Thailand Electrical Engineering Journal (TEEJ)

Comparative study of ANN and XGBoost in Water Level Prediction: A Case Study of the Pasak River in Thailand

สุรศักดิ์ พบวันดี, ณัฎฐ์ โอธนาทรัพย์ — Sat, 20 Dec 2025 00:00:00 +0700

Flooding and drought are some of the natural disasters in Thailand. It is a challenge to manage water resources to control the water level. However, many dams have been built to sufficiently hold Thailand's water resource reservation in the dry season. Oversupply water retention lead to flooding in the rainy season. Accurate forecasting of water level is, therefore, one of the important factors to make a decision-making process in controlling water level. This research aims to study machine learning techniques for water level prediction in the Pasak River basin. Extreme Gradient Boosting, a useful machine learning technique, has been implemented. Water level and precipitation of stations PAS001, PAS002, PAS003, and PAS004 are variables, and water level in PAS004 is the target prediction. The study found that ANN and XGBoost algorithm are accurate in predicting water levels. The evaluation means absolute error (MAE) by ANN is 2.42 cm. and XGBoost is 3.14 cm. The ANN algorithm uses computer’s memory much more and takes time for learning process longer than XGBoost. Keywords: Machine learning, Water level prediction, Artificial Neuron Network, XGBoost

Design and Development of An Auto Adaptive Multi-Vigilance for Simplified Fuzzy ARTMAP

Sat, 20 Dec 2025 00:00:00 +0700

This paper presents the design and development of a Simplified Fuzzy Resonance Theory (SFAM) with an auto-adaptive multivigilance parameter. Using the concept of adaptation from human familiarity in recognizing things to make a decision of patterns of the artificial neural network more flexible and efficient. Based on the original SFAM architecture that has only one vigilance parameter is defined and it is not adaptive. This vigilance parameter was designed to compare the similarity of the dataset to winner neurons stored in the neural weight layer. causing the flexibility in the model of the artificial neural network to decrease Therefore, an idea was born to design and develop the architecture of a simple adaptive fuzzy resonance theory network to be more flexible by designing an algorithm that can generate multiple vigilance parameters based on the number of nervous weight neural and this parameter can be adapted according to the learning process that the neural network receives from the dataset. Two types of datasets were tested including 1. simple input dataset and 2. complex input dataset. The test results show that the efficiency of the purpose neural network has an accuracy percentage of 96.67% and 95.33% respectively. While traditional networks have accuracy percentages of 86.67% and 92.66%.

The Determination Of Rotational Object Using Discriminant Feature Trace Transform Domain

Sat, 20 Dec 2025 00:00:00 +0700

This paper describes how to enhance the algorithm for determining the orientation of an object by predicting the orientation based on trace transform domain data. The image data trained by machine learning are transformed by the trace transform algorithm rather than being explicitly learned. The output of the trace transform algorithm is 2D data, which is then reduced to 1D data via the DFTF process. The 1D data is then further processed by machine learning. In the experiment, it was determined that the proposed system has three machine learning algorithms with the highest test accuracy from the database of water bottles and various produce databases. The accuracy of the following algorithms, Naïve Bay, Random Forest, and Support Vector Machine, is 98.99%, 95.63%, and 93.2%, respectively

The effect of grey and black floating platforms on the power conversion of bifacial solar cells

Sat, 20 Dec 2025 00:00:00 +0700

This research examines the impact of gray and black floating platforms on the power output of bifacial solar cell systems. Factors studied include the average power of the solar cell system (APSC), irradiation, wind speed, surface morphology, light absorption, and light reflection (R%) for both gray and black platforms. Solar panels were installed at an 8-degree tilt angle facing south. Findings indicate that gray platforms demonstrate notably higher R% than black platforms, resulting in a ~0.46% power output increase for solar panels on gray platforms compared to black ones.

A preference-based matching mechanism for participants in peer-to-peer energy market

Chuppawit Sompoh, Pikkanate Angaphiwatchawal , Surachai Chaitusaney — Sat, 20 Dec 2025 00:00:00 +0700

Due to an awareness of the climate change crisis and a cost reduction of renewable energy technology, more residential electricity consumers tend to install renewable energy technology in their homes and become prosumers. Consequently, an alternative energy market called peerto-peer energy market is introduced allowing these prosumers to trade energy with neighborhoods. This paper proposes a matching mechanism for participants in the peer-to-peer energy market which allows peers to match with preferred neighborhoods. This proposed mechanism can be applied with multiple peers’ preferences and also has no difference in matching results whether buyers or sellers start the matching process, which can be implied that equality among participants is secured. A matching mechanism between three buyers and three sellers is simulated in two scenarios. The first one is a scenario in which buyers start the matching process and sellers start the process in the second scenario. The simulation result shows that both matching results are identical.

BreathSmart Pro+: Innovative Breath Analysis and Lung Exercise Device with Blood Oxygen Saturation (SpO2) Monitoring for Post-COVID-19 Recovery

Sat, 20 Dec 2025 00:00:00 +0700

It is essential to engage in lung rehabilitation for individuals who have recently recovered from COVID-19 and are experiencing pneumonia. The rehabilitation process involves incorporating movements or exercises that gradually promote the restoration of flexibility in the lung tissue and air sacs, allowing them to regain their full capacity. This research presents a device for analysis and managing lung capacity in patients who have recovered from COVID-19, aiming to promote lung function and continuous pulmonary rehabilitation. The same device also includes an evaluating of blood oxygen saturation levels by measuring the quantity and rate of airflow through sensors that detect air pressure differentials. Additionally, the concentration of oxygen in the blood (SpO2) is measured through the assessment of oxygen levels at the fingertip. The test results compared to the standard instruments showed that the Peak Flow and Flow Rate values were within 99% accuracy. However, the SpO2 measurements had an error range of approximately ±1. This can be applied to patients to enhance their lung function assessment, pulmonary rehabilitation, and SpO2 evaluation, especially for those recovering from COVID-19 or individuals with respiratory system issues. It provides greater convenience in accessing these assessments, and the data can also be further analyzed to gain valuable insights into the overall respiratory system functioning and lung health of patients.