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Design of disease prediction method based on whale optimization employed artificial neural network in tomato fruits

Materials Today: Proceedings 2020年 33卷 4907-4918页

作者： S. Dhakshina Kumar S. Esakkirajan C. Vimalraj B. Keerthi Veena Department of Electronics and Communication Engineering University College of Engineering Ramanathapuram India Department of Instrumentation and Control Systems Engineering PSG College of Technology Coimbatore Tamil Nadu India Department of Electrical And Electronics Engineering Sri Venkateshwara College of Engineering and Technology Chittoor Andhra Pradesh India Research Scholar Department of Instrumentation and Control Systems Engineering PSG College of Technology Coimbatore Tamil Nadu India

The advancement made in the field of information technology paves way for developing and utilization of software based technique in various field. The application of software related technique in the field of agriculture was at its peak nowadays. The key problems faced in agriculture are decrease in quality and yield of crops. This was caused due to various reasons such as environmental condition, insects, pest and diseases caused through microorganism. Among these various reasons the major fall in quality and yield of crop was achieved due to variety of disease. So, disease prediction at earlier stage in crops is considered as significant. Many methods were developed for prediction of disease in crop. But, effective prediction with better accuracy was not achieved in any of the developed method. Therefore for solving this issue the proposed system is developed. In the proposed method Whale Optimization Based Artificial Neural Network (WOANN) is used for prediction of different kinds of disease in tomato. Prior to classification of disease in tomato the process of colour image segmentation is carried out using FA in order to attain effective disease prediction ultimately. Further to evaluate the performance of the proposed system some statistical parameter such as accuracy, specificity, sensitivity, error rate, F1 score are calculated. And to prove that the proposed method functions effectively in disease prediction of tomato when compared to other conventional method such as Probabilistic Neural Network (PNN), K-Nearest Neighbour (K-NN) and Back Propagation Artificial Neural Network (BPANN) a comparison analysis is performed.

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A Dynamic Surface controller based on Adaptive Neural Network for Dual Arm Robots 15

A Dynamic Surface Controller based on Adaptive Neural Networ...

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15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

作者： Le, Hai Xuan Nguyen, Linh Thiyagarajan, Karthick Hanoi University of Industry Department of Instrumentation and Control Engineering Hanoi10000 Viet Nam Engineering and It Federation University Australia School of Science Churchill VIC3842 Australia University of Technology Sydney Centre for Autonomous Systems UltimoNSW2007 Australia

ISBN: (纸本)9781728151694

The paper introduces an adaptive controller to efficiently manipulate the dual arms of a robot (DAR) under uncertainties including actuator nonlinearities, system parameter variations and external disturbances. It is proposed that by the use of the dynamic surface control (DSC) method, the control strategy is first established, which enables the robot arms to robustly operate on the desired trajectories. Nevertheless, the dynamic model parameters of the DAR system are unknown and impractically estimated due to its uncertain nonlinearities and unexpected external factors. Hence, it is then proposed to employ the radial basis function network (RBFN) to adaptively estimate the uncertain system parameters. The Lyapunov theory is theoretically utilized to derive the adaptation mechanism so that the stability of the closed-loop control system is guaranteed. The proposed RBFN-DSC approach was validated in a synthetic environment with the promising results. © 2020 IEEE.

关键词： Radial basis function networks

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Two-Loop Distributed Optimal Secondary Voltage control of an Islanded Microgrid with Multiple Distributed Generators

Two-Loop Distributed Optimal Secondary Voltage Control of an...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Muhammad Maaruf Sami El-Ferik Mohammad Abido Muhammad Khalid Control and Instrumentation Engineering Department Center for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department Center for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

Integration of large-scale distributed generators (DGs) (such as wind turbines, solar photo-voltaic, fuel cells, and so on) into the microgrid triggers voltage deviation which can give rise to desynchronization and distortion of power flow. To solve this problem, this article presents a distributed optimal secondary voltage control to synchronize the output voltage deviations of the DGs to zero. Instead of designing a distributed linear optimal control for the DGs linearized near an operating point as in the existing literature, a two-loop control structure comprising an inner-loop and outer loop is put in place. The inner-loop linearizes the nonlinear dynamics of each of the DGs. Then, the outer-loop which consists of the optimal distributed controller solves an optimization problem with the aim to minimize the voltage deviation of each DG by exchanging information with neighbouring DGs through a communication network. The proposed control technique will force the voltage deviations of the DGs to zero based on consensus. The efficacy of the proposed technique is confirmed by simulation studies.

关键词： Reactive power Simulation Optimal control Microgrids Generators Wind turbines Nonlinear dynamical systems

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A Comprehensive Review of Swarm Optimization Algorithms for MPPT control of PV systems under Partially Shaded Conditions

Electronics

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Electronics 2020年第1期24卷 3-14页

作者： Pilakkat, Deepthi Kanthalakshmi, S. Navaneethan, S. Department of Electrical and Electronics Engineering PSG college of Technology 641004 India Department of Instrumentation and Control Systems Engineering PSG college of Technology 641004 India

Nowadays many researchers have been investigating on different photovoltaic (PV) modeling methods, various configurations of arrays, numerous algorithms, converter topologies etc to improve the efficiency of solar system. Improving the efficiency of solar panel by utilizing the correct maximum power point tracking (MPPT) control has become more important for conceiving the solar power reasonably. For designing an efficient PV system, an appropriate literature review is necessary for all the researchers. In this paper, a compendious study of different Swarm Intelligence (SI) based MPPT algorithms for PV systems feasible under partially shaded conditions are presented. SI algorithms use motivation from the foraging nature of animals and insects. In the last few decades, SI has gained tremendous attention as it has been proven as an efficient control technique for global optimization problems. © 2020. All rights reserved.

关键词： Efficiency

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Design and Implementation of Fully Automated Calibration System for Multiple Temperature-controlled Enclosures Using Networked Measurement and Real-time Steady-State Detection Approaches

Design and Implementation of Fully Automated Calibration Sys...

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IEEE International Conference on control and Robotics engineering (ICCRE)

作者： Jettana Tongbai Santi Nuratch Department of Control Systems and Instrumentation Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9781728167916

ISBN: (纸本)9781728167923

This paper presents the system design, algorithms and user applications for the temperature-controlled enclosure calibration and testing processes. The system works as an IoT-based real-time temperature steady-state detection and monitoring system. The main goals of this research are divided into four parts. The first one is to design a networked calibration system that can perform multiple devices calibrating and testing at the same time. The system utilizes Modbus and Internet-based Wireless communication networks. The second one is to study and implement an appropriate algorithm that can detect the temperature steady-state of the temperature-controlled enclosure devices connected in the network. This proposed algorithm, the steady-state detector is implemented based on a low-pass filter and finite-state machine approaches. It is implemented using TypeScript, the strongly typed JavaScript. The third one is to design and implement web and mobile that supported applications used as real-time data visualization and user interface. The application is employed with development languages, including HTML, CSS, and TypeScript. The last one is to design and develop a web-based application to evaluate and deliver a technical report of the calibration and testing. The TypeScript files are exploited for the steady-state detector and applications are transpiled to common JavaScript that can perform directly in all browsers. The proposed system is evaluated in the real-use environment with various possible conditions. The experimental results validate the effectiveness of both system architecture, algorithm and user applications, offering overall performance comparable with legacy manual calibrating, testing and reporting processes. In particular, the normalized error (E n ) between manual and the proposed system significantly different (E n > 1), namely 95% confidence interval, thus widely satisfying requirements imposed by standardization bodies.

关键词： Calibration Temperature measurement Steady-state Temperature control Manuals Automation Temperature sensors

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Deep Deterministic Policy Gradient for Magnetic Levitation control

Deep Deterministic Policy Gradient for Magnetic Levitation C...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Sarawan Wongsa Nitis Kowkasai Department of Control Systems and Instrumentation Engineering King Monkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9781728164861

ISBN: (纸本)9781728164878

MagLev ball motion control is a challenging problem because of its nonlinear and unstable behaviour. It also requires to have a large operating range. In this work, we have extended the current success of a deep reinforcement learning algorithm in continuous control using the Deep Deterministic Policy Gradient (DDPG), to magnetic levitation (MagLev) control. In reinforcement learning, the reward function is very critical for learning the optimal policy. We propose a novel and simple reward function for DDPG and evaluate our method on MagLev control. Our DDPG-based controller shows the capability to learn from the system with comparable performance to the previously proposed reward function. It can also find the solution to control the ball at all positions, including the ones that were not shown during the learning stage.

关键词： Magnetic levitation Machine learning Training Learning (artificial intelligence) Telecommunications Information technology control systems

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Load Modeling for Cryptocurrency Mining Devices Using System Identification and Machine Learning

Load Modeling for Cryptocurrency Mining Devices Using System...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ghali Ahmad Md Shafiullah Nezar M. AL-Yazidi Mohamed Zaery Mujahed Al-Dhaifallah M. A. Abido Faris Aljehani Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia IRC for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia K.A.CARE Energy Research & Innovation Center King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia IRC for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Saudi Arabia Dema Energy Dhahran Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper presents a novel flexible load model tailored for cryptocurrency mining applications, specifically designed to dynamically adjust Application Specific Integrated Circuits-based mining operations based on real-time power availability. The model allows miners to use excess power during peak generation and reduce their usage during low power generation, all in support of maintaining a stable grid. The integration of the System Identification technique and some machine learning methods, such as Linear Regression, Support Vector Machine, and Neural Network Models, are used to identify and verify the proposed model against the genuine load data. Values of R 2 ranging from 0.9898 to 0.9953 point toward a very good agreement between the simulated and actual load profiles. The Neural Network Model presents the lowest RMSE, MSE, MAE, and WIE, which means that the model accurately captures the actual behavior of the load. This approach strengthens the efficiency and accuracy of mining processes and is quite compatible with renewable power generation and, therefore, the effective use of energy. This research forms the reference platform for adaptive load management for such intensive applications to provide a viable approach to renewable energy integration into high-power utilization systems.

关键词： Renewable energy sources Adaptation models Machine learning Load management Data models Cryptocurrency System identification Integrated circuit modeling Power generation Load modeling

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2024年 1148 LNEE卷 v页

作者： Sikander, Afzal Zurek-Mortka, Marta Chanda, Chandan Kumar Mondal, Pranab Kumar Department of Instrumentation and Control Engineering Dr. B. R. Ambedkar Nati onal Institute of Technology Jalandhar Punjab Jalandhar India Department of Control Systems Lukasiewicz Research Network—Institute for Sustainable Technologies Radom Poland Department of Electrical Engineering Indian Institute of Engineering Science West Bengal Howrah India Department of Mechanical Engineering Indian Institute of Technology Guwahati Assam Guwahati India

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FALCON based Quantum Attack Secure Digital Signature Scheme for IEC 61850 GOOSE Messages

FALCON based Quantum Attack Secure Digital Signature Scheme ...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： S.M. Suhail Hussain Mohd Asim Aftab Mohamed Zaery Syed Muhammad Amrr Charalambos Konstantinou Mohammad A. Abido Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia K.A. CARE Energy Research & Innovation Center King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

The integration of digital technologies in substations offers numerous advantages, including real-time monitoring, predictive maintenance, and enhanced operational efficiency. However, increased automation also introduces cybersecurity vulnerabilities, particularly in protocols like IEC 61850, notably in the Generic Object-Oriented Substation Event (GOOSE) message. Although IEC 62351-6:2020 suggests security measures employing Hash-based Message Authentication Code (HMAC) and Galois Message Authentication Code (GMAC) for lower computational overhead, their reliance on symmetric key-based encryption poses challenges in key distribution. Moreover, with the advent of quantum computing, it becomes imperative for security schemes to be quantum attack resistant. This paper proposes usage of quantum-secure FALCON-based digital signature scheme (FDSS) to secure GOOSE messages. FALCON offers robust security against quantum attacks while maintaining efficient performance. The performance evaluation of FDSS for GOOSE messages is presented, demonstrating its viability for securing time-sensitive GOOSE messages.

关键词： Performance evaluation Resistance Substations Quantum computing Codes IEC Standards Computer security Digital signatures Message authentication Standards

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Algorithms for automatic billet straightening machine 58

Algorithms for automatic billet straightening machine

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58th International Scientific Conference on Experimental Stress Analysis, EAN 2020

作者： Halama, R. Sikora, J. Fusek, M. Marek, M. Bartecka, J. Czebe, J. Guras, R. Wagnerova, R. Mahdal, M. Department of Applied Mechanics Faculty of Mechanical Engineering VŠB Technical University of Ostrava 17. listopadu 2172/15 Ostrava708 00 Czech Republic Department of Control Systems and Instrumentation Faculty of Mechanical Engineering VŠB Technical University of Ostrava 17. Listopadu 2172/15 Ostrava708 00 Czech Republic

ISBN: (纸本)9788024844510

This scientific contribution presents current results of cooperation focused on the automatic billet straightening machine development. An experimental study of three-point bending is shortly presented to explain basic ideas of the straightening. Main variants of straightening are briefly described. Some material parameters have to be properly determined for a new material in the straightening process, what is solved by creation of software for their estimation from a mapping regime of straightening. © 2020 Experimental Stress Analysis - 58th International Scientific Conference, EAN 2020. All rights reserved.

关键词： Stress analysis

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