Numsomran, Arjin
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Numsomran, Arjin
Alternative Name
Numsomran, A.
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Email
arjin.nu@kmitl.ac.th
15 results
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Item type:Publication, Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems(2023-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A design of fuzzy PID controller based on ARM7TDMI for coupled-tanks process(2012-12-01); ; Pholkeaw, PhayuppThis paper presents the design of Fuzzy PID controller based on ARM7TDMI for coupled-tanks process. The research is developed from previous studying of modified quadrupled-tanks process in the case of non-interacting second order process. The PID controller is designed by root locus technique, then it is improved a performance by combining with Fuzzy inference system for desired output response specification. Furthermore, PID Fuzzy inference system has been approximated into lookup table scheme for properly running on ARM7 embedded system. With the proposed method, the efficiency control algorithm can be performed on ARM7 platform that has a good performance and affordable costs. The experimental results have been shown that the design of Fuzzy PID embedded controller can achieve the performance specification requirement in level control. © 2012 ICROS. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Event-Based LQR Control for Rotary Inverted Pendulum Using Wireless Networked Control System(2023-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Development of a Lithium-Ion Battery Analyzer for Data-Driven Analysis of Battery Performance(2024-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The PIDmn Controller Tuning for Temperature and % RH Control in a Chamber Using Simulink Desktop Real-Time(2023-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The design of PID controller for track following control of hard disk drive using Coefficient Diagram Method(2011-12-01); ; Tipsuwanporn, VittayaThis paper presents the design of PID controller for track following control of hard disk drive using Coefficient Diagram Method (CDM). For this reason, Track following control of hard disk drive process has many disturbances from internal and external sources, so determining satisfactory controller parameters to stabilize control system and satisfying response specification is difficult. The proposed PID controller design using Coefficient Diagram Method (CDM) based on proper choosing the coefficients of the characteristic transfer function of the closed loop control system in accordance with stability index and equivalent time constant is applying an uncomplicated and convenience technique to controller design of Track following control in hard disk drive process. Matlab simulation results shown that CDM design is fairly stable and robust whilst giving the convenience in controller's parameters adjustment. © 2011 ICROS. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, TinyML Speech Classification Embedded Ai Module for Hand Rehabilitation Device(2025-01-01); Recent advancements in artificial intelligence and machine learning have significantly improved healthcare, especially in the development of assistive technologies for rehabilitation. This paper introduces a method for hand rehabilitation that utilizes the capabilities of tiny machine learning to enhance speech classification in a rehabilitation device. The proposed method employs a CNN model capable of classifying speech signals that are indicative of different hand movement patterns. Patients emit these speech signals during prescribed hand exercises, which are crucial for their rehabilitation process. The main focus of this study is on training and deploying a speech classification system that can work in the resource-limited environment of TinyML platforms. We detail the process of capturing speech data, preprocessing it, and extracting the most features relevant to different hand movements. Our results show that using TinyML to help with hand rehabilitation works. The method we came up with shows how TinyML could change the way rehabilitative devices are controlled, and it also shows us what personalized and easy-to-use rehabilitation tools might look like in the future. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Embedded Sensor Data Fusion and TinyML for Real-Time Remaining Useful Life Estimation of UAV Li Polymer Batteries(2025-06-01); The accurate real-time estimation of the remaining useful life (RUL) of lithium-polymer (LiPo) batteries is a critical enabler for ensuring the safety, reliability, and operational efficiency of unmanned aerial vehicles (UAVs). Nevertheless, achieving such prognostics on resource-constrained embedded platforms remains a considerable technical challenge. This study proposes an end-to-end TinyML-based framework that integrates embedded sensor data fusion with an optimized feedforward neural network (FFNN) model for efficient RUL estimation under strict hardware limitations. The system collects voltage, discharge time, and capacity measurements through a lightweight data fusion pipeline and leverages the Edge Impulse platform with the EON™Compiler for model optimization. The trained model is deployed on a dual-core ARM Cortex-M0+ Raspberry Pi RP2040 microcontroller, communicating wirelessly with a LabVIEW-based visualization system for real-time monitoring. Experimental validation on an 80-gram UAV equipped with a 1100 mAh LiPo battery demonstrates a mean absolute error (MAE) of 3.46 cycles and a root mean squared error (RMSE) of 3.75 cycles. Model testing results show an overall accuracy of (Formula presented.), with a mean squared error (MSE) of 55.68, a mean absolute error (MAE) of 5.38, and a variance score of 0.99, indicating strong regression precision and robustness. Furthermore, the quantized (int8) version of the model achieves an inference latency of 2 ms, with memory utilization of only 1.2 KB RAM and 11 KB flash, confirming its suitability for real-time deployment on resource-constrained embedded devices. Overall, the proposed framework effectively demonstrates the feasibility of combining embedded sensor data fusion and TinyML to enable accurate, low-latency, and resource-efficient real-time RUL estimation for UAV battery health management. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Efficient fractional order reference model of adaptive controller design for multi-input multi-output thermal system(2020-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Following control of hard disk drive using derivative state constrained optimal H2 controls(2010-12-01); The author presents following control of the hard disk drive using derivative state constrained optimal 2 H controls (H<inf>2</inf>ẋ). The study in this paper concern to the control amount of NRPE (non repeatable position error) and PES (position error signal) of track following control in the hard disk drive process by the effective method. The derivative state constrained optimal H<inf>2</inf> control (H<inf>2</inf>ẋ) has been formulated to suppress the rate of changes of state variables. It is suitable for the oscillated system and able to be used in non linear system. Thus, the derivative state constrained optimal H<inf>2</inf> control (H<inf>2</inf>ẋ) has been applied to track following control in the hard disk drive for increasing the accuracy of head positioning by decreasing the effect from oscillation, variation and non linearity characteristic in its system. Finally, the simulation results show that our proposed method can satisfy the design specifications of track following servo control system effectively. ©ICROS.