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Severity estimation of Coffee leaf disease using U-Net and pixel counting mechanism 24

Severity estimation of Coffee leaf disease using U-Net and p...

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8th international conference on Machine Learning and Soft computing, ICMLSC 2024

作者： Deb, Sagar Deep Kashyap, Rachit Abhishek, Arjun Jha, Rajib Kumar Department of Electrical Engineering Indian Institute of Technology Patna India

ISBN: (纸本)9798400716546

Coffee leaf disease (CLD) is a major threat to coffee production worldwide, causing significant economic losses for farmers. Accurate and timely estimation of the severity of CLD is crucial for implementing effective control measures. In this paper, we propose a novel approach for severity estimation of CLD using a combination of the U-Net deep learning architecture and a pixel counting mechanism. The U-Net architecture is used to generate the segmented masks of the coffee leaf images. Finally to estimate the severity of CLD, we introduce a pixel counting mechanism that quantifies the proportion of affected pixels in the segmented regions. By summing the total number of diseased pixels and dividing it by the total number of pixels in the region, we obtain a severity score that reflects the extent of CLD infections. The algorithm proposed by us achieves a five class accuracy of 89.10%. © 2024 ACM.

关键词： Deep learning

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A Development of Aquatic Landscape Type of Drones for the Implementation for Future Technology 4

A Development of Aquatic Landscape Type of Drones for the Im...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Jayashankari, J. Sahu, Satendra Arunkumar, B. Yadav, Kanchan Gokilavani, M. Vijayaprabhu, A. TN India M.P Bhopal India Karpagam Academy of Higher Education Department of Computer Science Engg India Gla University Department of Electrical Engineering Mathura India Nehru Institute of Engineering and Technology Department of Information Technology Tamil Nadu T.M.Palayam India Siddharth Institute of Engineering and Technology Department of Ece Andhra Pradesh Puttur India

ISBN: (纸本)9798350360165

Cutting-edge, cost-effective hardware components allow the development of vast automated underwater drone systems for marine uses. This talk focuses on the CORATAM along with HANCAD projects, covering key issues in organizing and speaking within networks of underwater drones. Our plan supports a novel approach: (i) adopting eclectic communication by outfitting some drones at long-range electronics while others hold shorter-range devices, and (iii) employing autonomous control for enhanced endurance and scaling. This approach ensures individual drones stay simple and cheap. Currently, we are currently engaged in the process of building 25 test drones using easily available components. The article dives into current hardware creates and the results of simulation-based studies where groups of as many as one thousand underwater drones successfully surveyed a 20 km -long location constantly for 24 hours. The results confirm the practicality of our approach in building a large-scale robotic underwater unmanned aircraft system that is simultaneously efficient and affordable. © 2024 IEEE.

关键词： Drones

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Synergistic Solutions: Federated Learning Meets CNNs in Soybean Disease Classification 4

Synergistic Solutions: Federated Learning Meets CNNs in Soyb...

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4th international conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Suryavanshi, Ankita Kukreja, Vinay Chamoli, Sushant Mehta, Shiva Garg, Ashish Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350343670

Modern agricultural demands call for creative, scalable, and flexible solutions, particularly for the early diagnosis and control of crop diseases. Using a Convolutional Neural Network (CNN) model and federated learning across five clients - O1 to O5 - this research study explores a novel method for categorizing five kinds of soybean leaf diseases. Using federated averaging techniques, the local models, each trained on a different data set, are combined into a global model, ensuring data privacy while improving predicted accuracy. Our study of the results indicates numerically solid values that support the model's effectiveness. Client O1 showed a precision of 86.71, recall of 86.47, and F1-score of 86.55, with an accuracy of 0.95 for the federated averaging metrics. These numbers were 83.30, 83.22, 83.24, and 0.93 for client O2. Client O4 had 92.34, 92.06, 92.19, and 0.97;client O5 had 90.31, 90.37, 90.34, and 0.96;and client O3 had 88.20, 88.11, 88.12, and 0.95. The aggregate averages further show the robustness of the model. The weighted averages covered the range from 83.39 to 92.24, while the macro averages for all customers varied from 83.25 to 92.20. The micro standards, which ranged from 83.40 to 92.24, were likewise strongly correlated. In conclusion, the federated learning-based CNN model represents a paradigm shift in categorizing soybean leaf diseases. Its high precision, recall, F1 scores, and accuracy rates show the model's potential for real-world applications. It provides an efficient, privacy-preserving, and scaleable solution for regional and worldwide agricultural landscapes. © 2024 IEEE.

关键词： (CNN)_(FL) Augmentation image Disease Leaf diseases soybean_leaves

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Research on Real-Time Calculation Method of Transformer Temperature Field Based on Reduced Order Model and Sparse Measurement 4

Research on Real-Time Calculation Method of Transformer Temp...

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2023 4th international conference on electrical Technology and automatic control, ICETAC 2023

作者： Wu, Tao Yang, Fan Farooq, Umer Liu, Zhi Jiang, Jinyang School of Electrical Engineering Chongqing University Chongqing400044 China Electric Power Research Institute State Grid Chongqing Electric Power Company Chongqing400015 China

The lifespan of transformers is closely related to their operating temperature. Among the existing temperature acquisition methods, the measurement method cannot obtain the complete temperature field temperature, while the numerical calculation method presents the challenge of significant computational expenses. This study theoretically derived how to determine the real-time temperature distribution of transformer windings based on sparse measurement data and reduced order models and solved sensor layout issues, including the number and placement position of sensors. In this study, the maximum calculation error of the temperature distribution is 1.8 K, and the calculation time is 0.38 seconds. The research findings suggest that multiple modes can contribute to the temperature distribution of a transformer. Under a constant load condition, the calculation of transformer temperature can be achieved by utilizing a temperature sensor positioned at the hot spot in conjunction with the first mode. © Published under licence by IOP Publishing Ltd.

关键词： Temperature distribution

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Device, Continuous Passive Motion-CPM, for the rehabilitation of motor skills of the forearm and wrist using a mobile application and Arduino 19

Device, Continuous Passive Motion-CPM, for the rehabilitatio...

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19th international conference on electrical engineering, computing science and automatic control, CCE 2022

作者： Zuniga, Hugo S. Gamboa Rangel, Laura Delgado Escamirosa, Fernando Perez Gnecchi, Jose Antonio Gutierrez Osnaya, Jose Ruben Huerta Espinoza, Daniel Lorias Cinvestav Ipn Electrical Departament Bioelectronic Section México City Mexico Issste Phisical Medicine and Rehabilitation México City Mexico ICAT/UNAM Institute of Applied Sciences and Technology México City Mexico Itm Departament of Engineering Electronic Morelia Mexico

ISBN: (数字)9781665455084

ISBN: (纸本)9781665455084

The objective is to support with this intelligent device to patients with injuries in the upper extremity. HS1 Forearm and wrist, Pronation/Supination CPM (HS1-CPM);is an intelligent device of rehabilitation therapeutic with Bluetooth technology, where, the Range of Motion (ROM) is controlled and monitored by means of short-range radio signal technology that uses radio frequency fields to transmit signals over short distances between devices. The mobile application used, also called a mobile app, is a type of application designed to run on a smart mobile phone using App Inventor to control the Hand Smart device (HS1-CPM). Besides, through data collection and analysis, the therapist will rely in this case on the joint movement of the patient's upper extremity after having performed the rehabilitation therapy with the intelligent device HS1-CPM, the analysis of data obtained by the Leap Motion controller (LMC) sensor, which captures the position of the joint movement of the affected upper extremity, this data obtained will help therapists to quantify the patient's improvement and/or identify the problem areas of the patient's upper extremity injury. © 2022 IEEE.

关键词： Patient treatment

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Development of a 10 kV Grid Model for the Research Methodology of Digital Instrument Transformers in the Conditions of Arc Intermittent Single-Phase Earth Faults

Development of a 10 kV Grid Model for the Research Methodolo...

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2023 international Russian Automation conference, RusAutoCon 2023

作者： Filatova, Galina Dobryagina, Olga Litvinov, Sergey Ivanovo State Power Engineering University Automatic Control of Electric Power Systems Ivanovo Russia Ivanovo State Power Engineering University Electrical Stations Substations and Diagnostics of Electrical Equipment Ivanovo Russia

ISBN: (纸本)9798350345551

Scientists of the Ivanovo State Power engineering University (ISPEU) together with industrial partners developed and researched digital instrument current and voltage transformers (DIT). The devices already have operational experience in existing electrical power installations. However, some applications of DIT require additional research. The article presents combined DIT of 6(10) kV experimental investigation method in the conditions of arc intermittent single-phase earth faults (SPEF), as well as a simulation model of a 10 kV grid developed in RSCAD RTDS for modeling transients during SPEF. The model parameters and its verification with a similar grid model developed in Matlab Simulink are given. © 2023 IEEE.

关键词： MATLAB

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IoT Based Optical Sensor Network For Precision Agriculture

IoT Based Optical Sensor Network For Precision Agriculture

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2024 international conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Durai, R. S. Vishnu Vijayakumar, R. Lakshmisridevi, S. Bhanu, Shaik Thasleem Arunkumar, U. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamilnadu Coimbatore641022 India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600048 India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641021 India

ISBN: (纸本)9798350383348

Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. Sensor networks and Machine Learning (ML) based tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (ASFONB) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification. © 2024 IEEE.

关键词： Agriculture IoT Optimization Approach

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An Extended Generalized Super-Twisting Algorithm for Estimating Key Biochemical Variables in Bioprocesses

An Extended Generalized Super-Twisting Algorithm for Estimat...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Víctor Alejandro Reza López Jorge Antonio Torres Muñoz Jesus Norberto Guerrero Tabares Automatic Control Department CINVESTAV-IPN Mexico City Mexico Mechatronics Departament Tecnológico Nacional de México/ITS Abasolo Guanajuato Mexico

In order to improve the behavior of any bioprocess, it is required to know some of its key biochemical variables, such as the reaction rates and the influent feeding rates. However, these variables are hard to measure, challenging to describe mathematically, or can be the source of unknown disturbances. Moreover, even though many unknown input observers were designed for their estimation, their convergence cannot be modified. In this work, an extended generalized super-twisting algorithm is designed to simultaneously estimate reaction rates and influent feeding rates in finite time. For this task, a change of variables is used, valid if the dilution rate is strictly positive. The proposed algorithm is applied on an alcoholic fermentation process, and simulated results show a good performance from the design observer.

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Performance degradation monitoring method of predictive control based on improved weighted DSVDD 3

Performance degradation monitoring method of predictive cont...

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3rd international conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Xie, Zhangcheng Li, Lijuan College of Electrical Engineering and Control Science Nanjing Tech University Nanjing211816 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

The operation data of the predictive control system is nonlinear, sequential and dynamic. The implicit nonlinear transformation of the traditional support vector data description (SVDD) cannot represent the timing and dynamic characteristics of the operation data of the control system. To solve this problem, this paper proposes a predictive control performance degradation detection method based on weighted Deep-SVDD. First of all, the target optimization function of SVDD is redefined based on the architecture of deep neural network, and the Deep-SVDD monitoring model is constructed using the extracted deep features. Secondly, the concept of sliding windows is introduced in view of the difference in sensitivity of deep features to predicted performance degradation of control systems. Using dynamic data from the window to weight the impact of sensitive features to highlight system performance degradation and construct a weighted Deep-SVDD monitoring model. Finally, the algorithm implementation process is theoretically deduced. The effectiveness of the proposed method is verified by the simulation of Wood-Berry distillation tower predictive control system. © 2023 SPIE.

关键词： Deep neural networks

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Identification and control Design for an Experimental Setup to Take Home

Identification and Control Design for an Experimental Setup ...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Efrain Alcorta Garcia Miguel Ángel Platas Garza David Alejandro Diaz Romero Fac. de Ing. Mecánica y Eléctrica Universidad Autónoma de Nuevo León San Nicolas de los Garza NL México

From classroom experience arises a necessity to demonstrate control outcomes through physical examples. One option to carry out these experiments is by using electronics to implement systems, along with microcontrollers to handle data acquisition and control tasks. In this study, a system that generates heat by passing current through the collector-emitter of a power transistor is implemented. The current flow is regulated by a PWM signal applied to the base. A temperature sensor is constructed using a thermistor, which provides feedback on the temperature and compares it to a reference value. The control scheme is implemented using an affordable microcontroller. Input-output measurements for open-loop process identification are performed using the same microcontroller with the support of a Python program. The results are presented to facilitate DIY implementation and to aid in better understanding of various concepts.

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