Moodleah, Samart
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Moodleah, Samart
Alternative Name
Moodleah, S.
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Email
samart.mo@kmitl.ac.th
6 results
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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, 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, 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, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multi-agent deep learning on tensor fields for segmentation of ultrasound images(2026-03-01); Makhanov, Stanislav S.Medical image analysis often relies on vector fields (VF), which are fundamental to deterministic models such as Active Contours, Level Set Methods, Phase Portrait Analysis, and artificial agent–based formulations. We experimentally demonstrate that a Deep Learning Neural Network (DLNN) capable of interpreting VF structures can substantially enhance the decision-making capabilities of artificial agents. We introduce a novel hybrid framework that integrates artificial life (AL) agents operating within a VF with a DLNN that guides their behavior. A key innovation of the model is the initialization of AL agents using streamlines derived from the VF orthogonal to the generalized gradient vector flow (GGVF) field. The VF is further transformed into a bi-directional Tensor Field (TF), where the spatial distribution and classification of degenerate points (DPs) serve as critical features. These DPs are leveraged to train AL agents through the DLNN, enabling them to follow meaningful anatomical structures. The framework employs DeepLabV3+ with ResNet50 as the backbone and is trained on 179 benign and 107 malignant breast ultrasound images collected at Thammasat University Hospital (TUH) and annotated by three leading radiologists, in addition to the BUSI and UDIAT datasets. Using 10-fold cross-validation, the proposed method achieves stable and robust performance across three datasets. Mean Dice scores of 94.84±1.63% (TUH), 94.16±1.62% (BUSI), and 93.67±1.51% (UDIAT) are obtained, with corresponding IoU values of 91.19±1.76%, 90.21±1.83% and 89.08±1.70%, demonstrating strong generalization across diverse imaging conditions. Comparative evaluations against state-of-the-art methods confirm the superiority of the proposed model. A video demonstration is available at: https://tinyurl.com/AL-DLNN.