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4th International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2024

作者： Mohanraj, K. Ramesh, A. Vairavel, K.S. Mohammed Thoufik, S. Muthuraja, R. Mohamed Arshad, M. Sri Sairam Engineering College Department of Instrumentation & Control Engineering Chennai India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Bannari Amman Institute of Technology India

ISBN: (纸本)9798331508845

There have been crucial changes in the packaging industry recently, especially with regard to Form Fill and Seal (FFS) machines. These machines can be found in different sizes making them available for use by small businesses that want to lower their plant costs using inexpensive automated packaging solutions. This paper describes a low-cost weighing and pouring mechanism for improving accuracy of pouch filling machine. The process flow is carefully designed so as to achieve optimum production rates. Manipulators under the control of our mechatronics system are regulated through feedback from sensors managed by microcontroller. © 2024 IEEE.

关键词： Costs

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Research study of image classification algorithms based on Convolutional Neural Networks 24

Research study of image classification algorithms based on C...

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24th International Carpathian control Conference, ICCC 2023

作者： Nawrocka, Agata Nawrocki, Marcin Kot, Andrzej AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Department of Process Control Krakow Poland

ISBN: (纸本)9798350310221

The beginning of the paper introduces the idea of neural networks by presenting the neuron models and artificial neural networks. Next, the flow of operations used in the development of convolutional neural networks is presented, as well as an overview of popular architectures used in model development. After introducing the theoretical issues, a project using the EfficientNet architecture to recognize medical images taken by MRI is presented. The project describes: the selection and parameters of the MRI image database representing different types of brain tumors, the process of implementation and appropriate modification of the EfficientNet architecture used to classify images, the presentation of the results of learning the convolutional neural network and the images that highlight on the basis of which area the convolutional neural network classifies images. © 2023 IEEE.

关键词： Convolutional neural networks

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Development of a Capacitive Sensing Module to Detect the Concentration of Isopropyl Alcohol for Covid-19 Disinfection Using Small Capacitance to DC Converter 9

Development of a Capacitive Sensing Module to Detect the Con...

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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Sriratana, Witsarut Faijaree, Manasnit Sriratana, Lerdlekha Satthamsakul, Sutham School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Instrumentation and Control Engineering Bangkok10520 Thailand Ramkhamhaeng University Faculty of Engineering Department of Industrial Engineering Bangkok10240 Thailand

ISBN: (纸本)9798350318241

This paper presents the development of a sensing module for analyzing the concentration of disinfectant alcohol commercially available in Thailand during the Covid-19 epidemic situation to ensure the ingredients and percentage concentration by focusing on simplicity, low cost with reliable performance. Under the basic physical structure of the capacitance change [1]-[5], it consists of a New Sensor Module developed to fully interface with the small capacitance to DC converter for capacitive sensor [2] model. Inside the sensing structure, there were 2 modules of metal conductors. Each conductor was 3 mm. thick, placed in parallel with a fixed distance between the module of 1 mm. The cross-sectional areas of the plates of those two modules were different in order to compare the efficiency of the detector developed. The cross-sectional areas of the plates of Module A and B were 4 cm2 and 1 cm2, respectively. Experiments were performed with 3 concentrations of alcohol, 65%, 70% and 75%, to enhance the concentration detecting performance of the sensing module developed. The signal conditioning was generated from a small capacitance to DC converter for capacitive detection with a frequency of 100 kHz, which was the most suitable frequency for exciting the capacitance change which was the capacitive reactance of the alcohol used in this study. From experiments, it was found that the sensing module with a small cross-sectional conductive material would provide more accurate results in terms of linearity and repeatability. The errors were less than 2.72% compared to the module with a large cross-sectional conductor material which would have an error up to 4.73%. © 2024 IEEE.

关键词： Capacitance

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Feasibility study of a 3D printed BLDC outrunner rotor

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Materials Today: Proceedings 2023年 80卷 163-167页

作者： Akula, Venkata Sai Rama Pramod Goyal, Kapil Kumar Verma, Om Prakash Gupta, Vishal Department of Industrial and Production Engineering Dr B R Ambedkar National Institute of Technology Jalandhar 144027 India Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar 144027 India Department of Mechanical Engineering Thapar Institute of Engineering and Technology Patiala 147004 Punjab India

Electric motors had been one of the greatest advancements in engineering. Since the 19th century, they are being used in most of the engineering equipment and industries. Various types of electric motors are used for variety of purposes and applications. BLDC (brushless DC) motors are generally used for high-speed applications. The BLDC motors commonly used in Remote controlled (RC) planes have an outrunner type BLDC motor which equips a spinning outer shell as the rotor. This study is carried out to test the feasibility of 3D printed BLDC Outrunner rotors which offers advantages in terms of weight and cost reduction. A factor of customizability can be added to the rotors which is useful in specific applications. A rotor has been modelled with minor changes which add few advantages and 3D printed using PLA composite and FDM 3D printing process. A basic Thrust jig setup was constructed thrust test have been carried out to assess the performance of 3D printed variant in comparison to the commercially available metal variant. Temperature readings were also taken to assess the structural integrity with increase in temperature. From the tests it has been observed that the 3D printed outrunner BLDC motor performs at par with the metal version motor and certainly results in weight reduction which shall be helpful in higher flight time of Unmanned aerial vehicles (UAVs). This conserves energy of the UAVs and drones and they have a longer flight time and payload carrying capacity. © 2022

关键词： Brushless DC motors

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Hybrid Gradient Boost based Heart Failure Prediction System 5

Hybrid Gradient Boost based Heart Failure Prediction System

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Athalye, Gargee Sarde, Atharva Badgujar, Mayur Gaikwad, Vijay Sondkar, Shilpa Vishwakarma Institute of Technology Instrumentation and Control Engineering Department Maharashtra Pune India Vishwakarma Institute of Technology Electronics and Telecommunication Engineering Department Maharashtra Pune India

ISBN: (纸本)9781665475242

Heart diseases are prevalent in today's world due to many factors like lipid disorder (hypercholesterolemia), corpulence (obesity), increase in triglycerides levels (lipids obtained from esterification fatty acids to glycerol), hypertension, etc. It is estimated that nearly 18 million lives are affected yearly due to various heart diseases. Early detection of such diseases could help save several lives. In the proposed system, heart failure prediction is estimated using the combination of Gradient boost detection and decision trees. The parallel handling approach is used for feature processing to speed up the results and for optimal performance. The generation and discrimination approach are used to verify the outcomes concerning other algorithms and pseudo-codes. This paper uses the data file from the University of California, Irvine Intelligent Systems Repository to test the results. It is observed from several experiments that it provides optimal performance compared to the remaining predictors in the context of f1 score, recall, and accuracy. The ROC curve of Gradient Boost provides a higher deviation for low false positives. The Gradient Boost shows a 0.919 ROC value and 92 % of accuracy with an F1 score of 0.928 and a recall of 0.934. © 2023 IEEE.

关键词： Machine learning

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Emotion detection on phone calls during emergency using ensemble model with hyper parameter tuning

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International Journal of Information technology (Singapore) 2023年第2期15卷 745-757页

作者： Selvan, A. Kalai Nimmi, K. Janet, B. Sivakumaran, N. C-DAC Thiruvananthapuram India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India

The paper’s main focus is on speech-based emotion detection in Malayalam phone call records, including emergency calls [(emergency response support system (ERSS)] and elicited *** emotions taken into consideration are fear, anger, sad, disgust, panic, anxious are among the emotions considered, while all other emotions are labeled as others. When reporting an emergency, callers commonly exhibit emotional stress, which results in speech output discrepancies. In such uncontrolled situations, determining caller qualities is exceedingly challenging due to poor speech quality and background noise. Fear, anger, and sadness are the three basic emotions expressed during an emergency. In order to classify the emotions, this paper used a bagging and boosting ensemble model. Using the Malayalam speech emotion dataset (MSED), the proposed support vector machine ensembles with Bagging with radial basis function kernel function were found to have an accuracy of 93.01%. Elicited and emergency speech from emergency calls are used to train support vector machine ensembles. For comparison of the performance of the model, we have used RAVDESS and Emo-DB. The accuracy obtained were 81.40% while using the RAVDESS dataset and 93.01% while using Emo-DB dataset which is high when compared to existing machine learning and ensemble model. The performance of boosting models was comparatively lesser than that of the Bagging model. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Emergency response support system (ERSS) Ensemble model Mel frequency cepstral co-efficient (MFCC) Speech emotion detection (SED) Support vector machine (SVM) ZeroCrossing rate (ZCR)

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Time-Varying Proportionate Least Mean Square Algorithm for an Adaptive Noise Canceller 3

Time-Varying Proportionate Least Mean Square Algorithm for a...

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3rd IEEE International Conference on Applied Electromagnetics, Signal processing, and Communication, AESPC 2023

作者： Rosalin Rout, Nirmal Kumar Das, Debi Prasad School of Electronics Engineering KIIT Deemed to Be University Bhubaneswar India CSIR-Institute of Minerals and Materials Technology Process Engineering and Instrumentation Department Bhubaneswar India

ISBN: (纸本)9798350358742

A novel time-varying proportionate least mean square algorithm (TVPLMS) is proposed in this paper which depends on a time-varying step size during adaptation. The proposed algorithm addresses the issues arising due to the presence of sparsity and the effect of Gaussian noise in physical systems. The proposed TVPLMS algorithm is a modified version of the proportionate least mean square algorithm (PNLMS) which uses a gain matrix proportional to the weight coefficients. Moreover, the PNLMS algorithm is a prominent algorithm developed for systems with sparse impulse response. In this paper, the proposed TVPLMS algorithm is used for an adaptive noise canceller (ANC). Simulation experiments to analyze the behavior of the proposed TVPLMS algorithm for the sparse system show its enhanced behavior. © 2023 IEEE.

关键词： Gaussian noise (electronic)

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Prescribed-Time Mitigation of False Data Attacks in Microgrid Load Frequency control

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IEEE Transactions on Smart Grid 2025年第4期16卷 3256-3268页

作者： Abouzeid, Said I. Chen, Y. Zaery, Mohamed Amrr, Syed Muhammad Abdelhameed, Esam H. Engineering Technology Research Center for Electric Vehicle Driving System and Safety School of Automation Engineering University of Electronic Science and Technology of China and Sichuan Province Sichuan Chengdu611731 China Aswan University Faculty of Energy Engineering Electrical Engineering Department Aswan Egypt K.A.CARE Energy Research and Innovation Center Saudi Arabia KFUPM Control and Instrumentation Engineering Department Saudi Arabia Saudi Arabia

Microgrid load frequency control (LFC) operation relies extensively on cyber networks which significantly increases their susceptibility to false data injection attacks (FDIAs) that can undermine microgrid’s stability. This paper proposes a multi-layer control framework that efficiently detects and mitigates false data injected in the actuator and measurements of the LFC. Firstly, an FDIAs detection layer based on an unknown input observer (UIO) utilizing an adaptive threshold has been proposed. An adaptive threshold aims to enhance detection sensitivity and reduce false alarms by distinguishing between actual attacks and temporal discrepancies in observer error caused by internal disturbances. Secondly, to ensure timely and accurate mitigation of FDIAs, an estimation layer utilizing a prescribed-time unknown input observer (PTUIO) has been designed. PTUIO is designed to be robust against unknown inputs and parameter uncertainty. The PTUIO employs periodic time-delayed output to estimate system states and FDIAs within a predefined time. Afterward, a two-layer robust and resilient LFC system was developed. The first layer ensures the rejection of unknown inputs, including load/generation disturbances and FDIAs based on PTUIO estimations. The second layer incorporates robust state feedback control, which stabilizes the microgrid and accurately tracks the frequency reference. The effectiveness of the proposed control system is confirmed through multiple scenarios by simulations and Hardware-in-the-loop (HIL), demonstrating its robustness and reliability in addressing challenges associated with internal disturbances, and FDIAs within the microgrid. © 2010-2012 IEEE.

关键词： Hardware-in-the-loop simulation

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Analysis of CNN and Feed-Forward ANN Model for the Evaluation of ECG Signal

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Current Signal Transduction Therapy 2023年第1期18卷 1-8页

作者： Mathur, Prerak Sharma, Tanu Veer, Karan Department of Instrumentation and Control Engineering Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar India

Background: Heart disease is considered one of the complex diseases that has affected a large number of people around the world. It is important to detect and identify cardiac diseases at early stages. Objective: A large number of methods are already present that detect various heart diseases;how-ever, there are some limitations to these methods, that have degraded their overall performance. Methods: In this paper, an effective and efficient method based on a convolutional neural network (CNN) and a feed-forward artificial neural network (FFANN) is proposed that can effectively detect cardiac diseases after analysing the electrocardiogram (ECG) signals. In this ongoing study, the transformed signals are used to extract the information from the processed data. The extracted features are then passed to the proposed CNN-FFANN classifiers for training and testing purposes. Results: The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices. Conclusion: The simulated outcomes have proved the proposed CNN-FFANN model to be more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications. © 2023 Bentham Science Publishers.

关键词： artificial intelligence artificial neural network biomedical applications cardiac diseases ECG signal health monitoring system

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The Effect of Insulating Oil Conditions in Transformer Bushing on Dielectric Response Analysis 14

The Effect of Insulating Oil Conditions in Transformer Bushi...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Srisub, Chanatip Maneerat, Noppadol Rojanasunan, Warisanan Buranaaudsawakul, Techatat Pannil, Pittaya Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Electrical Engineering Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Instrumentation and Control Engineering Bangkok Ladkrabang Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Electrical Engineering Bangkok Ladkrabang Thailand Pitchayabundit College Faculty of Engineering Lam Phu Nong Bua Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Instrumentation and Control Engineering Bangkok Thailand

ISBN: (纸本)9798350374605

The bushing is one of the important parts of a transformer. Once the transformer is operated;each transformer part including its bushing starts the degradation mechanism. In some cases, the dielectric strength degradation of the bushing can lead to severe consequences. Thus, the study and diagnosis of the bushing conditions are important. This study aims to investigate the characteristics of the oil-impregnated pressboard bushing under various oil conditions by filling the new mineral oil with a controlled moisture content and letting it reach an equilibrium state to examine the dielectric response of such bushing. To determine the dielectric characteristics of the investigated bushing, A Frequency Domain Spectroscopy (FDS) device was utilized. In the experiment, the dissipation factor, capacitance, and current measurement of the bushing with different water content level were measured. The bushing test object was tested with the frequency range of 1 mHz -1 kHz. The effect of moisture clearly influenced the low-frequency ranges of the FDS. The result will be discussed in the paper. © 2024 IEEE.

关键词： Frequency domain analysis

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