Air Rack is a product designed to address businesses with limited space and budget constraints for server rooms, cooling systems, and noise management. This system enables efficient use of IT equipment in open spaces, supporting both On-premise and On-cloud operations. It converts sensor data into digital information and displays it via a Dashboard, allowing users to monitor, analyze, and control the system remotely. Additionally, Air Rack significantly reduces power consumption and the costs associated with traditional server room management.
Air Rack เป็นผลิตภัณฑ์ที่ออกแบบมาเพื่อธุรกิจจำนวนมากไม่มีพื้นที่ หรืองบประมาณในการสร้างห้องเซริฟ์เวอร์, ระบบระบายความร้อน และการจัดการเสียงรบกวน ซึ่งเป็นผลมาจากการนำอุปกรณ์ไอทีออกมาในที่โล่ง ใช้งานรวมกับระบบแสดงผลและเก็บข้อมูลแบบออนไลน์ ระบบถูกออกแบบมาให้ทำหน้าที่เป็นเครื่องมือที่สามารถเก็บข้อมูลได้อย่างละเอียดและแม่นยำ ทั้งแบบ On-premise และ On Cloud ก่อนจะประมวลผลโดยแปลงสัญญาณจากเซ็นเซอร์ให้เป็นข้อมูลดิจิตอล เพื่อส่งตรงสู่หน้าจอคอมพิวเตอร์ในรูปแบบของ Dashboard ที่ประกอบไปด้วยแผนภูมิ (Charts) เกจ (Gauges) LEDs ตาราง และอื่นๆ ให้คุณสามารถควบคุมกระบวนการ ติดตาม ตรวจสอบ วิเคราะห์ และสั่งงานได้จากระยะไกล
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This project is a carbon safe haven of Bangkok, aspiring to be the prototypal gateway of the future's carbon net zero ambitions. The project aims to answer the fundamental "flaw" of the existing urban fabric, still being extremely inefficient and highly polluting. Conversely, Carbon Oasis would not only create its own energy, but look to provide its excess energy and water surplus' back to the city and its surroundings. Taking parts of the existing city and implementing new concepts to inspire a change in the urban fabric and its people.
วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
Diabetes is a significant global health issue, particularly due to complications related to diabetic wounds. Studies indicate that approximately 15-25% of diabetic patients develop foot ulcers, with more than 50% of severe cases leading to amputation. This results in a substantial decline in the quality of life for patients. Current treatments for diabetic wounds face challenges such as antibiotic-resistant bacterial infections and delayed wound healing, highlighting the need for innovative solutions to accelerate the healing process and reduce the risk of limb loss. Cotylelobium lanceolatum Craib, a medicinal plant long utilized in traditional Thai medicine, is known for its anti-inflammatory and wound-healing properties. This study focuses on developing an extract from Cotylelobium lanceolatum Craib in the form of nano silver (Nano Silver) to enhance the effectiveness of diabetic wound treatment. Nano silver technology enables deeper penetration into the skin, provides potent antibacterial activity, and promotes wound healing by reducing inflammation and stimulating tissue regeneration. The development of nano silver derived from Cotylelobium lanceolatum Craib extract is expected to help reduce chronic wounds in diabetic patients, lower the risk of infection, and decrease the incidence of limb amputation and mortality associated with diabetic wound complications. This research represents a significant step toward creating a safer and more effective treatment alternative for diabetic wound care.
คณะวิศวกรรมศาสตร์
Currently, lithium batteries are widely used in electronic devices and electric vehicles, making the estimation of their State of Health (SOH) crucial. Accurate SOH estimation helps extend battery lifespan, reduce maintenance costs, and prevent safety issues such as overheating or explosions. This project aims to study and analyze mathematical models of batteries and develop SOH estimation techniques using Neural Networks to enhance accuracy and evaluation speed. The experiment involved collecting charge and discharge data from three lithium battery cells under controlled temperature conditions while maintaining a constant current. The current, voltage, and time data were recorded and analyzed to determine the battery capacity for each cycle. These data were then used to train a Neural Network model. The results demonstrated an effective method for predicting battery health status. The outcomes of this project can contribute to the development of a Battery Management System (BMS) that improves battery efficiency and longevity. Additionally, it provides a foundation for applying artificial intelligence techniques in the energy sector effectively.