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Implementation of a Face Detection Surveillance Security System in a Rice Farm Using Supervised Machine Learning Techniques
(2025-01-01)
Polset, Siriwan
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Chompoo-inwai, Chow
This paper presents an innovative approach to developing a surveillance security system tailored for the agricultural sector, specifically rice paddy fields in Thailand, by employing supervised machine-learning-based face detection techniques. The proposed system integrates real-time facial recognition with automated notifications sent to the owner via text messages and facial images through the LINE application. Video inputs from on-site CCTV cameras are processed into a sequence of images, from which key facial features are extracted using the Haar-like feature extraction method. Identification of individuals as either authorized owners or intruders is achieved through supervised machine learning utilizing the K-Nearest Neighbors (K-NN) algorithm. Upon detecting an intruder, the system promptly activates an alarm and sends notifications, enhancing real-time security monitoring. The system is implemented using a Raspberry Pi microprocessor and is powered by a stand-alone solar energy system, ensuring sustainability and operational efficiency. Performance evaluation includes extensive testing, verification, and comparison against conventional methods using the widely recognized Labeled Faces in the Wild (LFW) dataset. Experimental results demonstrate that the proposed system achieves an average accuracy of 86.86%, slightly surpassing traditional techniques. Additionally, it exhibits a significantly improved recognition speed, averaging 8.3 seconds per detection. Robustness and adaptability were further assessed by evaluating the system under varying brightness and distance conditions. The findings confirm the system's ability to provide real-time, precise facial recognition of intruders, thereby establishing its effectiveness as an advanced surveillance security solution. The system's operational boundaries for successful intruder recognition and warning alerts are set within a 1 to 3-meter range from the camera. Recognition accuracy was observed at 95% under optimal conditions and 83% in more challenging scenarios, while the warning alert system demonstrated a success rate between 86% and 97%. These results highlight the proposed system's superior performance and reliability, making it a valuable security solution for agricultural applications.
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Advanced Design and Implementation of a 2-Channel, Multi-Functional Therapeutic Electrical Stimulator
(2024-10-01)
Lakatem, Rujira
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Boontaklang, Suttipong
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Chompoo-inwai, Chow
This research introduces the design, implementation, and rigorous evaluation of a novel 2-channel, multi-functional therapeutic electrical stimulator, meticulously engineered to meet the stringent demands of contemporary clinical applications. The device integrates a high-speed R-2R ladder DAC and a sophisticated pulse generator unit, capable of producing twelve essential current waveforms with fully adjustable parameters, including pulse amplitude, pulse duration, and pulse repetitive frequency. The proposed driving stage unit ensures precise voltage-to-current conversion, delivering stable and accurate output currents even under varying load conditions, which effectively simulate the diverse impedance characteristics of human tissue. Extensive testing confirmed the compliance with international medical standards, notably IEC 60601-1, IEC 60601-1-2, and IEC 60601-2-10. The experimental results underscore the device’s consistent operation within prescribed safety and performance thresholds, with all deviations in pulse parameters remaining well below the permissible limits. Furthermore, the proposed electrical stimulator demonstrated exceptional stability across variable load conditions, as evidenced by minimal amplitude errors and high correlation between waveform characteristics. These findings highlight the proposed device’s robustness and its potential as a versatile tool for a wide range of therapeutic applications, including pain management, muscle stimulation, and nerve rehabilitation, thus marking a significant advancement in the field of therapeutic electrical stimulation.
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Optimum Generated Power for a Hybrid DG/PV/Battery Radial Network Using Meta-Heuristic Algorithms Based DG Allocation
(2023-07-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-inwai, Chow
This paper presents four optimization outcomes for a diesel generator (DG), photovoltaic (PV), and battery hybrid generating radial system, to reduce the network losses and achieve optimum generated power with minimum costs. The effectiveness of the four utilized meta-heuristic algorithms in this paper (firefly algorithm, particle swarm optimization, genetic algorithm, and surrogate optimization) was compared, considering factors such as Cost of Energy (COE), the Loss of Power Supply Probability (LPSP), and the coefficient of determination (R<sup>2</sup>). The multi-objective function approach was adopted to find the optimal DG allocation sizing and location using the four utilized algorithms separately to achieve the optimal solution. The forward-backward sweep method (FBSM) was employed in this research to compute the network’s power flow. Based on the computed outcomes of the algorithms, the inclusion of an additional 300 kW DG in bus 2 was concluded to be an effective strategy for optimizing the system, resulting in maximizing the generated power with minimum network losses and costs. Results reveal that DG allocation using the firefly algorithm outperforms the other three algorithms, reducing the burden on the main DG and batteries by 30.48% and 19.24%, respectively. This research presents an optimization of an existing electricity network case study located on Tomia Island, Southeast Sulawesi, Indonesia.
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ANC System Modeling and Simulations in a Wide-Area Virtual Environment Using FDTD Approach with Perfectly Matched Layer Absorbing Boundaries
(2021-12-01)
Sookpuwong, Chadaporn
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Chompoo-inwai, Chow
This paper presents a model and simulation results of the active noise control (ANC) system in a wide-area virtual environment using the acoustics finite-difference time-domain (FDTD) propagation approach defined by particle velocities and sound pressure level (SPL) within a certain absorbing boundary conditions called a perfectlymatched layer (PML) technique. The ANC excitation applies a single-frequency noise source with an adaptive feedforward configuration. The FDTD algorithm is used to model the area of interest acoustically, including a desired quiet zone, considering the effects from a primary path, a secondary path, and a feedback path. A processing unit of the ANC system based on the least mean-squared (LMS) algorithm is utilized to synthesize a cancelling noise using a secondary loudspeaker. A single-channel feedforward ANC system used in this paper is modified from the proposed multi-channel models and forms. Acoustics FDTD propagation results can be used to determine the optimum placements for ANC sensors and actuators. The SPL numerical and graphical results are plotted to demonstrate the performance of the proposed ANC system. All of the simulation results confirm that the proposed modeling approach can be combined with wide-area acoustic simulations and a feedforward LMS adaptive algorithm. The proposed model also provides a way for the optimum placement of the ANC sensors and actuators before being used in a more-complex practical environment
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A multi-channel feedforward ANC system using a novel FXLMS algorithm in solving classroom aviation-noise problems
(2019-11-01)
Sookpuwong, Chadaporn
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Chompoo-inwai, Chow
Adaptive or Active Noise Control (ANC) system is now widely used in many applications. In this paper, a novel multi-channel feedforward ANC system is presented to lower the level of the aviation noises which occurred in the KMITL University's classrooms. A system is simulated in MATLAB/Simulink environment. The proposed multichannel feedforward ANC system configurations including related equations are described in this paper. The modified Filtered-X Least-Mean-Square (FXLMS) method is used for updating the weight vector in ANC control block diagram in order to achieve the optimal weight vector resulting in minimal errors. How to find bounds on the step size and how to estimate the rate of convergence as well as the steady-state errors of the proposed system with FXLMS algorithm are also elaborated. The secondary-path effects are considered and compensated in this research. The simulation results are presented in terms of noise attenuation capabilities and the Mean-Square-Error (MSE) convergence time. Simulation results and conclusions presented in this paper will be used for further analysis to improve the system performance of the proposed ANC methodology before implementing in the actual classroom.

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