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Application of Event-based Cloud NMPC with Time Delay Compensation

Application of Event-based Cloud NMPC with Time Delay Compen...

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Industrial Cyber-Physical Systems (ICPS)

作者： Alvin Immanuel Surjana Elmar Ahle Dirk Söffker Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Dynamics and Control University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

The increasing availability of cloud computing services has revolutionized industrial productivity by providing essential tools for data storage, data transfer, and computation. In a cloud-based control system (CCS), the controller is located in the cloud, thereby enabling complex computation processes such as nonlinear model predictive control (NMPC). Using cloud computing, expensive control algorithms can be deployed without the upfront cost of a local computing unit. The computation time can be drastically reduced due to the improved computing power, which is important in the case of systems with fast dynamics in combination with NMPC-based control to maintain the existing time step size with regard to the calculation time. Placing the controller in the cloud also introduces communication latency in the control loop. It has been highlighted in former studies that computation time and communication latency can reduce the reliability and stability of a CCS. This paper proposes a cloud-based model predictive control strategy that extends simple time delay compensation with event-based control to reduce the effect of computation and communication delays. The optimal input variable sequence is used until a new sequence is available. This event-based method reduces the amount of communication and redundant calculations. The developed CCS algorithm is implemented in the real-time control of an experimental pendulum crane system. The effects of the proposed algorithm in mitigating computation and communication delay are compared to another CCS strategy that only uses the first element of the sequence.

关键词： Productivity Cloud computing Delay effects control systems Prediction algorithms Stability analysis Real-time systems

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MicroNET: Microarray Image Orientation Predictor and Automated Data Extraction

MicroNET: Microarray Image Orientation Predictor and Automat...

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international conference on control, Instrumentation, Energy and Communication (CIEC)

作者： Arka Ray Sanjeeva Srivastava Siddhartha P Duttagupta Centre for Research in Nanotechnology & Science (CRNTS) IIT Bombay Mumbai Maharashtra Department of Biosciences and Bioengineering IIT Bombay Mumbai Maharashtra Department of Electrical Engineering IIT Bombay Mumbai Maharashtra

Microarray is a high-throughput platform consisting of several thousands of DNA, protein, or peptides on a chip surface. In the current scenario, researchers have to use a scanner such as GenePix®4000B to scan such chips. However, after scanning these chips, researchers need to manually adjust the orientation angle of the grid to be placed in the microarray image before moving on to the spot intensity extraction step and data analysis. This paper introduces a novel lightweight deep-learning model, MicroNET, to predict the orientation angle and top left coordinates of the grid. After the image is corrected and the grid is placed, we also perform automatic fluorescence intensity extraction. This paper has compared two different configurations of MicroNET. The first one makes use of a ResNet18 backbone while the next one is a modified version of the ResNet18 backbone with lesser parameters. As per our results, the model with the modified ResNet18 backbone predicts the orientation angle with an MSE of 0.379 and the top left coordinates with an MSE of 0.0011. Thus the model with the modified ResNet18 backbone achieves better results with a lesser number of parameters. Hence it can be easily executed on any microarray data extraction software and will enable researchers to prepare microarray datasets easily.

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Research on Intelligent Visual Detection Methods for Human Physiological Parameters 1

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wu, Chenggang Wan, Zilin Shao, Rui Lin, Pengcheng Zhang, Kun School of Electrical Engineering and Automation Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Institute of Technology Jiangsu Nantong China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

In recent years, the incidence and mortality rates of cardiovascular diseases have been increasing year by year, prompting greater attention to the monitoring of physiological parameters in daily life. This paper proposes a deep physiological parameter detection method based on Image Photoplethysmography (IPPG) technology, overcoming the discomfort of contact-based measurements and the limitation of measuring single physiological indices through intelligent visual means. A lightweight attention mechanism combined with the BHCNN model is designed to automatically extract waveform features of IPPG signals for estimating blood pressure and heart rate in the human body. The mean absolute errors (MAE) of the obtained systolic pressure, diastolic pressure, and heart rate are 6.60, 6.22, and 7.15 respectively, with standard deviations (STD) of 3.22, 2.61, and 3.98. The data consistency of both indices exceeds 95%, demonstrating high accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blood pressure

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Development of Hut Shaped Solar Photo Voltaic System with Rotating Convex Lens And Concave Mirror Mechanism

Development of Hut Shaped Solar Photo Voltaic System with Ro...

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Advances in electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： B. Pakkiraiah G. Durgasukumar P. Srividya Devi E. Satyanarayana M. Rekha P. Praveen Kumar Dept. of EEE Gokaraju Rangaraju Institute of Engineering and Technology-Autonomous Hyderabad India Dept. of EEE Vignan Institute of Technology and Science Deshmukhi Yadadri Bhuvanagiri India

A novel method has been proposed to design and develop a Triangular Hut-shaped photovoltaic panel with rotating mechanism using modified maximum power point tracking (MPPT). In this, irradiance for the Primary panel can be provided by the nature itself with the available Sun and Secondary panels are to be provided by reflection with the rotating convex lens and concave mirror rotating gear mechanism. Here that the rotation of the convex lens and concave mirror will be depending upon the position of the Sun. To locate the Sun an electro-optical sensor will be used. It consists of light-dependent resistor (LDR) sensors to track sun position. Modified MPPT based controller detects the difference of voltage signals sent by the LDRs. Based on the outcome of LDRs, signals will be transferred to the Optocoupler for the activation of the servo mechanism. The servo mechanism of the convex lens and concave mirror rotates either in clockwise or anticlockwise direction based on signals reached to the driver circuit. The inputs to this controller will be solar irradiation and temperature. The experimental results of Triangular Hut-shaped PV panels will be compared with the conventional PV system results.

关键词： Maximum power point trackers Resistors Voltage Temperature control Mirrors Solar panels Sun

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Study on the Process and Method of automatic Material Batching andWeighing in the Background of Informatization 3

Study on the Process and Method of Automatic Material Batchi...

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3rd international conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Zhong, Wenyu Qi, Mingyang Tang, You Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin132101 China Smart Agricultural Engineering Research Center of Jilin Province Jilin132101 China

ISBN: (纸本)9781450385046

automatic weighing of materials is widely used in metallurgy, medicine, agriculture and other fields, with the continuous development of sensor technology, weighing instrument production and control technology is gradually improved, weighing accuracy is more and more high [1-2]. Its weighing process and weighing method as early as last century has been widely studied, but each manufacturer is different, the focus is not the same. This paper discusses the weighing process and weighing method of several typical materials, such as dry material, powder material and liquid material [3-4]. © 2021 ACM.

关键词： Weighing

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A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute Nodes

A Reinforcement Learning Approach for Performance-aware Redu...

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IEEE international conference on Cloud engineering (IC2E)

作者： Akhilesh Raj Swann Perarnau Aniruddha Gokhale Department of Electrical Engineering Vanderbilt University Nashville Tennessee USA Mathematics and Computer Science Division Argonne National Laboratory Lemont Illinois USA Department of Computer Science Vanderbilt University Nashville Tennessee USA

As Exascale computing becomes a reality, the energy needs of compute nodes in cloud data centers will continue to grow. A common approach to reducing this energy demand is to limit the power consumption of hardware components when workloads are experiencing bottlenecks elsewhere in the system. However, designing a resource controller capable of detecting and limiting power consumption on-the-fly is a complex issue and can also adversely impact application performance. In this paper, we explore the use of Reinforcement Learning (RL) to design a power capping policy on cloud compute nodes using observations on current power consumption and instantaneous application performance (heartbeats). By leveraging the Argo Node Resource Management (NRM) software stack in conjunction with the Intel Running Average Power Limit (RAPL) hardware control mechanism, we design an agent to control the maximum supplied power to processors without compromising on application performance. Employing a Proximal Policy Optimization (PPO) agent to learn an optimal policy on a mathematical model of the compute nodes, we demonstrate and evaluate using the STREAM benchmark how a trained agent running on actual hardware can take actions by balancing power consumption and application performance.

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Limits the Time Relay Coordination of Photovoltaic Power System using ETAP

Limits the Time Relay Coordination of Photovoltaic Power Sys...

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international conference on sciences and Techniques of automatic control and Computer engineering (STA)

作者： Mohammed Jawad Abed Anis Mhalla Nadheer A Shalash Laboratory of Automation Electrical Systems Environment (LAESE) National Engineering School of Monastir (ENIM) Monastir Tunisia Department of electrical power engineering techniques Al-Ma'moon University College Baghdad Iraq

ISBN: (纸本)9781665482622

Proper coordination of time-limited relays is crucial to minimize the unnecessary outages of interconnected PhotoVoltaic (PV) power systems. In this article, new bounds are proposed to determine the relay time by appropriately computing the variations in the maximum and minimum of Short Current (SC) in addition to the techniques of reducing the influence of the interfacing inverter for diverse PV levels with time interval changes of Over Current (OC) relay coordination. Finally, to demonstrate the effectiveness and accuracy of the presented analysis made by ETAP program, an application to the Diyala grid power system is presented. The main is to determine the best PV candidate locations for buses after many cases of SCs.

关键词： Photovoltaic systems Maintenance engineering Inverters Power systems Relays Monitoring

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An Efficient Andean Condor Algorithm for Optimal Charging Stations for Electric Vehicle

An Efficient Andean Condor Algorithm for Optimal Charging St...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Senthil Kumar R Pradeep Katta Saravanakumar K Naresh Kumar S Vigneshwaran A K Vigneshwaran S Electrical and Electronics Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Electrical and Electronics Engineering Hindusthan College of Engineering and Technology Coimbatore India

In today's world, new energy science is gradually advancing, and a number of advances are taking place. Consequently, electric vehicles (EV) are being predictable by people around the world and are becoming increasingly popular in several countries. Electric vehicles are becoming increasingly popular, but their rapid growth has resulted in a number of problems, including a lack of charging stations, uneven distribution and high costs. In order to overcome such issues, this paper proposed a method for improving the charge scheduling of electric vehicles using ACA algorithm. In this way, decentralized chargers will be able to learn decentralized charger control strategies more effectively because they have access to a common replay of previous operations. The implementation of EV charging stations is examined in this paper due to the importance of charging station infrastructure. Its goal is to consider a variety of restrictions, such as, the charging station's power, typical amount of time needed for each recharge and daily travel times. Electric vehicle DG optimization is scheduled using the Andean Condor Algorithm (ACA) method as well as the Network Reconfiguration (NR) is carried out to minimize the power losses. By satisfying the grid's requirements for the charging schedule, this method minimize the active as well as reactive power losses in the distribution networks. The reliability of the charging system schedule is improved and overall power losses are entirely eliminated by the proposed technique. According to the MATLAB simulation results, ACA greatly reduces distribution system overall losses by placing charging units.

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An Artificial Intelligence-based Integrated Framework for Lean and Smart Manufacturing: A Case Study of the Rail Industry

An Artificial Intelligence-based Integrated Framework for Le...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), international conference on

作者： Felix Ale Ilesanmi Daniyan Olugbenga Aderoba Adeolu A. Adediran Engineering & Space Systems National Space Research & Development Agency Abuja Nigeria Department of Mechanical & Mechatronics Engineering Achievers University Owo Nigeria Department of Mechanical Engineering Landmark University Omu-Aran Nigeria Department of Industrial Engineering Tshwane University of Technology Pretoria South Africa Department of Mechanical Engineering Science University of Johannesburg South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This study deals with the development of an Artificial Intelligence-based integrated digital technology framework for lean and smart manufacturing implementation. Some technologies and techniques driving Industry 4.0 that are considered in this study include Additive Manufacturing (AM), automatic Train control (ATC), smart sensors, Internet of Things (IoT), big data, cloud computing, Total Productive Maintenance, Predictive Maintenance (PM), and Artificial Intelligence (AI). Using the rail industry as a case study, an overview of the enabling technologies was presented to acquaint the manufacturing industries with the potentials and applications of these technologies to manufacturing. The study also develops a conceptual framework which incorporates the main driving technologies of lean and smart manufacturing for adoption by the rail industry. The validation of the proposed integrated framework involves the use of AI technology for total productive and predictive maintenance using the rail industry as a case study was conducted in the MATLAB 2020 b environment. The outcome of the demonstration shows the Remaining Useful Life (RUL) of a bearing component of a railcar can be predicted using AI. The predicted RUL of the wheel bearing indicates 500 hours over the next 40 days before failure occurs. Thus, this study adds to the understanding of AI in the lean and smart manufacturing domain. The implementation of the integrated framework can promote efficiency and effectively service delivery in industries that employs the concept of lean and smart manufacturing.

关键词： Rails Manufacturing industries Wheels Three-dimensional printing Manufacturing Maintenance Internet of Things

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Recurrent Mutual Attention Network with Transformer and LSTM Integration for Radiology Report Generation

Recurrent Mutual Attention Network with Transformer and LSTM...

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international conference on Applied Nonlinear Dynamics, Vibration and control, ICANDVC 2024

作者： Li, Zhenhao Wang, Chao Zhang, Yi Lin, Shen Yang, Xiaohui Geng, Haokun College of Information Science and Engineering University of Jinan Jinan250024 China Shandong Provincial Maternal and Child Health Care Hospital Jinan250014 China Jinan Authority Hospital Jinan250001 China Jinan Key Laboratory of Intelligent Medical Frontier Applications in Artificial Intelligence and Automation Jinan250001 China

ISBN: (纸本)9789819633166

Generating radiology reports by analyzing medical imaging data is becoming increasingly important not only in the research field but also in clinical practice. The automatic generation of accurate and coherent reports is of paramount importance to enhancing the efficiency and quality of medical diagnosis. Due to the rapid development of deep learning, automatic radiology report generation has evolved from simple feature transformation to cross-modal interactions between images and text. However, the correspondence between image regions and words remains unclear. Therefore, this paper proposes a recurrent mutual attention network that queries image features through text and then queries corresponding text features through image features. These features are then input into the report generation module. To enrich the semantic information, the long short-term memory (LSTM) is connected with the Transformer through gate control, enabling the automatic generation of radiology reports. The experimental results show that, compared with the other published methods, our proposed method has significantly improved the diagnostic report generation in quality, accuracy, and natural language fluency. Although our method has only been tested in radiology, it has shown potential usability in a wider range of different types of medical imaging analysis, due to its design principles and versatility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mammography

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