The Kachatthai Project in Surin Province has been developed as a space to promote and generate income for farmers, incorporating a design concept that reflects the unique identity of Surin Province as its main guideline.
จังหวัดสุรินทร์เป็นพื้นที่ที่มีศักยภาพด้านการเกษตรและเป็นที่รู้จักในฐานะแหล่งเลี้ยงช้างที่สำคัญของประเทศไทย อีกทั้งยังมีอัตลักษณ์ทางวัฒนธรรมที่โดดเด่น โดยเฉพาะวิถีชีวิตของชาวพื้นเมืองและการเกษตรแบบดั้งเดิม อย่างไรก็ตาม เกษตรกรในพื้นที่ยังคงเผชิญกับปัญหาด้านรายได้และการตลาดของสินค้าเกษตร โครงการคชาทัยจึงถูกพัฒนาขึ้นเพื่อเป็นกลไกในการส่งเสริมและสนับสนุนเกษตรกร รวมถึงช่วยเพิ่มมูลค่าทางเศรษฐกิจและยกระดับคุณภาพชีวิตของประชาชนในท้องถิ่น
คณะวิศวกรรมศาสตร์
This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRRlayers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress–strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.
คณะวิศวกรรมศาสตร์
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คณะวิศวกรรมศาสตร์
The Thai Sign Language Generation System aims to create a comprehensive 3D modeling and animation platform that translates Thai sentences into dynamic and accurate representations of Thai Sign Language (TSL) gestures. This project enhances communication for the Thai deaf community by leveraging a landmark-based approach using a Vector Quantized Variational Autoencoder (VQVAE) and a Large Language Model (LLM) for sign language generation. The system first trains a VQVAE encoder using landmark data extracted from sign videos, allowing it to learn compact latent representations of TSL gestures. These encoded representations are then used to generate additional landmark-based sign sequences, effectively expanding the training dataset using the BigSign ThaiPBS dataset. Once the dataset is augmented, an LLM is trained to output accurate landmark sequences from Thai text inputs, which are then used to animate a 3D model in Blender, ensuring fluid and natural TSL gestures. The project is implemented using Python, incorporating MediaPipe for landmark extraction, OpenCV for real-time image processing, and Blender’s Python API for 3D animation. By integrating AI, VQVAE-based encoding, and LLM-driven landmark generation, this system aspires to bridge the communication gap between written Thai text and expressive TSL gestures, providing the Thai deaf community with an interactive, real-time sign language animation platform.