The research on improving the strength of solid electrolytes aims to enhance the properties of solid electrolyte materials produced from cement and additives that help develop the cement structure to generate electricity. The main components include sodium chloride (NaCl) and graphite, which contribute to the material’s ability to generate a weak electrical current. The objective is to develop an electricity-generating flooring material. This study involves preparing a mixture of cement, water, sodium chloride (NaCl), and graphite to enhance the material’s electrical conductivity. It is highly anticipated that this research will lead to the development of concrete flooring capable of generating electricity and can be further expanded for future applications.
ในปัจจุบัน ความต้องการใช้พลังงานไฟฟ้าเพิ่มขึ้นนอย่างต่อเนื่อง ส่งผลให้เกิดการพัฒนาเทคโนโลยีและนวัตกรรมใหม่ ๆ เพื่อเพิ่มแหล่งพลังงานทางเลือกที่มีความยั่งยืนและเป็นมิตรต่อสิ่งแวดล้อม หนึ่งในแนวทางที่ได้รับความสนใจ คือการพัฒนาวัสดุที่สามารถผลิตและกักเก็บพลังงานไฟฟ้าได้ในตัวเอง ซึ่งสามารถนำไปใช้ในโครงสร้างพื้นฐานต่าง ๆเช่น พื้นทางเดิน อาคาร หรือพื้นที่สาธารณะ ดังนั้น งานวิจัยนี้จึงมีเป้าหมายเพื่อพัฒนาและปรับปรุงคุณสมบัติของเซลล์อิเล็กโทรไลต์ชนิดแข็งที่มีโครงสร้างพื้นฐานจากซีเมนต์ โดยมุ่งเน้นการเพิ่มความแข็งแรงของวัสดุควบคู่ไปกับการรักษาคุณสมบัติการนำไฟฟ้า เพื่อให้สามารถนำไปใช้งานเป็นวัสดุปูพื้นที่สามารถผลิตกระแสไฟฟ้าได้ งานวิจัยนี้คาดหวังว่าจะเป็นแนวทางสำคัญในการ พัฒนาวัสดุก่อสร้างสามารถต่อยอดไปสู่การประยุกต์ใช้ในอนาคตได้อย่างมีประสิทธิภาพ
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
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คณะครุศาสตร์อุตสาหกรรมและเทคโนโลยี
This aimed to 1) develop an effective augmented reality (AR) media integrated with the metaverse to enhance English phonics and communication skills. 2) To evaluate English pronunciation skills using augmented reality media integrated with the metaverse, and 3) To assess English communication skills through interactions within the metaverse. The sample group comprised 120 Grade 4 students from two classrooms in the first semester of the 2024 academic year, selected through cluster random sampling and divided into experimental and control groups. The research instruments included AR media sets, media quality assessment forms, phonics tests, and English communication skills assessment forms, administered before and after the learning intervention. Data analysis employed mean (x ̅), standard deviation (S), t-tests for independent samples, and one-way analysis of variance (Multivariate Analysis of Variance: One-Way MANOVA) to compare mean score differences between the experimental and control groups. Results indicated that the overall quality of the AR media kit with the metaverse was rated at a very high level (x ̅= 4.80, S.D. = 0.12). Evaluating specific aspects showed that the content quality was at the highest level (x ̅= 4.92, S.D. = 0.07), while the media production technique also rated highly (x ̅ = 4.70, S.D. = 0.17). Furthermore, the English pronunciation and communication skills of the Grade 4 students using the AR media with the metaverse were significantly higher after the intervention compared to before, the overall quality of the AR media integrated with the metaverse was rated at the highest level (x ̅= 4.80, S.D. = 0.12). For individual aspects, content quality was rated at the highest level (x ̅= 4.92, S.D. = 0.07), and media production techniques were also rated at the highest level (x ̅ = 4.70, S.D. = 0.17). Comparing the mean scores of English pronunciation and communication skills between the two groups, it was found that the experimental group using AR media integrated with the metaverse demonstrated significantly higher English pronunciation skills than the control group (F(1, 89) = 3261.422, p = 0.001, Partial η² = 0.98). Additionally, the experimental group exhibited significantly higher English communication skills than the control group (F(1, 89) = 4239.365, p = 0.001, Partial η² = 0.98). These results aligned with the research hypothesis that "Grade 4 students’ English pronunciation and communication skills post-learning with AR media integrated with the metaverse would significantly improve compared to their pre-learning levels at the .05 level of significance.
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.