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Determination of Optimal Checkpoint Intervals for Quintuple Modular Redundancy Real-Time Systems

Transactions of the Korean Institute of Electrical Engineers 2025年第3期74卷 506-515页

作者： Kwak, Seong Woo Yang, Jung-Min School of Electronics Engineering Kyungpook National University Korea Republic of Dept. of Control and Instrumentation Engineering Pukyong National University Korea Republic of

The paper proposes a novel checkpoint management method for a real-time system with a quintuple modular redundancy (QMR) structure wherein multiple fault detections are performed between checkpoints. If the detected faults can be overcome within the context of the QMR structure, they are recovered by simply synchronizing the state values. Otherwise, the system rollbacks to the most recent checkpoint and re-executes the task to recover from the faults. The real-time system having the QMR structure and incorporating the proposed checkpoint scheme is modeled by a Markov chain. Based on the Markov chain model, a probability equation is derived to calculate the likelihood of completing the real-time task within the deadline. The optimal number of checkpoints and fault detections maximizing the probability value are also determined. Numerical experiments are conducted to illustrate the optimization procedure of the real-time system with a QMR structure using the proposed checkpoint management method. Copyright © The Korean Institute of Electrical Engineers.

关键词： Markov processes

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Development of Real-Time Oxygen and Temperature Monitoring System with CI/CD Pipeline Integration and HMI 1

Development of Real-Time Oxygen and Temperature Monitoring S...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kolte, Gaurav Amar Gunnal, Aditya Kolhare, Nilima R. COEP Technological University Dept. of Electronics and Telecommunication Engineering Pune411005 India COEP Technological University Dept. of Instrumentation and Control Engineering Pune411005 India

ISBN: (纸本)9798331518578

The steel manufacturing industry stands as the backbone of global infrastructure development, providing the raw materials essential for constructing everything from skyscrapers to automobiles. Central to this industry's efficiency is the performance of its furnaces, where the transformation of raw materials into high-quality steel occurs. Steel furnace environments require precise monitoring of parameters like temperature and oxygen levels to ensure efficiency, safety, and compliance with environmental standards. This paper presents a novel monitoring device designed to address these needs while incorporating modern software development practices. The device offers real-time monitoring and graph visualization of temperature and oxygen levels, surpassing conventional methods. It introduces the integration of Git, Continuous Integration/Continuous Deployment (CI/CD) pipelines, Real-Time Operating Systems (RTOS) and diagnostic tools, enhancing reliability and facilitating seamless updates. This amalgamation of hardware and software engineering replaces outdated monitoring systems. Through its multifaceted capabilities, this research contributes significantly to the evolution of smart, interconnected industrial systems, promising improved performance and safety in steel furnace operations. The NXP LPC 55s69 micro-controller unit is used for the computation of oxygen and temperature from a zirconia-based oxygen sensor and thermocouple using nernst-einstein equation. For integration of the device with a PLC control we have used RS232 and RS485 communication protocols. © 2025 IEEE.

关键词： Thermocouples

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator 4

Closed-Loop System Identification of an Unstable Nonlinear S...

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4th International Symposium on instrumentation, control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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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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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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Development of Real-Time Oxygen and Temperature Monitoring System with CI/CD Pipeline Integration and HMI

Development of Real-Time Oxygen and Temperature Monitoring S...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Gaurav Amar Kolte Aditya Gunnal Nilima R. Kolhare Dept. of Electronics and Telecommunication Engineering COEP Technological University Pune Dept. of Instrumentation and Control Engineering COEP Technological University Pune

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

关键词： Temperature measurement Performance evaluation Oxygen Furnaces Pipelines Real-time systems Raw materials Safety Steel Monitoring

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Comparative Analysis of Machine Learning Models for Photovoltaic Forecasting

Comparative Analysis of Machine Learning Models for Photovol...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Aayati Jain Ananay Kaushik Palak Thareja Aryan Sinha Anuradha Tomar Dept. of Electrical Engineering Netaji Subhas University of Technology Delhi India Dept. of Instrumentation and Control Engineering Netaji Subhas University of Technology Delhi India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

As solar energy continues to increase its share in the electrical mix, accurate forecasting of photovoltaic (PV) output will play an increasingly critical role in grid integration. This paper presents a comparative study of four machine learning models, K-Nearest Neighbors (KNN), Random Forest, XGBoost, and Linear Regression, in addition to physical and statistical methodologies for forecasting PV power. Real-field data is used in this work to enhance the trustworthiness and importance of the findings. It also shows the influence of certain input parameters, such as air temperature, runtime hours, and dust, on the performance of the models. Also, the assessment of all models is done using several evaluation criteria, which include mean absolute error (MAE), root mean squared error (RMSE), $\mathbf{R}$ squared (R2) and mean percentage error (MPE), in order to guarantee that there is no bias in the evaluation. The result shows XGBoost as the best-performing model.

关键词： Accuracy Temperature Statistical analysis Atmospheric modeling Time series analysis Supervised learning Predictive models Nearest neighbor methods Solar panels Forecasting

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator

Closed-Loop System Identification of an Unstable Nonlinear S...

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First International Symposium on instrumentation, control, Artificial Intelligence, and Robotics (ICA-SYMP)

作者： Benjaset Maneeloke Sudchai Boonto Peerayot Sanposh Yodyium Tipsuwan Natthawut Chinthaned Warit Tanaprasitpattana Dept. of Electrical Engineering Kasetsart University Bangkok Thailand Dept. of Control Systems and Instrumentation Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Computer Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9798331505400

ISBN: (纸本)9798331505417

关键词： Time-frequency analysis Adaptation models Regulators Accuracy Fitting Machine learning Robustness Closed loop systems Complex systems Frequency control

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ML based Non-Invasive Diabetes Detection System using Pulse Decomposition Analysis of PPG Signal 8

ML based Non-Invasive Diabetes Detection System using Pulse ...

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8th International Conference on Signal Processing and Communication, ICSC 2022

作者： Shaan, Bhavya Yadav, Jyoti Prabha, Anju Dept. of Instrumentation and Control Engineering Delhi India Dept. of Instrumentation and Control Engineering Delhi India

ISBN: (纸本)9781665454308

In this paper, Pulse Decomposition Analysis (PDA) of Photoplethysmography (PPG) and physiological parameters is used to develop Non-Invasive (NI) Diabetes Detection system. For this study, dataset of 217 participants from normal, diabetes and prediabetes subjects are used. Further signals are segmented in 5sec window which is filtered using bandpass filter to remove noise. After filtration PDA features are extracted from PPG signal. A total of seven features are extracted. In addition to this physiological parameter such as age, weight and height of the participants are also used as features. For classification a matrix of (7886 times 10) is formed containing seven PPG features and three physiological parameters. Dataset is classified under three classes i.e., Diabetic, Pre-diabetic, and Normal. Two commonly known machine learning classification learning algorithm i.e., ensemble and decision Trees are used for classification. The results shows that 99.3% accuracy is obtained using ensemble tree method. The study shows feasibility of wearable device for non-invasive diabetes detection. © 2022 IEEE.

关键词： Decision trees

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Quantitative analysis of EMG amplitude estimator for surface emg signal recorded during isometric constant voluntary contraction

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Transactions of the Korean Institute of Electrical Engineers 2017年第5期66卷 843-850页

作者： Lee, Jin Dept. of Control and Instrumentation Engineering Kangwon National Univ. Samcheok Korea Republic of

The EMG amplitude estimator, which has been investigated as an indicator of muscle force, is utilized as the control input to artificial prosthetic limbs. This paper describes an application of the optimal EMG amplitude estimator to the surface EMG signals recorded during constant isometric %MVC (maximum voluntary contraction) for 30 seconds and reports on assessing performance of the amplitude estimator from the application. Surface EMG signals, a total of 198 signals, were recorded from biceps brachii muscle over the range of 20-80%MVC isometric contraction. To examine the estimator performance, a SNR(signal-to-noise ratio) was computed from each amplitude estimate. The results of the study indicate that ARV(average rectified value) and RMS(root mean square) amplitude estimation with forth order whitening filter and 250[ms] moving average window length are optimal and showed the mean SNR improvement of about 50%, 40% and 20% for each 20%MVC, 50%MVC and 80%MVC surface EMG signals, respectively. © 2017 The Korean Institute of Electrical Engineers.

关键词： Signal to noise ratio

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