Atchariyachanvanich, Kanokwan

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School of Information Technology

kanokwan.at@kmitl.ac.th

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Bridge Sub Structure Defect Inspection Assistance by using Deep Learning
(2019-10-01)
Kruachottikul, Pravee
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Cooharojananone, Nagul
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Phanomchoeng, Gridsada
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Chavarnakul, Thira
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Kovitanggoon, Kittikul
Road transportation is the most popular transportation in Thailand, which the top two highest traffic are the region-to-region highways; and then inter-city highways. Therefore, the regular maintenance is required to maintain the good condition due to road safety. The most significant process of bridge inspection procedures is sub structure inspection, which requires visual inspection as an initial step. This process is used to quick determine the damage severity i.e. appearance and crack that may cause damage to the structure strength. The current process requires that the experienced maintenance engineer to be on the field in order to visual inspect and estimate whether the maintenance is required. Yet, due to the limitation of number of expert engineers to be on the field, the photo verification is introduced to assist them so that they are no need on every inspection site. However, using human to verify has no standard and uncontrollable. They need to have experience and good knowledge. As well as it is highly depended on individual decision-making skill. Thus, in this paper, the deep learning technique will be presented to assist the expert for quality inspection process of bridge sub structure images. That is using image enhancement and then image splitting and overlapping for image pre-processing. After that applying CNNs for object classification. As a result, the total accuracy is 89% based on 3926 dataset.
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A Data Visualization for Helping Students Decide Which General Education Courses to Enroll: Case of Chulalongkorn University
(2019-01-01)
Cooharojananone, Nagul
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Dilokpabhapbhat, Jidapa
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Rimnong-ang, Thanaporn
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Choosuwan, Manutsaya
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Bunram, Pattamon
Chulalongkorn University has been utilizing information systems for course administration system, named CU-CAS, to help manage the course syllabus, course contents and course satisfaction survey. While current students have been selecting courses based on information from seniors and friends, we recognize that the data from CU-CAS could be useful in selecting course, but have not been fully utilized. Therefore, this project aims to design a data dashboard to help students select courses to register, based on the data from course satisfaction survey by students from the past three years of course offerings. In this work, we developed CU-CAS data visualization using Google Data Studio. Data were analyzed and presented the overall of the evaluation result in term of dashboard. According to our pilot study, students make decisions for enrollment by comparing the evaluation result in the past three years, in the form of different indicators. We also collected and analyzed data from the student blogs that review courses that they took using word cloud and Markov chain. Both data from CU-CAS and blogs will be represented to students to help students make decision in registering courses. This project is one of the e orts to utilize data in a way that is easy to understand to students, allow Chulalongkorn University to understand students learning behavior, and bring back to plan and adjust teaching strategies.
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What Makes university students use cloud-based e-learning?: Case study of KMITL students
(2015-01-13)
Atchariyachanvanich, Kanokwan
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Siripujaka, Nutchanon
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Jaiwong, Nattapong
Cloud computing technology has been influential in overcoming problems in e-learning systems, such as the lack of scalability and storage limitation. Therefore, a framework for applying cloud computing on e-learning systems has been prepared. However, to develop a cloud-based e-learning system that matches well with the learners' needs and solves the current problems, it is important to know the learners' requirements. This research evaluated the key significant factors required for university students to use Cloud-based e-learning based on a research model, including the theory of motivation, and characteristics of cloud computing. In total, 250 students from King Mongkut's Institute of Technology Ladkrabang were surveyed by questionnaire. Data analysis was performed by factor and multiple regression analyses. Overall the factors that influence the intention to use cloud-based e-learning were identified as the availability, collaboration, cloud-based e-learning notifications, intrinsic motivation and extrinsic motivation. However, these account for only 62.9% of the usage intention, and so other factor(s) still remain to be determined.
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RSQLG: The Reverse SQL Question Generation Algorithm
(2019-05-14)
Julavanich, Thanakrit
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Nalintippayawong, Srinual
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Atchariyachanvanich, Kanokwan
Nowadays, the people who learn and teach SQL commands require to get a hand-on practice with real environment to make the learning effective. The creating SQL exercise is a time-consuming task for instructors. As a result, students might not get enough questions to meet their demands. The reverse SQL question generation algorithm (RSQLG) is developed to solve this problem. The RSQLG has ability to automatically generate SQL exercise for students. The RSQLG can reverse the manual question creation process which starts from creating question to start creating query answer first instead. The RSQLG considers the existing data and database structure by using various constraints. The instructors also can specify the language, format and explanation of the questions. The RSQLG supports DML commands-SELECT, INSERT, UPDATE and DELETE-and support data retrieval from multiple tables which perform by JOIN and subquery operations. The algorithm has ability to generate bulk questions with less effort. The instructor is not required to writing requirements and validating the queries. The RSQLG can be implemented in e-learning to enhance sustainable practices and improve learning outcome for students.
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BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV
(2024-01-01)
Kujareanpaisal, Poonnaphop
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Mayhasap, Rujira
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Tea-Makorn, Pin Pin
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Jindahra, Pavitra
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Starita, Stefano
The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions.
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Predicting Fuel Burn with Neural Network to Adjust Contingency Fuel of Airplane
(2023-01-01)
Ounsrimoung, Pimolrat
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Nootyaskool, Supakit
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Atchariyachanvanich, Kanokwan
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Yooyen, Soemsak
The amount of fuel in an airplane tank is very important for flying. however, flying a short distance by adding a fuel-full tank is not energy efficient because spending a lot of tons for holding fuel weight. The flight planners who consider the amount of fuel to add to the tank by using historical data, use fuel burn calculating and adjust contingency fuel. This research presents the neural networks to predict fuel burn, which learn from historical airplane data. The experiment applied to local and international flight data and used both Airbus and Boeing. The predicted model was swapped and tested on the outbound and inbound replacements for confirmation capable of the predicted mode.
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A statistical model for estimating statistical contingency fuel
(2022-01-01)
Atchariyachanvanich, Kanokwan
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Kruaklai, Warune
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Chaipatchareekorn, Nattanan
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Sukteab, Nuttavadee
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Yooyen, Soemsak
Contingency fuel is the amount of fuel used to compensate for unexpected events. This amount of fuel is equal to 5% of the trip fuel or 3% of the trip fuel when it has been determined to have an alternate airport on the route according to the rules of the Thai Civil Aviation Authority. Currently, contingency fuel planning determines the minimum and maximum values of contingency fuel based on aviation industry experience. As a result, the fuel supply may be either too much or too little on some flights. In this research, we aim to create a statistical model that can estimate the fuel required in the event of an emergency and measure the efficiency of contingency fuel with a loss function. The model uses statistical methods to calculate the contingency fuel in the form of Statistical Contingency Fuel (SCF) and monitors the fuel deviation for the planned and actual trip. We used fuel preparation data from 2018 and 2019 that was sourced from Thai Airlines data for six routes with a total of 4,184 flights. The results show that the SCF of flight A was at confidence of level 95, while that of other flights was at a confidence level of 99. The results obtained from the model can be used to assist flight planners to make better decisions concerning the determination of contingency fuel.
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Foot Traffic Analysis Using Wi-Fi Sensor During the Tokyo 2020 Olympics and Paralympics
(2023-01-01)
Traganmaturapot, Peerada
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Sonehara, Noboru
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Hiruma, Nobuharu
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Cooharojananone, Nagul
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Jirapongwanich, Jirakit
Various real-world factors, such as time, weather, distance, environment, the COVID-19 pandemic, or even protests, can all impact human decision-making. However, restrictions and unexpected occurrences may also influence people's decisions regarding their path at any given time. These factors can lead to challenges in managing foot traffic at largescale events. In response to these challenges, this paper proposes a data-driven web-based foot traffic management supporting dashboard for large-scale events based on limited pedestrian count data, consisting of sensor name, latitude, longitude, MAC address, Datetime, and RSSI, collected by Wi-Fi sensors around the Sendagaya area during the Tokyo 2020 Olympics and Paralympics. The results confirmed that our proposed web-based dashboard contributes to human behavior understanding and decision-supporting policymaking for foot traffic management, which improves the design of spectator movement between transportation and venues in large-scale events. Furthermore, the dashboard is valuable from various perspectives, including preventing crowd crushing, redesigning areas to increase engagement in the shopping district, and improving traffic management.
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DBLearn: Adaptive e-learning for practical database course - An integrated architecture approach
(2017-08-29)
Nalintippayawong, Srinual
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Atchariyachanvanich, Kanokwan
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Julavanich, Thanakrit
In this paper, an integrated architecture approach in designing and developing a DBLearn web-based application is presented. The DBLearn system is a personalized and adaptive e-learning system designed especially for learning practices in database courses. This approach focused on topics that are important but difficult for new learners, such as database design and structured query language (SQL) command query. The concept of adaptive e-learning and autonomous agents were applied in this system to eliminate the traditional constraints of effective e-learning, such as the problem of different learning sensory and knowledge levels. Four approaches were used to solve this problem. First, learning style theory was used to classify the way of learning for each student. Second, the student activity (historical data) is kept in the system to analyze the next knowledge the student should learn or review. Next, the SQL query automated grader was used to judge the correctness of the student's query. This grader supports all the necessary commands in both DML and DDL. Finally, the SQL query question generator module that can generate SQL query questions automatically is presented. This will reduce the instructor's work load in creating enough questions and allow the students to practice at their own pace as much as they want. By using these four techniques, the students will have a better learning experience and becoming more successful in learning outcomes.
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ScrambleSQL: A Novel Drag-and-drop SQL Learning Tool
(2019-10-01)
Phewkum, Chanapat
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Kaewchaiya, Jittakan
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Kobayashi, Kazuki
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Atchariyachanvanich, Kanokwan
Structured Query Language (SQL) is a standard language for forming queries to access relational database systems, e.g. create table, as well as read, update, and delete data from the database. Thus, it is commonly taught in computer science classes. Technology growth helps developers to create many SQL tools which encourage students to learn effectively. Although many SQL learning tools have been developed, they are not suitable for some users who prefer to use a tablet for learning SQL, because the existing tools were not developed for a touchscreen device. Thus, we developed prototype ScrambleSQL, optimized for tablets. The purpose was to enhance learning, speed up SQL command writing and allow users to practice SQL commands anywhere and anytime, with the internet. We tested undergraduate students from an Information Technology program. They wrote SQL commands by ScrambleSQL and then completed a questionnaire. The participants were satisfied with its screen, learning and system capabilities. ScrambleSQL reduced typing errors and helped novice users to learn SQL commands with the provided keywords. In addition, the participants enjoyed learning with ScrambleSQL.

Atchariyachanvanich, Kanokwan

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