Tangtisanon, Pikulkaew
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Preferred name
Tangtisanon, Pikulkaew
Alternative Name
Tangtisanon, P.
Main Affiliation
Email
pikulkaew.ta@kmitl.ac.th
3 results
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Item type:Publication, A Generational Cohort Comparison of Icon Selection Accuracy under Varying Conditions of Icon Entropy and Concreteness(2023-01-01); —The objective of this research was to compare icon selection accuracy under varying icon entropy and concreteness conditions between different generational cohorts (Millennial, Generation X, and Baby Boomers). These generational cohorts have different levels of experience with technology, with younger generations often being framed as “digital natives” and holding stronger technological experience and competence in comparison to older groups. Generational groups also have variations in physiological factors including visual acuity and reaction time. Despite these differences between user groups, many user interaction systems and processes are designed for a single user, rather than considering differences in user processing between different groups. Therefore, this study compares generational cohorts in their icon selection accuracy under varying icon conditions, to help identify what generational differences can be observed in this task. The study selected a sample of 150 participants (n = 50 for each generational cohort). The experiment was a 2☓2☓3 design (entropy (high/low) ☓ abstractness (abstract/concrete) ☓ time (9/6/3 seconds), with each participant completing 60 trials (five questions per entropy/abstractness pair over three timed runs). Results showed that there were significant differences in mean accuracy per trial under all of the time conditions and icon entropy and concreteness conditions. Mean differences showed that under most conditions, Millennial and Generation X participants did not have a significant mean difference, but Baby Boomers were significantly slower under almost all conditions. The implication of this finding is that Baby Boomers are more sensitive to icon abstractness and entropy conditions than other age groups tested. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Portable Electronic Nose for Real-time Monitoring of Food Spoilage Using Multiple Machine Learning Models(2025-01-01); Grodniyomchai, BoonyaweeIn this study, we present the design and development of a portable electronic nose (E-nose) system for detecting and classifying spoiled household food through the application of machine learning (ML) techniques. The targeted odors include fungi from bread, spoiled rice, spoiled milk, yoghurt, rotten egg, rotten boiled egg, rotten pork, and rotten beef, totaling eight odor classes. A total of 1800 samples were collected using three gas sensors and one temperature sensor. After outlier removal with Isolation Forest, 1000 samples remained. Multiple ML models were trained and evaluated over ten iterations, comparing classification accuracy and processing time. Among all the models, the k-nearest neighbor (KNN) achieved the highest performance, with an average accuracy of 99.889% and an average processing time of 0.167477 s. The decision tree (DT) model followed closely with an accuracy of 99.843% and required a significantly less processing time of 0.012080 s. Although DT has a slightly lower accuracy than KNN, its processing time is 13.86 times faster. For our scenario that requires real-time results, DT is a better choice than KNN. The proposed portable E-nose demonstrates strong potential for realworld applications such as food spoilage detection, environmental monitoring, and health diagnostics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Small gardening robot with decision-making watering system(2019-01-01)At present, people who live in the countryside tend to move downtown in order to get jobs. As a consequence, only elders and children live in their hometowns with plenty of land left uncultivated. The aim of this research is to build cheap small gardening robots to help people grow plants in small yards as a hobby via a long-distance communication system. In this research, two robots composed of Raspberry Pi and ESP32 microcontrollers, which are low-price controller boards and convenient to buy within the country, were constructed. The robots can be controlled from long distances using Android smartphones. The capacities of the two robots with the two types of microcontroller are compared and discussed. To measure the soil moisture content, two types of moisture sensor, which are capacitive and resistive sensors, were implemented in this project. There are two main functions of the proposed model, which are weed cutting and watering plants. Moreover, a decision-making watering system was implemented and connected to moisture sensors and sprinkler controllers placed in the user’s garden. The robots were placed in the northern region of Thailand, while the user stayed in the central region and remotely controlled them with a smartphone for three months. The results show that the automatic watering system is better than a manual watering system since the plant growth rate for the automatic watering system was 20% higher than that for the manual watering system.