This project aims to develop a conceptual prototype of a weapon aiming system that simulates an anti-aircraft gun. Utilizing an optical camera, the system detects moving objects and calculates their trajectories in real time. The results are then used to control a motorized laser pointer with two degrees of freedom (DoF) of rotation, enabling it to aim at the predicted position of the target. Our system is built on the Raspberry Pi platform, employing machine vision software. The object motion tracking functionality was developed using the OpenCV library, based on color detection algorithms. Experimental results indicate that the system successfully detects the movement of a tennis ball at a rate of 30 frames per second (fps). The current phase involves designing and integratively testing the mechanical system for precise laser pointer position control. This project exemplifies the integration of knowledge in electronics (computer programming) and mechanical engineering (motor control).
โปรเจคนี้เกิดจากความสนใจในการพัฒนาระบบที่มีการผสมผสานของ Machine Vision และระบบความคุมกลไกมอเตอร์ 2 แกนแบบ Degrees of Freedom(DoF) เพื่อพัฒนาอุปกรณ์ต้นแบบที่สามารถตรวจจับ ติดตาม และเล็งเป้าหมายได้อย่างมีแม่นยำ ซึ่งหวังเป็นอย่างยิ่งว่าโปรเจคนี้จะมีประโยชน์ต่องานในอนาคตต่างๆที่เกี่ยวข้อง ไม่ว่าจะเป็น ทางการทหาร ทางการแพทย์ หรือทางอุตสาหกรรม
คณะศิลปศาสตร์
Board games are becoming a popular way to connect people, especially in cafés and social spaces. Meanwhile, Thailand’s diverse tourism—rich in nature, culture, and regional charm—attracts visitors worldwide. We combine the thrill of travel with board games to showcase hidden gems in a fun and educational way, leading to the creation of “C(4)ulture Adventure Board Game.”
คณะเทคโนโลยีสารสนเทศ
This research project aims to study and develop a metaverse system for tourism in Horklong Subdistrict, Phitsanulok Province. The primary goal is to create a prototype metaverse system that showcases the cultural and historical tourist attractions in Horklong Subdistrict through virtual reality technology. This will help promote tourism in rural areas that are not yet widely known and enhance the modern promotion of tourist attractions in the province. The development of the metaverse system in this project utilizes virtual reality technology to simulate the experience of touring Horklong Subdistrict via a virtual boat ride. Users will be able to access the system through the Unity platform, which is a tool used to develop 3D and VR applications. The system is designed to allow users to choose to ride a virtual boat and visit various places that have been creatively recreated in a virtual format. These locations are designed and developed using 3D models based on real data collected from the Horklong Subdistrict area. This project is therefore an initiative to sustainably promote cultural and historical tourism in Phitsanulok Province. It has the potential to increase income for local communities as well as to disseminate local knowledge and wisdom to a broader audience. The metaverse system for tourism in Horklong Subdistrict is thus an important project in modernizing the province’s tourism sector and attracting more visitors, both domestically and internationally.
คณะวิศวกรรมศาสตร์
This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRRlayers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress–strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.