Developing a Smart Farming Simulation Utilizing LoRa Communication and Presenting Knowledge on LoRa Communication System Components
เทคโนโลยี LoRa (Long Range) เป็นเทคโนโลยีที่สามารถสื่อสารข้อมูลได้ระยะไกลที่สุดในหลักกิโลเมตร ใช้พลังงานต่ำและไม่มีค่าบริการรายเดือน จึงเหมาะกับการพัฒนาการเกษตรซึ่งมีพื้นที่กว้างและWifiเข้าไม่ถึง ในรูปแบบของการทำเกษตรอัจฉริยะด้วยระบบการสื่อสารแบบLoRa
คณะวิศวกรรมศาสตร์
A high-pressure gas storage tank made from composite materials, including carbon fiber, resin, and plastic, is designed for storing compressed natural gas (CNG) or hydrogen. This type of tank is classified as a Type IV high-pressure vessel. In this research, it is designed to operate at a pressure of 250 bar for the transportation of compressed natural gas.
คณะวิทยาศาสตร์
In raising crickets for meat consumption, the growth rate and growth period of crickets are important data used to identify the number of crickets per breeding area at each age. Therefore, the researcher has an idea to create a system for monitoring the growth rate of crickets in a closed system using an infrared camera combined with computer image processing to study the growth and identify the growth period of crickets at each age in order to obtain knowledge that can be disseminated to farmers to improve the breeding process for maximum efficiency.
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.