Discover the ultimate innovations of the future developed by Thai researchers! Meet the latest technology from KMITL that will transform our way of life and industry.
คณะวิทยาศาสตร์
The current residential solar panels lack an adequate monitoring system, which hinders their optimal utilization. This research aims to design an Internet of Things (IoT) monitoring system and employ machine learning techniques to predict the current and voltage generated by solar panels. Experimental studies have revealed a correlation between dust accumulation and the current output of solar panels. The proposed system facilitates the prediction of the optimal time for cleaning solar panels.
คณะวิศวกรรมศาสตร์
An automated hydroponic system for household use has been developed to cater to individuals with limited space who wish to conveniently and easily grow their own salad greens at home. This system is designed to automatically control nutrient delivery by setting appropriate electrical conductivity (EC) and pH levels tailored to the specific salad greens being grown. It includes artificial lighting to enable cultivation in confined spaces with insufficient sunlight and is more cost-effective than similar systems available on the market. System testing revealed that the automated control of EC and pH values performed effectively, achieving the preset levels within 30 minutes and maintaining them consistently throughout operation. In an experiment growing green oak lettuce using a simulated balcony setup, the plants demonstrated a higher growth rate compared to conventional methods, particularly when artificial lighting was used.
คณะอุตสาหกรรมอาหาร
This research aims to optimize the production process of gamma-aminobutyric acid (GABA) in fermented pineapple juice using probiotics and acetic acid bacteria (AAB), which are microorganisms with the potential to enhance GABA levels. This process has been developed to improve the nutritional value of fermented pineapple juice and to increase the economic value of Thai pineapples, which have long suffered from low market prices. This study focuses on determining the optimal conditions for GABA production by examining factors such as sugar content, pH levels, fermentation duration, and L-glutamate concentration, as well as the co-cultivation of probiotics and acetic acid bacteria. The experiments are conducted using controlled fermentation techniques, and the bioactive components of the fermented juice are analyzed with advanced instruments such as HPLC and GC-MS. The findings of this research are expected to contribute to the development of formulations and production processes for a high-GABA pineapple-based functional beverage. This product could offer health benefits such as stress reduction, cognitive function enhancement, and relaxation while also strengthening the potential of Thailand’s fermented food and beverage industry.
คณะเทคโนโลยีการเกษตร
Dwarf whipray (Brevitrygon heterura) is a common species found in a local market in the Gulf of Thailand. However, like many other species of stingrays, it is threatened by overfishing and habitat destruction. Therefore, an accurate species identification is crucial because conservation efforts may vary depending on the species. This study aims to understand morphological variation of B. heterura in the Gulf of Thailand by morphometric study and genetic analysis. During October 2022 and February 2023, we obtained 49 samples from research vessels fish landing ports and local fish markets. We observed two distinct groups based on 43 morphological variables/ratios. B. heterura samples from Chanthaburi, Rayong, Chonburi, Samut Sakhon, Nakhon Si Thammarat and Songkla provinces, called “group A," typically have longer snout length than those from Prachuap Khiri Khan provinces, called “group B" according to external morphological characters for species identification. Three morphological variables/ratios were significantly different between groups A and B. Main characters to explain intraspecific variations between group A and group B are further discussed. DNA barcoding based on a fragment of the cytochrome c oxidase subunit I (COI) gene were obtain from eight samples of group A and eight samples from group B. Pairwise percent sequence divergence (p-distance) for COI between group A and group B were 0.0-2.5. This study contributes to the understanding of variations of morphology and genetics of B. heterura in the Gulf of Thailand.
คณะวิทยาศาสตร์
Sugar production from sugarcane is a complex process that requires precise control. One of the major issues is sugar loss, which can result from various factors, particularly "burnt cane," before being sent to the mill. This affects the quality of the sugarcane and the efficiency of sugar extraction, along with the performance of the machinery and the properties of the cane, which impact the amount of sugar extracted. This study aims to analyze the factors that influence sugar loss in the sugar production process, using quantitative data from a sugar factory. Nine variables were examined, including mechanical efficiency, machine downtime per day, cane waiting time per day, sand content in cane juice, pol extraction efficiency, overall working time efficiency, cane juice purity, cane sugar content (C.C.S.), and burnt cane. The data were analyzed using correlation analysis to examine relationships between variables and regression modeling to predict sugar loss. The results showed that mechanical efficiency, cane sugar content, and the amount of sand or impurities in the cane juice were significantly correlated with sugar loss. Mechanical efficiency had a direct relationship with the amount of cane milled, which improved sugar production. On the other hand, burnt cane, or cane that was burnt before harvesting, resulted in reduced sugar extraction and impacted the quality of the sugar. Therefore, reducing sugar loss in the production process can be achieved by improving machine efficiency, reducing impurities in cane juice, and managing burnt cane, which will improve sugar production efficiency in the future.
คณะวิทยาศาสตร์
Recruitment is a crucial process that enables organizations to select candidates whose qualifications match the requirements of a given position. However, this process often faces challenges related to data management, delays, and human bias. This research aims to design and develop an intelligent web application for employee recruitment using artificial intelligence (AI) technology to evaluate and score candidates' suitability for job positions. The system leverages data analysis techniques on resumes and a qualification-matching process based on predefined criteria. Developed using Agile principles, the system employs Natural Language Processing (NLP) to analyze resumes, assess candidates’ qualifications, skills, and experience, and utilizes Machine Learning to predict and rank suitability. The system consolidates data from multiple sources into a unified database to reduce redundancy and input errors. Additionally, it presents insights through a dashboard, enabling HR teams to make more effective hiring decisions.
คณะวิทยาศาสตร์
In this paper, Vanadium dioxide (VO2) thin-film devices with two different use cases have been redesigned to introduce an asymmetrical resonant cavity structure. The structure is designed with the goal of enhancing the optical performance of the central VO2 layer and has an anti-reflection property in the cold state. The advantages and limitations of such a design are discussed.
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.
คณะบริหารธุรกิจ
This project is a part of KMITL business student’s thesis. The topic is business plan about blazers and trousers made by recycled fabric