Chawuthai, Rathachai

Preferred name

Alternative Name

Chawuthai, R.

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School of Engineering

rathachai.ch@kmitl.ac.th

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A machine learning approach for predicting osmotic coefficients and deriving activity coefficients in alkyl ammonium salts
(2026-12-01)
Chawuthai, R.
;
Murathathunyaluk, S.
;
Saengsuradech, S.
;
Nukaew, A.
;
Simasatitkul, L.
Quaternary Ammonium Salts (Quats) have diverse applications across various domains. They are extensively used as phase-transfer catalysts (PTCs) in chemical reactions, facilitating the transfer of reactants between aqueous and organic phases. Their unique structure enables the formation of ion pairs, enhancing reaction rates at phase boundaries. This research develops a novel method for predicting Quats’ osmotic coefficients using Simplified Molecular Input Line Entry System (SMILES) notation and supervised machine learning. A comprehensive dataset of 1,654 data points from 52 distinct Quats was compiled. The structural characteristics were encoded using SMILES notation. The data was evaluated using random splitting and Leave-One-Group-Out (LOGO) validation to train seven machine learning algorithms. Gaussian Process (GP) emerged as the optimal algorithm. The GP model achieved a mean absolute percentage error (MAPE) of 5.29% and root mean square error (RMSE) of 0.034. Comparisons with Electrolyte-NRTL and Extended UNIQUAC models demonstrate that this data-driven approach offers competitive accuracy while enabling generalization to structurally similar compounds. This work marks a significant starting point for the machine learning-enhanced prediction of activity coefficients, with considerable potential for future refinement and application.
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GPS Data Analytics for the Assessment of Public City Bus Transportation Service Quality in Bangkok
(2023-04-01)
Chawuthai, Rathachai
;
Sumalee, Agachai
;
Threepak, Thanunchai
Evaluation of the quality of service (QoS) of public city buses is generally performed using surveys that assess attributes such as accessibility, availability, comfort, convenience, reliabilities, safety, security, etc. Each survey attribute is assessed from the subjective viewpoint of the service users. This is reliable and straightforward because the consumer is the one who accesses the bus service. However, in addition to summarizing personal feedback from humans, using data analytics has become another useful method for assessing the QoS of bus transportation. This work aims to use global positioning system (GPS) data to measure the reliability, accessibility, and availability of bus transportation services. There are three QoS scoring functions for tracking complete trips, on-path driving, and on-schedule operation. In the analytical process, GPS coordinates rounding is adopted and applied for detecting trips on each route path. After assessing the three QoS scores, it has been found that most bus routes have good operations with high scores, while some bus routes show room for improvement. Future work could use our data to create recommendations for policy makers in terms of how to improve a city’s smart mobility.
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Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning
(2025-12-01)
Chawuthai, Rathachai
;
Promchan, Teeratat
;
Rojsanga, Jularak
;
Chandra-ambhorn, Somrerk
;
Nilsonthi, Thanasak
Material degradation is one of the main problems in various high-temperature processes, directly resulting in the failure of the material. Crack and protective oxide film spallation caused either by mechanical stress development in the oxidation process or thermal stress due to a mismatch of the thermal expansions of the formed oxide and alloy are common forms of failure in high-temperature processes. Typically, the Pilling-Bedworth ratio (PBR) is employed to predict crack and spallation of the oxide by determining the volume changes of oxide and alloy because of its simplicity. However, this approach provides poor crack and spallation predictions. Hence, machine learning was adopted in the present work to predict oxide formation and spallation in the temperature range of 600-1,200 °C. The inputs for the present developed model were alloy compositions, oxide formed during oxidation, and oxidation conditions and periods. Furthermore, the predicted results of the present developed machine learning model were compared to those obtained by the PBR method. The present results revealed that the accuracy of the oxide spallation prediction of the present model was better than that of the PBR method. The random forest with 15 estimators was the best machine learning model. Finally, it can be concluded that the machine learning model is essential for accurate material failure prediction.
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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
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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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Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms
(2023-07-01)
Sharma, Nabin
;
Baral, Sushish
;
Paing, May Phu
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Chawuthai, Rathachai
The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively.
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Eye Landmarks Detection using RT-DETR with Rules
(2024-01-01)
Boonnithititikul, Chatree
;
Jaknamon, Teetouch
;
Chawuthai, Rathachai
In order to help ophthalmologists diagnose eye problems, it is necessary to scan for eye landmarks such as the pupil, the reflection point on the retina, and the boundary of the eye. An individual's eye landmarks on their face can be obtained via some facial landmarks' detection methods, including Haar Cascade. Two problematic aspects of the current approaches, however, are that the pupil and reflection point information is not provided, and the detection is ineffective when confronted with a picture of the upper half of the face or a person wearing a mask. In this study, we intend to develop a deep learning model for eye landmark identification using the Realtime identification Transformer (RT-DETR) approach together with our rules. As a consequence, nine landmark points-two for the eye, six for the pupil, and one for the reflection, are computed with an accuracy of 0.974. Since the focus of this paper is on eye landmark recognition, the next stage will be to build an application and a machine learning model for the diagnosis of eye disorders. - Keywords Deep Learning, Detection, Eye Landmarks, Facial Landmarks, Ophthalmology, RT-DETR
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Preface
(2021-01-01)
Hong, Tzung Pei
;
Wojtkiewicz, Krystian
;
Chawuthai, Rathachai
;
Sitek, Paweł

Chawuthai, Rathachai

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