Suwalak, Rattapong

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Suwalak, Rattapong
Alternative Name
Suwalak, R.
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rattapong.su@kmitl.ac.th

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    Item type:Publication,
    Determination of dielectric property of construction material products using a novel RFID sensor
    (2012-01-01)
    Suwalak, R.
    ;
    Phongcharoenpanich, C.
    ;
    Torrungrueng, D.
    ;
    Krairiksh, M.
    This paper presents a concept, by which radio frequency (RF) tags are employed as remotely read dielectric-property sensors to determine qualities of some construction material products (CMPs); e.g., light weight concrete (LWC), mortar specimens and concrete. Using the dependency of the read range of the passive RF identification (RFID) sensor system on the electromagnetic properties of CMPs near or in contact with RFID tags, the qualities of CMPs can be determined through their estimated dielectric properties. Theoretical formulation is provided, and numerical simulations are performed for optimal design of passive RFID tag antennas suitable for RFID sensors and for read-range calculations. In addition, a series of measurements is performed to measure read ranges of the passive RFID sensor system for an LWC as an example of CMPs, and these measured read ranges will be processed appropriately to inversely determine the dielectric constant of the LWC under test, which in turn provides information on its qualities. It is found that the novel RFID sensor can be employed to determine the dielectric properties of the LWC under test with reasonable accuracy.
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    Machine learning-based prediction of nutritional status in oil palm leaves using proximal multispectral images
    (2022-07-01)
    Chungcharoen, Thatchapol
    ;
    Donis-Gonzalez, Irwin
    ;
    Phetpan, Kittisak
    ;
    Udompetaikul, Vasu
    ;
    Sirisomboon, Panmanas
    This study evaluated the application of proximal multispectral images accompanied by 4 machine learning approaches for estimating the nutritional status of oil palm leaves. The image responded for five bands: blue, green, red, red edge, and near-infrared regions with a center wavelength of 475, 560, 668, 717, and 840 nm. Average and standard deviation (SD) values from the leaf pixels of each band were extracted, obtaining 5 average and 5 SD values from 5 bands. Thirty-four vegetation variables were generated based on those average and SD values. In total, forty-four variables consisted of 10 average-and SD-based features, and 34 vegetation variables were used as the input candidates for analyses against 10 target variables: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn), boron (B), and chlorophyll (SPAD). No significant input came out for modeling with P and Zn based on the stepwise selection. Therefore, 8 nutritional models were proposed in this study. A training set with 50 samples was used to be modeled for each target, and a test set with 15 samples was employed to evaluate the models' performances. Based on random forest (RF), support vector regression (SVR), partial least square regression (PLSR), and artificial neuron network (ANN) applied to be modeled, the models for chlorophyll, N, and Ca predictions were acceptable for screening, and those for K and Mg predictions were acceptable for rough screening. The chlorophyll model developed based on the RF had the predictive statistics in terms of coefficient of determination for prediction (r<sup>2</sup>), root mean square error of prediction (RMSEP), and standard error of prediction (SEP) of 0.752, 5.46 SPAD, and 5.65 SPAD, respectively. The other 2 screening models developed based on SVR and RF for N and Ca, respectively, gave the performances with the r<sup>2</sup>, RMSEP, and SEP ranging from 0.655 to 0.718, 0.12 to 0.17%, and 0.12 to 0.18%, respectively. In the case of the 2 rough screening models established using the RF algorithm, the predictive statistics ranged from 0.496 to 0.530 for the r<sup>2</sup> and 0.07–0.16% for both RMSEP and SEP. In this study, the Fe, Mn, and B models had poor results presenting the range of r<sup>2</sup>, RMSEP, and SEP of 0.308–0.491, 2.39–72.9 ppm, and 2.45–62.8 ppm, respectively. Based on the results, this study confirmed that the proximal multispectral information of oil palm leaves had enough significance to account for the status of chlorophyll and macro-nutrients: N, K, Ca, and Mg in the leaves.
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    Hybrid machine learning models: A comprehensive, data-driven evaluation with diverse data partitioning strategies for net radiation estimation
    (2025-04-30)
    Bajao, Kristian Lorenz
    ;
    Phetpan, Kittisak
    ;
    Chophuk, Ponlawat
    ;
    Suwalak, Rattapong
    Surface net radiation (Rn) is crucial for climate modeling and agricultural management but is often not readily available, especially in regions like Thailand. Accurate prediction of Rn is essential for estimating evapotranspiration, which is vital for irrigation planning and agricultural productivity. This study develops a hybrid machine learning framework that incorporates K-Nearest Neighbors (KNN) for missing data imputation, Random Forest-Recursive Feature Elimination (RF-RFE) for feature selection, and machine learning models (Multi-layer Perceptron, K-Nearest Neighbors, and Random Forest) for prediction. The research evaluates various data partitioning methods, including hold-out split, K-fold cross-validation, and growing-window forward-validation (gwFV), alongside hyperparameter tuning using GridSearch to enhance model robustness and prevent overfitting. The primary objectives are to develop and evaluate the hybrid ML models for daily Rn estimation using basic meteorological inputs (temperature, relative humidity, and sunshine duration), assess the impact of different input combinations on prediction accuracy in Sawi, Chumphon, Thailand, and compare data partitioning techniques to determine the optimal model performance. Utilizing FAO56PM-calculated Rn as a reference, this study finds that the Random Forest model, with average temperature and sunshine duration (M2) as inputs evaluated under the gwFV method, achieves the highest stability and high accuracy (R² of 0.972, RMSE of 0.457 MJ m<sup>-2</sup> day<sup>-1</sup>, and MAPE of 3.50%). The Random Forest demonstrates strong generalization capabilities, making it a reliable choice. Even models using only sunshine duration (M3) perform adequately, offering a solution when data availability is scarce. This study concludes that hybrid machine learning models, combined with careful data partitioning, significantly improve Rn estimation. These advancements provide valuable insights for climate modeling, agricultural management, and irrigation scheduling, particularly in data-scarce regions.