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14th International Conference on Advances in Computing, control, and Telecommunication Technologies, ACT 2023

作者： Sharma, Disha Tiwari, Sheela Dr. B. R. Ambedkar National Institute of Technology Jalandhar Dept. of Instrumentation & Control Engineering India

The paper focuses on the comparative study of Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA) based PID controllers for stabilizing the linearized model of Triple Link Inverted Pendulum. The model is simulated in a MATLAB environment and the simulation results present a comparison on the basis of settling time, steady-state error, and peak overshoot. © Grenze Scientific Society, 2023.

关键词： Ant colony optimization

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Machine Learning based approach for Myocardial Infarction Prediction using real time ECG Signals

Machine Learning based approach for Myocardial Infarction Pr...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Shilpa Sondkar Sharv Apte Aneesh Deshmukh Paritosh Chavan Dept. Of Instrumentation and Control Engineering VIT Pune India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

This project focuses on utilizing machine learning techniques to identify and detect abnormal cardiac conditions through the analysis of Electrocardiogram (ECG) signals. With cardiovascular diseases being a significant global health concern, the need for precise diagnostic tools is paramount. The primary objectives of this study involve identifying specific cardiac conditions, including Myocardial Infarction, Arrhythmias and discerning normal cardiac conditions. Real time ECG signals were taken from Symbiosis hospital and research center pvt. Limited hospital in Satara. The methodology encompasses the acquisition of diverse ECG datasets, preprocessing to enhance signal quality, and feature extraction to characterize different cardiac conditions effectively. Support vector machines (SVMs), logistic regression, kNNs, and XGBoost these machine learning techniques are employed to develop robust classification models. The anticipated outcomes of this research aim to provide accurate models capable of early detection, enabling prompt interventions and, ultimately, enhancing patient outcomes within the domain of cardiovascular health.

关键词： Support vector machines Machine learning algorithms Accuracy Signal processing algorithms Machine learning Cardiac arrest Electrocardiography Myocardium Prediction algorithms Real-time systems

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Thermal Vision RC Tank for Human Detection in Disaster Area Using IoT 5

Thermal Vision RC Tank for Human Detection in Disaster Area ...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Jyotsna, P. Sanjay Surendar, B. Sharan, B. Dhanaselvam, J. Saravanakumar, K. Rajesh, R. Sri Krishna College of Technology Dept of Instrumentation and Control Engineering Coimbatore India Sri Krishna College of Technology Dept. of Electrical and Electronics Engineering Coimbatore India Indian Institute of Technology Department of Engineering Design Chennai India

ISBN: (纸本)9798350385649

In this modern era of technology, still conventional methods (human and trained dogs) are being used to find and rescue the victims, who are buried under the rubble after natural or human-made destruction. Offering a revolutionary solution, this robot outperforms traditional methods of searching for individuals during rescue operations. Another key initiative is the integration of IoT technologies, which will facilitate communications and data exchange. These rescue efforts come with significant risks for both rescuers and the victims aim to save. Compared to current methods, this robot performs exceptionally well in locating people during rescue missions. Its main advantage lies in its incorporation of advanced sensors, particularly those capable of detecting body heat and infrared signals. This technological enhancement allows the robot to identify individuals even in challenging conditions, such as darkness, smoke, or clutter, where regular cameras struggle. © 2024 IEEE.

关键词： Cameras

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A Large-Scale Dataset for Small and Tiny Object Detection from Aerial Imagery

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Procedia Computer Science 2025年 258卷 4228-4237页

作者： Sourabh Verma Himanshu Gupta Om Prakash Verma Dept. of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India Dept. of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India 576104

Aerial object detection has gained significant attention due to its broad applications in areas such as security, surveillance, and agriculture. However, existing datasets present several challenges, such as annotation errors and limited scale, which hinder the accuracy and speed of current detection models. To address this, the NITJ-AIR dataset—an open-source, large-scale dataset has been proposed that aims to advance research in small and tiny object detection. Baseline results are provided for several state-of-the-art detectors, including Faster R-CNN, SSD, YOLO (v3, v4, and v5s), and CornerNet. Further, the performance of these detectors on NITJ-AIR is compared against the VisDrone and AU-AIR datasets. The comparative analysis demonstrates enhanced feature extraction capabilities, yielding a substantial minimum improvement of 8.37% in average precision and 7.65% in mean average precision across models. The developed dataset is available at https://***/himanshugiriraj/NITJ-AIR .

关键词： Aerial object detection NITJ-AIR dataset Overhead imagery Small tiny object detection Visual surveillance

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Partial Discharge Classification with Transformer Neural Networks 14

Partial Discharge Classification with Transformer Neural Net...

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

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

ISBN: (纸本)9798350374605

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 © 2024 IEEE.

关键词： Electric corona

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Partial Discharge Classification With 1D Convolutional Neural Network 14

Partial Discharge Classification With 1D Convolutional Neura...

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

ISBN: (纸本)9798350374605

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. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning Based Categorization of Heart Sounds for Cardiac Arrhythmia Detection

Machine Learning Based Categorization of Heart Sounds for Ca...

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International Conference on Biosignals, Images and instrumentation (ICBSII)

作者： Sreedevi S Nair T Indu Dept. of Instrumentation and Control Engineering NSS College of Engineering Palakkad Kerala India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

Early detection of cardiac arrhythmias is vital for preventing severe cardiovascular conditions and improving patient outcomes. This research focusses on the implementation of machine learning models for classifying heart sounds (HS) to identify arrhythmias effectively. Heart sound signals were analyzed and classified using five machine learning models: XGBoost, Support Vector Machine (SVM), Extra Trees, LightGBM, and Logistic Regression. These models were trained and estimated using benchmarks such as accuracy, precision, recall, and F1-score to judge their performance in discriminating rhythmic and arrhythmic heart sounds. The results indicate that Extra Trees achieved the highest accuracy, followed by LightGBM and Logistic Regression, while XGBoost and SVM demonstrated comparatively lower accuracies.

关键词： Heart Support vector machines Logistic regression Accuracy Arrhythmia Scalability Machine learning Feature extraction Classification algorithms Reliability

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NANOHEXCOOL: Advanced Thermal control for Lithium-Ion Batteries 8

NANOHEXCOOL: Advanced Thermal Control for Lithium-Ion Batter...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Ragul, S. Rajeshwari, M. Meganthan, P. Dhanaselvam, J. Saravanakumar, K. Rajesh, R. Sri Krishna College of Technology Dept of Instrumentation and Control Engineering Coimbatore India Sri Krishna College of Technology Dept. of Electrical and Electronics Engineering Coimbatore India Indian Institute of Technology Madras Department of Engineering Design Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

This study evaluates thermal management strategies for lithium-ion batteries in electric vehicles, addressing safety concerns like thermal runaway and performance degradation under extreme temperatures. A comprehensive assessment considers factors such as production costs, maintenance needs, system complexity, and internal temperature control. Among various techniques explored, a hexagon-structured cooling chamber utilizing nano-fluids emerges as a promising solution. Liquid cooling systems, categorized into direct and indirect systems, are assessed based on fluid interaction with the battery surface. Hexagonal cooling chambers offer innovative thermal regulation, demonstrating efficient mitigation of temperature-related challenges. Numerical simulations confirm the effectiveness of the devised thermal management mechanism in maintaining battery cell temperatures within specified ranges. Implementation of such strategies is crucial for enhancing battery safety and performance in electric vehicles, ensuring reliable operation across diverse temperature conditions while mitigating risks associated with thermal runaway events. © 2024 IEEE.

关键词： Temperature control

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Xception CNN-Ensemble Learning Based Facial Emotion Recognition 6

Xception CNN-Ensemble Learning Based Facial Emotion Recognit...

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6th International Conference on Computing, Communication, control and Automation, ICCUBEA 2022

作者： Shirsath, Supriya Vikhe, Vaishali Vikhe, Pratap Pravara Rural Engineering College Dept. of Computer Engineering Ahmednagar India Pravara Rural Engineering College Dept. of Instrumentation & Control Engineering Ahmednagar India

ISBN: (纸本)9781665484510

Emotions in human is an effective medium to study the mindset of a person. Since, expression on the face of human is significant approach to understand the condition and communicate with him to release the pressure or stress of person. The emotions and behavior have a strong relationship and is noteworthy in non-verbal form of communication. The advancement in medical science and use of image processing tools like Artificial Intelligence (AI) and Machine Learning (ML), can be helpful to recognize emotion, detect stress and depression level of a person. Thus, in this research work emotion recognition system is developed using Convolution Neural Networks (CNNs) with increased dept. and width. CNNs have been shown to enhance prediction accuracy. In terms of proper emotion categorization and accurate prediction, the suggested ensemble technique is effective. In this paper Xception CNN architecture is proposed for accurate facial emotions prediction along with an ensemble model, using Max Voting ensemble technique, mainly contributes in accurate classification. In proposed technique trained CNN models were loaded and for each trained model prediction probabilities were generated. Furthermore, it is then used for Max Voting to generate final emotion prediction. The CNN models are trained and evaluated on FER-2013 dataset. © 2022 IEEE.

关键词： Emotion Recognition

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Further Studies of A Proof of Utkin's Theorem for ITSMC of Second Order Uncertain Plants

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Transactions of the Korean Institute of Electrical Engineers 2023年第10期72卷 1208-1220页

作者： Choi, Myeong-Soo Lee, Jung-Hoon Dept. of Control & Robot Engineering Gyeongsang National University Korea Republic of ERI Dept. of Control & Robot Engineering Gyeongsang National University Korea Republic of

In this paper, the two sliding surface part transformations are investigated for ITSMC (integral terminal sliding mode control) of second-order uncertain linear plants when the input gain uncertainty of the system is not zero i.e. ∆b≠0 as the further study of [33] and as the alternative methods of [33]. As highlighted in [33], the realm of designing ITSMCs encompasses five distinctive approaches: control input transformation, sliding surface full transformation, and three variations of sliding surface part transformations. While the discussion on the first three methods has been comprehensively covered in [33], this paper centers its focus on the previously unexplored last two transformations. These latter two transformations are presented as novel alternatives to the approaches detailed in [33]. The initial three transformations, as elucidated in [33], demonstrate a performance wherein the real output can be effectively predicted through the utilization of solutions derived from the ideal sliding dynamics. These solutions are thoughtfully pre-designed and predetermined through the application of the ideal sliding dynamics. This paper significantly contributes by presenting a comprehensive exploration of the last two transformation methods, achieving performance on par with the first three transformations described in [33]. These final two sliding surface part transformations stand as noteworthy design methodologies for ITSMCs with the same performance as in [33], offering alternative avenues in contrast to those laid out in [33]. The practical value of our key findings is effectively demonstrated through an illustrative example and an extensive simulation study, thereby underscoring the pertinence and applicability of our research outcomes. © The Korean Institute of Electrical Engineers.

关键词： Sliding mode control

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