This project presents the development of a "Smart Cat House" using Internet of Things (IoT) and image processing technology to facilitate and enhance the safety of cat care for owners. The infrastructure of the smart cat house consists of an ESP8266 board connected to an ESP32 CAM camera for cat monitoring, and an Arduino board that controls various sensors such as a motion sensor in the litter box, a DHT22 temperature and humidity sensor, an ultrasonic water and food level sensor, including a water supply system for cats, an automatic feeding system, and a ventilation system controlled by a DC FAN that adjusts its operation according to the measured temperature to maintain a suitable environment. There is also an IR sensor to detect the cat's entry into the litter box and an automatic sand changing system with a SERVO MOTOR. All systems are connected and controlled through the Blynk application, which can be used on mobile phones, allowing owners to monitor and care for their pets remotely. Cat detection and identification uses image processing technology from the ESP32 CAM camera in conjunction with YOLO (You Only Look Once), a high-performance object detection algorithm, to detect and distinguish between cats and people. Data from various sensors are sent to the Arduino board to control the operation of various devices in the smart cat house, such as turning lights on and off, automatically changing sand, adjusting temperature and humidity, feeding food and water at scheduled times, or ventilation. The use of a connection system via ESP8266 and the Blynk application makes it easy and convenient to control various devices. Owners can monitor and control the operation of the entire system from anywhere with internet access.
ในปัจจุบัน ผู้คนจำนวนมากเลือกเลี้ยงแมวเป็นเพื่อนคลายเหงา แต่ด้วยภาระหน้าที่การงาน การเรียน หรือธุระส่วนตัว ทำให้หลายครั้งเจ้าของไม่สามารถดูแลแมวได้อย่างใกล้ชิดตลอดเวลา ก่อให้เกิดความกังวลใจและเป็นห่วงสัตว์เลี้ยงที่บ้าน ปัญหาเหล่านี้เป็นแรงบันดาลใจให้เกิดแนวคิดในการพัฒนาบ้านแมวอัจฉริยะ (Smart Cat House) เพื่ออำนวยความสะดวกและตอบโจทย์ความต้องการของผู้เลี้ยงแมวในยุคปัจจุบัน บ้านแมวอัจฉริยะเป็นระบบที่ถูกออกแบบมาเพื่อช่วยให้เจ้าของสามารถติดตามและดูแลแมวได้จากระยะไกลผ่านแอปพลิเคชันบนมือถือ โดยภายในบ้านแมวอัจฉริยะจะมีระบบต่างๆ ที่ช่วยอำนวยความสะดวกสบายให้กับทั้งเจ้าของและแมว อาทิ ระบบควบคุมการให้อาหารและน้ำ ระบบจัดการกระบะทราย ระบบควบคุมอุณหภูมิ และระบบกล้องวงจรปิด เป็นต้น ทำให้บ้านแมวอัจฉริยะเป็นทางออกที่ตอบโจทย์สำหรับผู้เลี้ยงแมวที่ไม่ค่อยมีเวลา แต่ยังคงต้องการดูแลสัตว์เลี้ยงของตนให้มีความสุขและมีสุขภาพแข็งแรง
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.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This conceptual model, titled "DeHome", incorporates the principles of Deconstructivism in architectural design. It deconstructs the fundamental elements of a house—roof, columns, doors, windows, and bricks—separating them and reassembling them in a way that conveys fragmentation, contradiction, and movement. This design challenges the traditional concept of structural stability by enlarging key elements such as doors, windows, and columns, emphasizing distortion and the dynamic force of transformation. Beyond merely dismantling the physical structure of a house, this project reinterprets the very concept of "home" within the context of contemporary architecture.
คณะวิทยาศาสตร์
The species Enterococcus lactis is closely related to E. faecium and is known for its beneficial and probiotic effects. In this study, strain RRS4 was isolated from Raphanus sativus Linn. and identified based on both phenotypic and genotypic characteristics. Strain RRS4 exhibited cell viability in environments with 2-8% NaCl, pH ranging from 4 to 9, and temperatures between 4°C and 45°C. Through comprehensive genomic analysis, strain RRS4 was confirmed to be E. lactis. E. lactis RRS4 demonstrated inhibitory effects against Vancomycin-resistant E. faecalis JCM 5803. Safety assessments via in silico methods, including KEGG annotation, indicated the absence of virulent and undesirable genes in E. lactis RRS4. VirulenceFinder analysis aligned virulence-related genes with those from three strains of E. lactis and four strains of E. faecium. While antibiotic resistance genes were found to be conserved, they did not correlate with key pathogenicity traits. Furthermore, safety evaluations highlighted that E. lactis RRS4 is generally safe, despite the presence of genes associated with antibiotic resistance. Lastly, we propose guidelines for assessing the safety of microbial strains using whole-genome analysis. These findings represent advancements in probiotic research.