Special Issue on Innovation in Computing, Engineering Science & Technology 2024-25

This research analyses the challenges faced by ASEAN countries in managing its energy efficiencies and resources due to rapid economic growth, increasing energy demand, and diverse energy infrastructures across member states. This paper explores the energy management policies and strategies within the ASEAN region, focusing on the integration of energy efficiency measures, renewable energy initiatives, and cross-border energy trade. This paper analyse the region’s progress towards its sustainable energy goals, the role of policy frameworks, and the impact of regional collaboration. Key challenges such as energy security, affordability, and environmental sustainability are examined, alongside opportunities for innovation in energy technologies and policy reforms. The findings highlight the importance of a cohesive energy management strategy that balances the diverse needs of ASEAN member states while advancing the region’s transition towards a low-carbon future. This paper provides policy recommendations aimed at enhancing ASEAN’s energy resilience and supporting its sustainable development goals.

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Augmented reality (AR) technology has been increasingly used in developing teaching materials with the aim of sparking more interest in technology (T) and engineering (E) among students in STEM education. In the proposed system, AR is integrated with an educational robot controlled by a KidBright microcontroller board, developed by the Educational Technology research team (EDT) at the National Electronics and Computer Technology Center in Thailand. Moreover, the KidBright program has been implemented over 2,200 Thai schools. To maximize the benefits of the KidBright program, the Assistive System for Collaborative Assembly Task using Augmented Reality (ASCAT-AR) was created with the objective of enabling students to learn and collaborate in assembling robots. Students will work in pairs to assemble robots using the system and learn about mechanics, sensors, and 3D-printed parts. The students were divided into two groups: Group A read the manual and assembled the robot independently, while Group B used the ASCAT-AR system. In addition, AR applications offer smooth graphic rendering at 44-60 frames per second. Evaluation result showed that Group B students had a higher average success rate than average success rate of Group A students. The results showed that users of the ASCAT-AR system were more motivated in learning and obtained more knowledge about robot technology and programming.

Augmented reality (AR) technology has been increasingly used in developing teaching materials with the aim of sparking more interest in technology (T) and engineering (E) among students in STEM education. In the proposed system, AR is integrated with an educational robot controlled by a KidBright microcontroller board, developed by the Educational Technology research team (EDT) at the National Electronics and Computer Technology Center in Thailand. Moreover, the KidBright program has been implemented over 2,200 Thai schools. To maximize the benefits of the KidBright program, the Assistive System for Collaborative Assembly Task using Augmented Reality (ASCAT-AR) was created with the objective of enabling students to learn and collaborate in assembling robots. Students will work in pairs to assemble robots using the system and learn about mechanics, sensors, and 3D-printed parts. The students were divided into two groups: Group A read the manual and assembled the robot independently, while Group B used the ASCAT-AR system. In addition, AR applications offer smooth graphic rendering at 44-60 frames per second. Evaluation result showed that Group B students had a higher average success rate than average success rate of Group A students. The results showed that users of the ASCAT-AR system were more motivated in learning and obtained more knowledge about robot technology and programming.

The integration of machine learning techniques in industrial production has the potential to revolutionize traditional manufacturing processes. In this study, we examine the efficacy of gradient-boosting machine learning models, specifically focusing on feature engineering techniques, applied to a novel dataset with 3D product models pertaining to work moan-hours in metal sheet stamping projects, framed as a regression task. The results indicate that LightGBM and XGBoost surpass other models, and their effectiveness is further enhanced by employing feature selection and synthetic data generation methods. The optimized LightGBM model exhibited superior performance, achieving a MAPE score of 10.78%, which highlights the effectiveness of gradient boosting mechanisms in handling heterogeneous data sets typical in custom manufacturing. Additionally, we introduce a methodology that enables domain experts to observe and critique the results through explainable AI visualizations.

As society ages, the imbalance between family caregivers and elderly individuals increases, leading to inadequate support for seniors in many regions. This situation has ignited interest in automatic health monitoring systems, particularly in fall detection, due to the significant health risks that falls pose to older adults. This research presents a vision-based fall detection system that employs computer vision and deep learning to improve elderly care. Traditional systems often struggle to accurately detect falls from various camera angles, as they typically rely on static assessments of body posture. To tackle this challenge, we implement a feature fusion strategy within a deep learning framework to enhance detection accuracy across diverse perspectives. The process begins by generating a Human Silhouette Image (HSI) through background subtraction. By combining silhouette images from two consecutive frames, we create a Silhouette History Image (SHI), which captures the shape features of the individual. Simultaneously, Dense Optical Flow (DOF) extracts motion features from the same frames, allowing us to merge these with the SHI for a comprehensive input image. This fused representation is then processed using a pre-trained Convolutional Neural Network (CNN) to extract deep features. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is subsequently trained on these features to recognize patterns indicative of fall events. Our approach’s effectiveness is validated through experiments on the UP-fall detection dataset, which includes 1,122 action videos and achieves an impressive 99% accuracy in fall detection.