Moodleah, Samart

Preferred name

Moodleah, Samart

Alternative Name

Moodleah, S.

Main Affiliation

School of Information Technology

samart.mo@kmitl.ac.th

Now showing 1 - 7 of 7

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Pig Carcass Assessment on Image Segmentation
(2021-01-01)
Tanthong, Jitpanu
;
Moodleah, Samart
;
Vittayakorn, Sirion
Pork is the most commonly consumed meat across the world: about one-third of all meat consumed is pork, ahead of beef and chicken. Every day a massive number of pig carcasses enter the production pipeline. When entering the pipeline, the carcasses are graded by the slaughterhouses to determine the market price of the meat. The grading criteria could depend on a variety of factors such as the tenderness, color, pH value, water holding capacity as well as the proportion of red meat inside the carcasses. Since the grade can be used to determine the market price and the commercial usage of the meat, this process is crucial. Unfortunately, the grading process is not only time consuming but also requires expertise. To mitigate this problem, in this work we propose: 1) a pig carcass image dataset segmented by experts, 2) an LSQ index image dataset and 3) an algorithm for carcass quality analysis based on the ratio of red meat inside the carcasses, or the Lenden-Speck-Quotient (LSQ). Our experimental results demonstrate that the performance of the proposed LSQ index algorithm is reliable and agrees with experts' annotation with MAPE 5.55%.
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Points using Localized Distance for Contour Generation from Point Cloud for 3D Printing
(2021-01-01)
Moodleah, Samart
;
Kirimasthong, Khwunta
We present a robust and simple method to select the most correlated points (in each layer) for layered contour generation from a 3D point cloud model in additive manufacturing. The contour projection of each layer point uses a planar least square projection technique. One critical step in contour projection is that the correlation between each projection point and other points (weights) on a slicing plane directly affects to the contour generation accuracy. The constructing contour from point cloud directly is a challenging task because there is no information of mesh topology, hence, no sequential order of points for contour generation. A search algorithm to find the most correlated points from skeletal points (reference) using the localized distance function is implemented. The experiment results show that our method reduces an average accuracy error for both wide and narrow point distributions by 15.57% to 35.20% and 4.28% to 12.78% respectively compared to the existina method.
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Low-complexity turbo equalization using E-BAD detector combine with LDPC codes for high-density magnetic recording
(2008-10-06)
Moodleah, S.
;
Benjangkaprasert, C.
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Sangaroon, O.
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Udomsripaiboon, T.
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Sa-Ad, S.
An extend bi-directional arbitrated decision feedback equalizer (E-BAD) was developed from BAD which shown that performance is improved at low error rate. BAD detector can be replaced for the high-complexity detector in turbo equalization for a coded magnetic recording channel which the performance is closed. This paper considers a performance evaluation of E-BAD detector in turbo equalization with selected LDPC code which the performance is improved while keep low-complexity structure. ©2008 IEEE.
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A Development of Game-Based Learning in Virtual Reality for Fire Safety Training in Thailand
(2021-06-30)
Satapanasatien, Kodchaporn
;
Phuawiriyakul, Thanchanok
;
Moodleah, Samart
Fire incidents damaged both the economy and human life. In the past three decades, 59,387 fire incidents in Thailand approximately lost 40 billion baht and 2,076 deaths. Fire safety training methods are organized in a very limited number each year for many reasons such as training place or safety concern. We propose game-based learning for fire safety training using virtual reality technology. We create five learning lessons based on fire training contents and three playing stages (play, learn and test) that players can interact with. It behaves as a self-learning tool that can be used often and overcome the difficulty in organizing fire safety training. In addition, virtual technology simulates realistic computer graphic contents and rich interactive actions. Most importantly it offers a safe virtual environment; it extends the audience to a wide range of ages. Our game-based training is evaluated, and the result shows that fire safety knowledge is increased by 122% and 63% compared to the non-training and the traditional training respectively as well as users' satisfaction average score is exceeded 90%.
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Naval Wargame Prototyping: Multiplayer Real-Time Strategy Game Simulation Using Unreal Engine
(2023-01-01)
Chavanit, Nattawat
;
Bualoy, Sukawit
;
Moodleah, Samart
We propose an integration framework for a wargame prototype using the modern game engine - Unreal Engine. This wargame functions as a simulation tool for strategy training, strategy testing, and simulating enemy forces like warships, aircraft, and weapons. Existing wargames come in proprietary and free versions. The former is often expensive and exclusive due to security reasons, while developing a functional wargame is complex, requiring various technological components such as a physics system and artificial intelligence (AI). To overcome these challenges, we propose a rapid prototype using Unreal Engine. This approach leverages advanced technology and ensures the prototype is ready for future upgrades when new versions of the game engine are released. We evaluate the prototype's system capabilities and expert assessments.
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Human Posture Detection in Virtual Reality Applications for Stress Reduction
(2024-01-01)
Kongchansawang, Panuwat
;
Wongsiripa, Santakorn
;
Moodleah, Samart
This research explores the potential of human pose estimation (HPE) using machine learning to analyze user movements within a virtual reality (VR) environment for stress assessment. By analyzing 2D video footage captured during VR sessions, HPE provides a computationally efficient method for tracking user poses, making it an ideal tool for evaluating therapeutic exercises. However, challenges such as clothing occlusions, background complexity, and varying lighting conditions can affect HPE accuracy. This study investigates the effectiveness of HPE in overcoming these challenges within a VR therapy context. We propose a robust system designed to accurately assess user posture during VR therapy simulations. Our approach leverages advanced machine learning algorithms to enhance the precision of pose estimation, even under challenging conditions. Preliminary results indicate that our system achieves an impressive overall accuracy of 94%, demonstrating its potential to provide reliable assessments of user movements.
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Learning by Doing: The Impact of Virtual Reality Scenarios on First Aid Training Effectiveness
(2024-01-01)
Kawmong, Chanikarn
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Kuljittwattana, Ittiporn
;
Moodleah, Samart
Limited accessibility, high costs, and potential lack of user engagement can hinder the effectiveness of traditional first-aid training methods. Thailand serves as a case study, where data from 2017-2021 reveals a concerning incidence of emergency illnesses (12,000 per 100,000 people) and a concerning mortality rate (179.6 per 100,000 people). This study investigates the potential of Virtual Reality (VR) technology as a novel and engaging approach to first-aid t raining, s pecifically de signed for Thai speakers. Participants engaged with a VR first aid training game featuring interactive modules on essential equipment, emergency procedures, and Cardiopulmonary Resuscitation (CPR) with Automated External Defibrillator (AED) u sage. Compared to a control group, participants who trained with the VR First Aid game demonstrated a significant increase in first-aid knowledge (31.00%). Additionally, the VR training method yielded a substantial increase in user satisfaction (42.42%) compared to traditional approaches. These findings highlight V R technology as a promising alternative for first-aid training, potentially offering improved accessibility, engagement, and learning outcomes compared to traditional methods.

Moodleah, Samart

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