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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Chatterjee, Sayanti
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Robust Fault Detection Of A Hybrid Control System Using Derivative Free Estimator And Reinforcement Learning Method Chatterjee, Sayanti; Deka, Bhupesh; Debnath, Dipanwita; Tripathy, Sasmita; Agarwalla, Bindu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.644

Abstract

Fault detection in hybrid control systems (HCS) poses significant challenges due to dynamic variations in system dynamics caused by event-based inputs and the existence of unknown large process noise. A novel scheme for optimized robust fault detection of HCS has been proposed and projected here that can effectively handle dynamic system changes and process noise along with the fault detection while achieving high accuracy and reliability. The challenge with the HCS is the presence of a large process noises due to changing of state equations drastically with dynamical input making the fault detection a complex task. The derivative-free estimator minimizes process noise and provides reliable state estimation, while the Markov Decision Process (MDP) framework is employed to optimize fault detection. MDP has been chosen here due to its mathematical introspection for dynamic system's decision-making process when the results are random or under the control of a decision maker. The data generated by the derivative-free estimator is used to train this deep learning model. Simulation studies were conducted to evaluate the scheme’s performance, and additional tests for convergence, optimization, and robustness were performed using MDP infused with adaptive estimators. The efficacy of the proposed estimators has been confirmed on a benchmark problems, namely the liquid level control system for an chemical stirred tank reactor (CSTR) model. Simulation studies has been employed to prove the efficacy of the proposed method. The proposed method achieved 98.6% fault detection accuracy and a 12% mean error reduction compared to existing techniques. It demonstrated robustness under varying noise levels, dynamic conditions and presence of external disturbances . The results confirm the method's effectiveness for robust and optimized fault detection in HCS, offering a scalable, accurate, and noise-resilient solution for real-world industrial systems.
Optimized PCA Infused Liquid Neural Network Deployment for FPGA-Based Embedded Systems Deka, Bhupesh; Chatterjee, Sayanti; Chintalapudi, S Rao; Nikitha, Jajala; Kodavali, Lakshminarayana; Babu, Kancharagunta Kishan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.681

Abstract

The integration of neural networks into FPGA-based systems has revolutionized embedded computing by offering high performance, energy efficiency, and reconfigurability This paper introduces a novel optimization framework integrating Principal Component Analysis (PCA) to reduce the complexity of input data while preserving essential features for accurate neural network processing. By applying PCA for dimensionality reduction, the computational burden on the FPGA is minimized, enabling more efficient utilization of hardware resources. Combined with hardware-aware optimizations, such as quantization and parallel processing, the proposed approach achieves superior performance in terms of energy efficiency, latency, and resource utilization. Simulation results demonstrate that the PCA-enhanced Liquid Neural Network (LNN) deployment significantly outperforms traditional methods, making it ideal for edge intelligence and other resource-constrained environments. This work emphasizes the synergy of PCA and FPGA optimizations for scalable, high-performance embedded systems. A comparison study using simulation results between cascaded feed forward neural network (CFFNN), deep neural network (DNN) and liquid neural network (LNN) has been encountered here for the embedded system to show the efficacy of PCA based LNN. It has been shown from case studies that the average F1score is 98% in case of proposed methodology and accuracy is also 98.3% for high clock value.
Co-Authors Agarwalla, Bindu Babu, Kancharagunta Kishan Chintalapudi, S Rao Debnath, Dipanwita Deka, Bhupesh Kodavali, Lakshminarayana Nikitha, Jajala Tripathy, Sasmita