Crispy Rice-berry Snack is a product made from broken rice-berry rice that has been processed into a snack that is thin and crispy, bite-sized. Broken rice-berry rice is cooked, finely ground, and mixed with other ingredients to increase its nutritional value, such as adding plant seeds, adding plant protein nutrients, and then forming it into sheets using heat. The resulting product is a thin sheet, purple-brown in color, crispy, and has the smell of the ingredients used in the production process. It does not contain sugar or sweeteners. It is used as a snack with tea or coffee. Crispy Rice-berry Waffle is a product that contains complete nutrients, including carbohydrates, protein, and fat, which are derived from the ingredients in the production formula.
เป็นการเพิ่มมูลค่าใหักับข้าวหักไรซ์เบอร์ โดยนำมาแปรรูปเป็นผลิตภัณฑ์อาหารที่รับประทานได้ง่าย
คณะวิศวกรรมศาสตร์
This research project focuses on the design and development of a Manual Control Robot using Load Cell technology to enhance precision and reduce the time required for robot control. The use of automation robots in industries still presents challenges due to the complexity of programming and control. Therefore, developing a manual control system that responds to force input in all directions can significantly improve the efficiency of robots, making them more suitable for tasks requiring precise and intricate control. The study integrates Load Cell sensors, an HX711 amplifier circuit, and an Arduino UNO R3 to develop a control module that translates user-applied forces into commands for an RV-7FRL-D industrial robotic arm. Additionally, MATLAB is utilized for processing Load Cell data to analyze and optimize the robot’s movement accuracy. The results demonstrate that the developed system effectively reduces robot setup time while simplifying and improving control flexibility. This project represents a crucial step in enhancing the capabilities of industrial robots, allowing for seamless human-robot interaction through a manual control system that directly responds to user-applied forces.
วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
This research aims to study the waste management process of horse manure, the production process of organic fertilizer from horse waste, and opinions on the use of innovative organic fertilizer from horse manure. A mixed-method approach, combining qualitative and quantitative research, is employed. The organic fertilizer is produced from horse manure, which is a waste that incurs disposal costs. Through the fermentation process, it is transformed into an environmentally friendly fertilizer containing essential nutrients beneficial to plants. According to the laboratory analysis of the organic fertilizer conducted by the Soil Science Laboratory, Faculty of Agricultural Technology, King Mongkut's Institute of Technology Ladkrabang, it was found that organic fertilizer from horse manure contains essential nutrients for plant growth, including macronutrients, secondary nutrients, and micronutrients. This reflects the potential of horse waste management, the production process of organic fertilizer from horse manure, the efficiency of the organic fertilizer, and strategies for adding value to expand its commercialization.
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.