Chawuthai, Rathachai

Preferred name

Alternative Name

Chawuthai, R.

Main Affiliation

School of Engineering

rathachai.ch@kmitl.ac.th

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Assessing the Effects of Corrupted Parameters in a Large Language Model: A Case Study of LLAMA 3.2 1B
(2026-01-21)
Chawuthai, Rathachai
;
Thongsawaeng, Anon
;
Perdio, John Paul Layug
;
Zaw, Kaung Khant
;
Kraichoke, Phalat
This study explores the effects of parameter corruption in a large language model (LLM) by altering its weights and evaluating performance. Experiments involve corrupting different layers and matrix types, including Self-Attention and Feed-Forward components, with performance assessed using BERT and ROUGE scores. Testing the Llama-3.2-1B-Instruct model was performed on the GLUE-QNLI dataset. Results show that increased corruption leads to greater degradation, with Feed-Forward matrices having a stronger impact especially in the Down matrices. According to the study, later layers are more important for performance than those that come before them. These results shed light on possible future chip implementations of LLM, which may help guide the design of fault-tolerant systems by taking vulnerable parameter placement into account.
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Defect detection of GPS trackers through data visualization
(2019-07-01)
Chawuthai, Rathachai
;
Threepak, Thanunchai
A Global Positioning System (GPS) tracker installed in a vehicle is commonly used to improve logistics management processes and transportation safety. All GPS trackers must send data including locations, timestamps, and speeds to a server all the time. In case of a device failure, it can be checked by incomplete data; however, a device's sensor inaccuracy, which can create negative consequences to many parties, becomes a challenging issue to detect. With this reason, this paper aims to adopt data visualization to find out the defect of GPS trackers. It has been found that some defects noticed by a visualization were reported, and providers got advantage of this result to maintain their devices.
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Modelling an RDF Knowledge Graph with Transitivity and Symmetry for Bus Route Path Finding
(2023-12-16)
Chawuthai, Rathachai
;
Kertkeidkachorn, Natthawut
;
Racharak, Teeradaj
A key property of Linked Data is the representation and publication of data as an inter-connected labelled graph where different resources linked to each other form a network of meaningful information. A problem of path finding can be seen as searching important relationships between resources, such as, looking for chains of intermediate nodes. In this paper, we tackle this problem in the context of public transport navigation system, where we aim to find candidates of bus route path given two bus stations. We model a novel lightweight bus network as Resource Description Framework (RDF) triples of directed bus lines and walking paths between connected stations. Indeed, we demonstrate that lightweight bus network can be achieved by exploiting the sub-property of RDF Schema (RDFS) and the transitivity and symmetry provided by Web Ontology Language (OWL). We also perform a scalability test of our approach using a real-world bus network in Bangkok, Thailand. Various patterns of SPARQL Protocol and RDF Query Language (SPARQL) query statements are validated, showing the usefulness of the RDF model. The further step of this paper is to work with bus schedules and travel time analysis in order to select some proper candidates for users through an application.
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Assessing HDBSCAN Implementation for Traffic Congestion Pattern Estimation in Bangkok with Taxi GPS Probe
(2023-01-01)
Tony, Dio
;
Chawuthai, Rathachai
Traffic congestion is a major issue that is experienced globally in metropolitan cities. The phenomenon becomes more serious during peak hour as congestion increases and degrades the traffic networks. Each city possesses a unique traffic network, and the behaviour of its residents affects its traffic patterns. Therefore, a flexible congestion pattern identification approach is desirable. We proposed the employment of Hierarchical Density Based Spatial Clustering of Applications with Noise (HDBSCAN) to estimate traffic congestion propagation patterns through congestion length distribution. Global positioning System (GPS) probe of taxis were utilised to represent traffic pattern within Bangkok. The dataset was preprocessed into two successive timeframes, namely 'later' timeframe and 'prior' timeframe before being clustered. The identified congestion hotspots from both timeframes were transformed into a congestion area from which congestion lengths were extracted. Similarity measurements on congestion lengths distribution were conducted against Longdo Traffic's top 100 most congested roads list in Bangkok, showed encouraging results across all tests, with more than 90% similarity in one of the measurements, which indicated that HDBSCAN was feasible to make a key contribution to traffic management research.
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A Classification Model for Road Traffic Incidents on Twitter Data
(2022-01-01)
Raksachat, Thawatchai
;
Chawuthai, Rathachai
This study aims to create a classification model for road traffic incidents in Thailand using Twitter data. The challenging issue of our work is to deal with highly imbalanced dataset of 5 classes. As we surveyed, some pieces of research solved this issue by the Markov Chains method. However, using the Markov Chains in our dataset provides low performance, so we study the Undersampling, Oversampling, Markov Chains, and Bi-directional Long Short-Term Memory (Bi-LSTM). As we use the Markov Chains as the baseline, the result of our experiment found that using Bi-LSTM provides the improvement of F1-score up to 15.44% against the baseline.
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Novel method for properties prediction of pure organic compounds using machine learning
(2021-01-01)
Chorbngam, Nattasinee
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Chawuthai, Rathachai
;
Anantpinijwatna, Amata
In classical thermodynamic, the estimation method of pure compounds properties was based on Newtonian physics, which required experimental data. It is proven to be inadequate for the growing demand of the novel chemical synthesis. There were several studies on the prediction of the pure compound properties based on QSPR methods. However, the conventional group-contribution based methods predictive capability was limited by the available measured data. Therefore, this study aims to approach the property prediction with a novel statistical-based method. The proposed method is derived using supervised machine learning algorithms. The experimental data used to train and validate the models were collected from the published literature. These data set are composed of the alkanes, alkenes, and alkynes derivatives containing 1-12 carbon atoms. The results show the improved accuracy of the model prediction compare to the conventional method in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).
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A Recommender System for Trip Planners
(2020-01-01)
Chawuthai, Rathachai
;
Omarak, Prodpran
;
Thaiyingsombat, Vitchaya
Making a trip plan is a key activity for having a satisfying trip for tourists. However, as we survey, there are many users do not need to spend a lot of time to write the plan, and it becomes a pain point for users. The users prefer to have a simple way to create a whole trip plan from a few users’ constraints, and the users just customize some items for their satisfaction. Thus, this work introduces a recommender system that mainly employs the genetic algorithm for generating a trip plan. The approach accepts a few roughly input requirements from users, and then it creates a whole trip schedule and allows users to modify. To have a quality trip plan, any places and times in the plan have to correspond to places’ categories, open weekdays, times to spend, favorite daytimes and months, and possible routes. A web application for trip planner is developed to demonstrate the suitability and feasibility of the proposed recommender system. After that, the user feedback and usage statistic present the high degree of user satisfaction and opportunity to improve tourism of any city.
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Monitoring roadway lights and pavement defects for nighttime street safety assessment by sensor data analysis and visualization
(2018-01-01)
Chawuthai, Rathachai
Street maintenance and improvement are significant missions in ensuring transportation safety, especially at nighttime because the severity of injuries doubles at night. Driving visibility and road surface conditions are key factors behind nighttime traffic accidents, and they must be solved as a major priority. Having an exclusive report representing this issue becomes useful documentation for preparing an effective plan for repairing and upgrading a street at appropriate locations. However, road observations are mostly performed by humans, so reports are imprecise owing to the limitation of human cognition and documentation during observation at night. For this reason, the aim of this work is to create a visualization report for monitoring the risk on a street at nighttime. To achieve this goal, a light sensor for measuring brightness on the road, a gyro sensor and an accelerometer for detecting pavement defects, and a location sensor for marking the current latitude and longitude are placed in a car, and the data obtained are transferred to a cloud database while driving on the road. After that, all data are analyzed by machine learning techniques to identify some critical failures and report on map visualization. The result demonstrates that this approach can visualize the right defect at the correct location, and it will become an important contribution to transport safety.
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Integration of Genetic Algorithm with Machine Learning for Properties Prediction
(2025-01-01)
Chawuthai, Rathachai
;
Murathathunyaluk, Siripan
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Amornratthamrong, Nalin
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Arunchaipong, Run
;
Anantpinijwatna, Amata
Numerous studies have demonstrated that machine learning (ML) provides more accurate estimations of properties for oxygenated organic derivatives compared to the conventional Quantitative Structure-Property Relationship (QSPR) method. Consequently, ML’s predictive capabilities have been extended to encompass a broader range of properties, including Partition Coefficient, Boiling Point, and Solubility, among others, for oxygenated hydrocarbon derivatives. Algorithms such as Linear Regression, Support Vector Machine, Random Forest, and Gaussian Process are selected through trial-and-error to identify the most suitable approach. The models are trained and validated using experimental data from published literature. Despite the accuracy of these property predictions, they have limited practical utility in industry, where specific property ranges are essential for processes. To address this, Genetic Algorithms (GA) are employed to design chemical compounds that meet industrial requirements. Integrating GA with ML could yield alternative chemical compounds, enhancing overall production processes by increasing economic potential, sustainability, and reducing environmental impact.
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Linked open data model for taxonomic information
(2018-07-02)
Chawuthai, Rathachai
;
Jinbo, Utsugi
;
Takeda, Hideaki

Chawuthai, Rathachai

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