This research presents a Digital Twin of an Aquarium for Water Quality Monitoring, developing a virtual model that displays real-time key water parameters, including pH level, temperature, flow rate, and dissolved oxygen. Sensor data is processed and visualized through a Graphical User Interface (GUI) to reflect the real-time status of the virtual aquarium. This system enables accurate water quality monitoring and analysis while reducing reliance on expensive software solutions.
มีเป้าหมายในการสร้าง Digital Twin ของตู้ปลา เพื่อใช้เป็นกรณีศึกษาในการตรวจสอบคุณภาพน้ำ โดยนำเซ็นเซอร์ตรวจวัด ค่าความเป็นกรด-ด่าง (pH), อุณหภูมิ, อัตราการไหลของน้ำ และออกซิเจนละลายน้ำ มาประมวลผลและแสดงผลผ่านอินเทอร์เฟซกราฟิกแบบเรียลไทม์ ระบบนี้สามารถใช้เป็นสื่อการสอนด้าน คอมพิวเตอร์ช่วยงานเทคโนโลยีการผลิต โดยไม่ต้องพึ่งพาซอฟต์แวร์ราคาแพง ซึ่งช่วยลดต้นทุนและเพิ่มโอกาสในการศึกษา Digital Twin อย่างมีประสิทธิภาพ
คณะครุศาสตร์อุตสาหกรรมและเทคโนโลยี
This research confirms the potential of bamboo fiber as a sustainable raw material for the textile industry, demonstrating exceptional properties that meet both functional requirements and environmental friendliness. The study focuses on integrating sustainability concepts with material innovation, encompassing fiber property analysis, production process development, and product design. The research objectives were to: 1) develop the properties of bamboo fiber for production; 2) study factors in designing environmentally friendly textile products from bamboo fiber; and 3) forecast future prospects for environmentally friendly textile product design using bamboo fiber. The findings revealed that 60-day-old bamboo possessed optimal properties for fiber separation, with an average fiber size of 5.32 μm, smaller than other natural fibers, resulting in superior moisture absorption and ventilation properties. When blended with recycled polyester fiber in a 30:70 ratio, the yarn exhibited strength and unique tactile characteristics. Although the antibacterial properties against Staphylococcus aureus were low, the fibers demonstrated excellent whiteness and softness. Factor analysis identified four key components in product design: Local Materials, Green Products, Healthy, and Sustainability. Consumer satisfaction evaluation of the prototype products showed high levels of acceptance, with the model explaining 84.7% of consumer satisfaction. The developed production process reduced chemical usage and hazardous waste. Furthermore, utilizing fast-growing bamboo minimized long-term environmental impact, contributing to sustainable development in Thailand's rural communities across economic, environmental, and occupational stability dimensions. The research demonstrates that developing bamboo fiber blended with recycled polyester creates sustainable products that meet consumer demands for health consciousness, local material utilization, and green product promotion. Commercial implementation of these products can enhance economic value and promote environmentally friendly product development in the future.
คณะอุตสาหกรรมอาหาร
This research investigates active packaging films made from polyvinyl alcohol (PVA) and nanocellulose fibers (NFC), incorporating silver nanoparticles (AgNPs) synthesized from Terminalia chebula extract, which possesses antibacterial and antifungal properties. The developed films were tested for their mechanical properties, microbial inhibition, and biodegradability. The results showed that the addition of AgNPs from Terminalia chebula enhanced product protection and effectively extended the shelf life of strawberries while being environmentally friendly.
คณะวิทยาศาสตร์
Recruitment is a crucial process that enables organizations to select candidates whose qualifications match the requirements of a given position. However, this process often faces challenges related to data management, delays, and human bias. This research aims to design and develop an intelligent web application for employee recruitment using artificial intelligence (AI) technology to evaluate and score candidates' suitability for job positions. The system leverages data analysis techniques on resumes and a qualification-matching process based on predefined criteria. Developed using Agile principles, the system employs Natural Language Processing (NLP) to analyze resumes, assess candidates’ qualifications, skills, and experience, and utilizes Machine Learning to predict and rank suitability. The system consolidates data from multiple sources into a unified database to reduce redundancy and input errors. Additionally, it presents insights through a dashboard, enabling HR teams to make more effective hiring decisions.