The forest firefighting suit consists of the following components and uses: The forest firefighting suit is designed and developed to be suitable for the behavior of the officers and the conditions of the work area, consisting of a shirt and pants. The material used in the sewing of the suit is aramid fabric, which has the property of being able to prevent the spread of fire, to prevent the officers from burning while performing their duties in the event that the forest fire spreads close to them, which is different from the current suits that cannot prevent fires. The shirt is designed with a mesh on the side of the body to release internal heat so that air can circulate well. The sleeves at the elbows have a support point to prevent contact with the ground or obstacles. The collar has a slot for a portable fan and a fan air circulation channel on the back, which can be turned on while performing forest firefighting duties, helping to prevent the body temperature from getting too hot, reducing the risk of heatstroke. When the fan battery runs out, it can be removed for charging and put back in when needed. The pants are designed with mesh on the inside or in blind spots to release internal heat so that air can circulate well. The pants at the knees have a support point to prevent contact with the ground or obstacles. The forest firefighting suit, consisting of a shirt and pants, has been designed and developed to be able to be produced domestically, reducing imports from abroad
ปัญหาไฟป่าที่ทวีความรุนแรงขึ้นเรื่อย ๆ ส่งผลกระทบในหลายๆด้านโดยเฉพาะผู้ที่พักอาศัยบริเวณใกล้ไฟป่าต้องเผชิญกับปัญหาด้านสุขภาพที่เกิดจากฝุ่น PM2.5 ประกอบกับทางภาครัฐยังขาดแคลนบุคลากรเจ้าหน้าที่ ที่มีความชำนาญในการดับไฟป่า รวมถึงยังขาดชุดดับไฟป่าที่เป็นชุดสามารถกันไฟได้เหมาะสมต่อการดับไฟ เนื่องจากในปัจจุบันชุดที่เจ้าหน้าที่อุทยาน และเจ้าหน้าที่ศูนย์ดับไฟป่า สวมใส่ในการปฏิบัติหน้าที่นั้นเป็นชุดลายพลาง กางเกงกับเสื้อแบบทั่วไป ไม่สามารถปกป้อง หรือกันไฟเมื่อไฟมาถึงระยะประชิดตัวได้ในระดับที่เหมาะสม ซึ่งสาเหตุนี้ส่งผลให้เจ้าหน้าที่บางรายได้รับอันตรายถึงขั้นไฟคลอกได้ ผู้วิจัยจึงได้เล็งเห็นถึงความสำคัญในการออกแบบและพัฒนาชุดดับไฟป่า เพื่อให้เจ้าหน้าที่อุทยาน และเจ้าหน้าที่ศูนย์ดับไฟป่า สามารถสวมใส่ปฏิบัติหน้าที่ได้อย่างปลอดภัย ช่วยลดอุปสรรคในการดับไฟป่าและปฏิบัติหน้าที่ได้อย่างเต็มประสิทธิภาพมากยิ่งขึ้น
คณะศิลปศาสตร์
Layla, the hotel robot, is responsible for carrying guests’ luggage and guiding them to their accommodations. It is equipped with an internal map of the hotel, allowing it to navigate various locations efficiently. Additionally, it features an AI-powered system that enables interactive conversations in three major languages: Thai, English, and Chinese.
คณะวิศวกรรมศาสตร์
The integration of intelligent robotic systems into human-centric environments, such as laboratories, hospitals, and educational institutions, has become increasingly important due to the growing demand for accessible and context-aware assistants. However, current solutions often lack scalability—for instance, relying on specialized personnel to repeatedly answer the same questions as administrators for specific departments—and adaptability to dynamic environments that require real-time situational responses. This study introduces a novel framework for an interactive robotic assistant (Beckerle et al. , 2017) designed to assist during laboratory tours and mitigate the challenges posed by limited human resources in providing comprehensive information to visitors. The proposed system operates through multiple modes, including standby mode and recognition mode, to ensure seamless interaction and adaptability in various contexts. In standby mode, the robot signals readiness with a smiling face animation while patrolling predefined paths or conserving energy when stationary. Advanced obstacle detection ensures safe navigation in dynamic environments. Recognition mode activates through gestures or wake words, using advanced computer vision and real-time speech recognition to identify users. Facial recognition further classifies individuals as known or unknown, providing personalized greetings or context-specific guidance to enhance user engagement. The proposed robot and its 3D design are shown in Figure 1. In interactive mode, the system integrates advanced technologies, including advanced speech recognition (ASR Whisper), natural language processing (NLP), and a large language model Ollama 3.2 (LLM Predictor, 2025), to provide a user-friendly, context-aware, and adaptable experience. Motivated by the need to engage students and promote interest in the RAI department, which receives over 1,000 visitors annually, it addresses accessibility gaps where human staff may be unavailable. With wake word detection, face and gesture recognition, and LiDAR-based obstacle detection, the robot ensures seamless communication in English, alongside safe and efficient navigation. The Retrieval-Augmented Generation (RAG) human interaction system communicates with the mobile robot, built on ROS1 Noetic, using the MQTT protocol over Ethernet. It publishes navigation goals to the move_base module in ROS, which autonomously handles navigation and obstacle avoidance. A diagram is explained in Figure 2. The framework includes a robust back-end architecture utilizing a combination of MongoDB for information storage and retrieval and a RAG mechanism (Thüs et al., 2024) to process program curriculum information in the form of PDFs. This ensures that the robot provides accurate and contextually relevant answers to user queries. Furthermore, the inclusion of smiling face animations and text-to-speech (TTS BotNoi) enhanced user engagement metrics were derived through a combination of observational studies and surveys, which highlighted significant improvements in user satisfaction and accessibility. This paper also discusses capability to operate in dynamic environments and human-centric spaces. For example, handling interruptions while navigating during a mission. The modular design allows for easy integration of additional features, such as gesture recognition and hardware upgrades, ensuring long-term scalability. However, limitations such as the need for high initial setup costs and dependency on specific hardware configurations are acknowledged. Future work will focus on enhancing the system’s adaptability to diverse languages, expanding its use cases, and exploring collaborative interactions between multiple robots. In conclusion, the proposed interactive robotic assistant represents a significant step forward in bridging the gap between human needs and technological advancements. By combining cutting-edge AI technologies with practical hardware solutions, this work offers a scalable, efficient, and user-friendly system that enhances accessibility and user engagement in human-centric spaces.
คณะวิทยาศาสตร์
This special problem aims to compare the performance of machine learning methods in time series forecasting using lagged time periods as independent variables. The lagged periods are categorized into three groups: lagged by 10 units, lagged by 15 units, and lagged by 20 units. The study employs four machine learning methods: Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The time series data simulated as independent variables diverse including characteristics: Random Walk data, Trending data, and Non-Linear data, with sample sizes of 100, 300, 500, and 700. The research methodology involves splitting the data into 90% for training and 10% for testing. Simulations and analysis are performed using the R programming language, with 1,000 iterations conducted. The results are evaluated based on the average mean squared error (AMSE) and the average mean absolute percentage error (AMAPE) are calculated to identify the best performing method. The research findings revealed that for Random Walk data, the best performing methods are Random Forest and Support Vector Machine. For Trend data, the best performing methods are Random Forest. For Non-Linear data, the best performing methods are Support Vector Machine. When tested with real-world data, the results show that for the Euro-to-Thai Baht exchange rate, the best methods are Random Forest and Support Vector Machine. For the S&P 500 Index in USD, the best performing methods are Random Forest. For the Bank of America Corp Index in USD, the best performing methods are Support Vector Machine.