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.
คณะครุศาสตร์อุตสาหกรรมและเทคโนโลยี
Mulberry Kefir is a fermented drink made from ripe mulberry fruit, made from ripe mulberry juice. Its pinkish-red color is the color of the natural anthocyanin in mulberries. Anthocyanins have antioxidant properties and contain the prebiotic fructo-oligosaccharides, the probiotic microorganisms Lactobacillus and Saccharomeces. It has a sweet and sour taste, is fizzy and has a little alcohol. The taste and fizz come from production technology, which uses a fermentation process from microorganisms. Mulberry kefir is considered a functional beverage made from plants (Plant Based Beverage), suitable for people who are lactose intolerant and those who do not consume beverages made from milk. It makes you feel refreshed after drinking it.
คณะวิทยาศาสตร์
Microalgae are rich in bioactive compounds that may contribute to the growth of probiotics, which require appropriate nutrients, known as prebiotics, to thrive. This study aims to evaluate the effectiveness of crude extracts from intracellular components residues of the microalga Chlorella sp. KLSc61 in promoting the growth of the probiotic bacterium Lactiplantibacillus plantarum JCM1149 under simulated gastrointestinal conditions. The intracellular extracts were obtained using 70% (v/v) ethanol, and their effects on probiotic growth were tested at concentrations of 0.1%, 0.75% and 1.5%. The growth of Lactiplantibacillus plantarum JCM1149 was assessed using the drop plate method. The findings of this study will provide insights into the potential of Chlorella sp. KLSc61 extracts in enhancing probiotic growth, which could lead to the development of synbiotic dietary supplements containing both probiotics and prebiotics. Additionally, this study may serve as a foundation for further research on the role of microalgal extracts in gut health and immune system modulation.
คณะอุตสาหกรรมอาหาร
Spent coffee grounds (SCG) are a byproduct of the coffee brewing process, and their quantity continues to increase due to the growing global coffee consumption. SCG contain beneficial compounds such as polysaccharides, dietary fibers, and antioxidants, which can be utilized in various applications, including prebiotic extraction. This study focuses on extracting prebiotics from SCG using acid hydrolysis and enzymatic hydrolysis methods to evaluate their potential in promoting the growth of beneficial gut microorganisms. The expected results of this research include adding value to coffee industry waste, reducing organic waste, and providing a sustainable approach to developing prebiotic products for use in the food and health industries. Furthermore, this study aligns with sustainable resource utilization and environmentally friendly practices.