
The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.
In one, docking is defined as “when one incoming spacecraft rendezvous with another spacecraft and flies a controlled collision trajectory in such a manner to align and mesh the interface mechanisms”, and defined docking as an on-orbital service to connect two free-flying man-made space objects. The service should be supported by an accurate, reliable, and robust positioning and orientation (pose) estimation system. Therefore, pose estimation is an essential process in an on-orbit spacecraft docking operation. The position estimation can be obtained by the most well-known cooperative measurement, a Global Positioning System (GPS), while the spacecraft attitude can be measured by an installed Inertial Measurement Unit (IMU). However, these methods are not applicable to non-cooperative targets. Many studies and missions have been performed by focusing on mutually cooperative satellites. However, the demand for non-cooperative satellites may increase in the future. Therefore, determining the attitude of non-cooperative spacecrafts is a challenging technological research problem that can improve spacecraft docking operations. One traditional method, which is based on spacecraft control principles, is to estimate the position and attitude of a spacecraft using the equations of motion, which are a function of time. However, the prediction using a spacecraft equation of motion needs support from the sensor fusion to achieve the highest accuracy of the state estimation algorithm. For non-cooperative spacecraft, a vision-based pose estimator is currently developing for space application with a faster and more powerful computational resource.

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.

คณะอุตสาหกรรมอาหาร
A new jelly snack alternative for health-conscious individuals—delicious, convenient, and gut-friendly. Rich in probiotics and prebiotics, packed with antioxidants, and essential vitamins. Suitable for health enthusiasts and lactose-intolerant individuals. Free from artificial colors and flavors

คณะเทคโนโลยีการเกษตร
This research aimed to (1) analyze the problems and needs in designing labels for catfish strip products of the Lam Sai Phatthana Community Enterprise Group, (2) develop the labels for catfish strip products, and (3) evaluate the satisfaction levels of consumers and community group members with the developed labels. The study involved 17 members of the community enterprise group and 151 consumers. Research methods included in-depth interviews and questionnaires to gather satisfaction data. The results showed that the newly developed product labels were effective in attracting attention and building trust in the product. The satisfaction levels among the Lam Sai Phatthana Community Enterprise Group members and consumers were high (x ̅= 4.17 and 3.75, respectively).