognitive radio can effectively improve the utilization rate of spectrum and relieve the current spectrum pressure. To realize spectrum sharing without trust between SUs and PUs belonging to various operators, a decentralized spectrum sharing scheme based on blockchain is proposed in this paper. A latency model and a decentralization degree model of blockchain-enabled spectrum sharing network are formulated. Network scale law in this paper is defined as the change law of structure and scale in a network. Based on the proposed decentralization degree and latency model, the scale law of blockchain networks under the constraints of latency and decentralization is studied. The simulation analyzes the change of the proportion of consensus nodes in the blockchain network with different requirements for latency and decentralization. The results show that when blockchain networks have both low latency and decentralization characteristics, the upper limit of the total number of nodes is 390, and the value range of the proportion of consensus nodes is between 0.13 and 0.56.

关键词： Blockchains Cognitive radio Low latency communication Mobile computing

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