Cooling suit with two-phase flow heat-exchange system is a state-of-the-art heat sink, designed for thermal dissipation in fire fighter, racing driver and worker who needs to wear Personal Protective Equipment (PPE). The liquid cooling system with gas injection can enhance heat transfer performance and continuously maintain the temperature at 18-20 degree Celsius.
นักดับเพลิง นักแข่งรถ บุคคลที่ต้องสวมใส่ชุด PPE ในการทำงาน เป็นอาชีพที่มีความเสี่ยงที่จะเสียชีวิตจากโรคลมแดด (heat stroke) ดังนั้นจึงมีแนวคิดในการใช้องค์ความรู้ทางวิศวกรรมเครื่องกลมาออกแบบและสร้างนวัตกรรมใหม่ สำหรับกลุ่มบุคคลที่มีความเสี่ยงจากโรคลมแดด
คณะวิศวกรรมศาสตร์
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คณะเทคโนโลยีการเกษตร
In the present day, interest in health and the consumption of chemical-free food has been steadily increasing, particularly in homegrown produce such as Phoenix oyster mushrooms (Pleurotus pulmonarius), which are highly nutritious and suitable for weight control. However, small-scale mushroom cultivation often faces challenges related to unsuitable environmental conditions, such as unstable temperature and humidity, which affect the growth and quality of the mushrooms. The development of an automatic temperature and humidity control system plays a crucial role in addressing these issues by utilizing sensor technology to monitor and adjust environmental conditions with precision. This helps enhance production efficiency, reduce human errors in manual control, and promote safe food production at the household level. Additionally, it helps lower production costs and supports the concept of sustainable living. The adoption of this technology is considered an important innovation in improving the quality of mushroom cultivation and increasing sustainability in food production.
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.