Moodleah, Samart
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Moodleah, Samart
Alternative Name
Moodleah, S.
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Email
samart.mo@kmitl.ac.th
13 results
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Item type:Publication, Pig Carcass Assessment on Image Segmentation(2021-01-01); 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%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Pattern detection for toolpath generation on triangular meshes for 5-axis CNC machining(2020-01-01); Makhanov, Stanislav S.Toolpath generation for freeform triangular meshes for 5-axis milling machines using an optimal feeding direction (OFD) field is an important topic in subtractive manufacturing technology. By considering parameters in both the CAD and the CAM stages, there exists a direction(s) at every cutter contact (CC) point, such that the machining efficiency is maximal. The directions could form a special structure which can be detected for toolpath generation on the CAD stage. In this paper, a novel toolpath generation method is proposed to create a toolpath for freeform triangular meshes by detecting regular structures of the OFD in the parametric domain using moment invariants. Three common templates, corresponding to standard toolpath structures, are considered: (1) curl (contour/spiral contour path), (2) star/spiral (radial path), and (3) shear (zigzag path). Mesh parameterization is used to flatten the 3D triangulated surface. A transfinite interpolation (TFI) is utilized to construct the toolpath on the flattened surface. The final toolpath is then obtained by the inverse map. The proposed method has been compared with conventional and standard commercial toolpaths. Numerical results show that the pattern detection algorithm helps to find an appropriate toolpath strategy. The generated toolpaths follow the OFDs closer than the competing methods, and therefore, require shorter machining time. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, AI-Powered MetaHuman Interviewer: Serious Game for Student Job Interview Skills(2025-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Points using Localized Distance for Contour Generation from Point Cloud for 3D Printing(2021-01-01); Kirimasthong, KhwuntaWe 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adaptive Slicing of Point Cloud Directly with Discrete Interpolable-Area Error Profile in Additive Manufacturing(2023-02-01); Kirimasthong, KhwuntaPoint cloud objects have gained popularity in three-dimensional (3D) printing recently due to advancements in reverse engineering technology. Fabricating an object with a fused deposition modeling (FDM) printer requires converting the object to layered contours, which involves a slicing process. The slicing process of a point cloud object usually requires reconstructing a 3D object from a point cloud, which requires users' deep understanding of 3D modeling software and a laborious work process. To avoid these problems, the direct slicing of point cloud objects is gaining more popularity. This research work proposes an adaptive slicing approach from point cloud objects directly without surface reconstruction. The adaptive slicing maintains the global geometry error while requiring a smaller number of fabrication layers and printing time. A new error profile used in the adaptive slicing approach is introduced. It approximates the geometry error from the point cloud directly based on the discrete interpolable-area (DIA) error between two adjacent layers. The interpolable capability of the DIA error profile allows the adaptive slicing algorithm to efficiently measure the geometry error of a point cloud. We perform the proposed algorithm with four point cloud models that represent both symmetrical and asymmetrical shapes. The adaptive slicing results show that the performance is increased by 8.05%-32.73% while maintaining accuracy compared to traditional uniform slicing. Furthermore, the fabrication time and materials used are reduced by 10.30%-39.10% and 1.01%-13.47%, respectively. Based on these results, further research can be focused on finding an optimal threshold between the accuracy of the contour projection and the distance between the layers, which could further improve fabrication performance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Low-complexity turbo equalization using E-BAD detector combine with LDPC codes for high-density magnetic recording(2008-10-06); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Development of Game-Based Learning in Virtual Reality for Fire Safety Training in Thailand(2021-06-30) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automated Classification of User Exercise Poses in Virtual Reality Using Machine Learning-Based Human Pose Estimation(2025-01-01); Human Pose Estimation (HPE) using machine learning presents significant potential for objectively analyzing user movements within Virtual Reality (VR) environments, particularly for applications involving guided physical exercises or interactive tasks. Analyzing 2D video footage from VR sessions for HPE offers a computationally efficient approach for tracking user movements. However, accuracy can be compromised by various factors, including occlusions from clothing or the VR headset itself. This study develops and evaluates a robust HPE system specifically designed to accurately classify predefined poses performed by users within such VR environments. The proposed system utilizes MediaPipe for corporal landmark extraction from user images (for training) and employs a Stacking Classifier ensemble, with XGBClassifier as the meta-learner, to classify six key exercise poses. Key results demonstrate an overall pose classification accuracy of 94% on a dedicated test set, with certain poses like ‘Butterfly Hug’ and ‘Arm’ achieving 100% accuracy. These findings highlight the system’s potential to provide reliable, quantitative assessments of user adherence to prescribed movements in immersive interactive environments, offering valuable data for applications requiring objective analysis of physical performance in VR. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Naval Wargame Prototyping: Multiplayer Real-Time Strategy Game Simulation Using Unreal Engine(2023-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation(2023-12-01); Makhanov, Stanislav S.A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the contour of the tumor. The agents move similarly to robots navigating in the unknown environment with the goal of maximizing the rewards. The individual agent does not know the goal of the entire population. However, since the robots communicate, the model is able to understand the global information and fit the irregular boundaries of complicated objects. Combining the RL with a neural network makes it possible to automatically learn and select the local features. In particular, the agents handle the edge leaks and artifacts typical for the US images. The proposed model outperforms 13 state-of-the-art algorithms, including selected deep learning models and their modifications.