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    PREDICTION OF AIR POLLUTION FROM POWER GENERATION USING MACHINE LEARNING
    (2024-01-31)
    Photsathian, Thongchai
    ;
    Suttikul, Thitiporn
    ;
    Tangsrirat, Worapong
    Electrical energy is now widely recognized as an essential part of life for humans, as it powers many daily amenities and devices that people cannot function without. Examples of these include traffic signals, medical equipment in hospitals, electrical appliances used in homes and offices, and public transportation. The process that generates electricity can pollute the air. Even though natural gas used in power plants is derived from fossil fuels, it can nevertheless produce air pollutants involving particulate matter (PM), nitrogen oxides (NO<inf>x</inf>), and carbon monoxide (CO), which affect human health and cause environmental problems. Numerous researchers have devoted significant efforts to developing methods that not only facilitate the monitoring of current air quality but also possess the capability to predict the impacts of this increasing rise. The primary cause of air pollution issues associated with electricity generation is the combustion of fossil fuels. The objective of this study was to create three multiple linear regression models using artificial intelligence (AI) technology and data collected from sensors positioned around the energy generator. The objective was to precisely predict the amount of air pollution that electricity generation would produce. The highly accurate forecasted data proved valuable in determining operational parameters that resulted in minimal air pollution emissions. The predicted values were accurate with the mean squared error (MSE) of 0.008, the mean absolute error (MAE) of 0.071, and the mean absolute percentage error (MAPE) of 0.006 for the turbine energy yield (TEY). For the CO, the MSE was 2.029, the MAE was 0.791, and the MAPE was 0.934. For the NO<inf>x</inf>, the MSE was 69.479, the MAE was 6.148, and the MAPE was 0.096. The results demonstrate that the models developed have a high level of accuracy in identifying operational conditions that result in minimal air pollution emissions, with the exception of NO<inf>x</inf>. The accuracy of the NO<inf>x</inf> model is relatively lower, but it may still be used to estimate the pattern of NO<inf>x</inf> emissions.
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    Single CFTA-based dual-mode biquadratic filter
    (2018-06-08)
    Channumsin, Orapin
    ;
    Photsathian, Thongchai
    ;
    Tangsrirat, Worapong
    A scheme consisting of only one current follower transconductance amplifier (CFTA) and four passive components is introduced for realizing the novel dual-mode universal biquadratic filter. The introduced filter can perform either in voltage-mode or current-mode, and can realize the highpass, bandpass and lowpass filter responses simultaneously without needing any element matching constraints. The natural angular frequency (ω<inf>o</inf>) and the quality factor (Q) of the filter can be tuned electronically by the transconductance gain of the CFTA. Computer simulations are used to confirm the workability of the proposed circuit.
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    Design and improvement of wireless crayfish breeding system by controlling water temperature and monitoring pH via cloud system services
    (2018-06-08)
    Photsathian, Thongchai
    ;
    Suttikul, Thitiporn
    ;
    Tangsrirat, Worapong
    This work proposes the design and improvement of the wireless cloud system services for controlling water temperature and monitoring the pH-value for the crayfish breeding system. The cooling system employs a set of peltier, microcontroller board, and temperature and pH sensors. The circulate water system is used in order to maintain well-distributed water temperature in the water tank. The record data is displayed via the cloud computing processing system, which can be access the measured data at any time and everywhere by various PDA devices via internet network.
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    Digitally programmable current amplifier
    (2012-12-01)
    Prasertsom, Danucha
    ;
    Photsathian, Thongchai
    ;
    Tangsrirat, Worapong
    In this paper, a the digitally programmable current amplifier (DP-CA) for NMOS integrated circuit implementation is presented. To provide precise digital control of the gain characteristic, the proposed DP-CA comprises current proportional gain blocks that can be digitally controlled. The proposed DP-CA can be operated from a low power supply of ±1.25V. As an application example of the proposed circuit, the digitally programmable current-mode first-order allpass filter is described. Simulation results using 0.35-μm TSMC CMOS process parameters are also included. © 2012 IEEE.