• Journal of the Korea Computer Graphics Society
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• v.21 no.1
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• pp.23-31
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• 2015

We present a method for creating realistic 2D stick figure animations easily and rapidly using captured motion data. Stick figure animations are typically created by drawing a single pose for each frame manually for the entire time interval. In contrast, our method allows the user to summarize an action (e.g. kick, jump) for an extended period of time into a single image in which one or more action lines are drawn over a stick figure to represent the moving directions of body parts. In order to synthesize a series of time-varying poses automatically from the given image, our system first builds a deformable character model that can make arbitrary deformations of the user's stick figure drawing in 2D plane. Then, the system searches for an optimal motion segment that best fits the given pose and action lines from pre-recorded motion database. Deforming the character model to imitate the retrieved motion segment produces the final stick figure animation. We demonstrate the usefulness of our method in creating interesting stick figure animations with little effort through experiments using a variety of stick figure styles and captured motion data.

• Journal of Astronomy and Space Sciences
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• v.38 no.4
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• pp.237-250
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• 2021

A rover is a planetary surface exploration device designed to move across the ground on a planet or a planetary-like body. Exploration rovers are increasingly becoming a vital part of the search for scientific evidence and discoveries on a planetary satellite of the Sun, such as the Moon or Mars. Reliable behavior and predictable locomotion of a rover is important. Understanding soil behavior and its interaction with rover wheels-the terramechanics-is of great importance in rover exploration performance. Up to now, many researchers have adopted Bekker's semiempirical model to predict rover wheelsoil interaction, which is based on the assumption that soil is deformable when a pressure is applied to it. Despite this basic assumption of the model, the pressure-sinkage relation is not fully understood, and it continues to present challenges for rover designers. This article presents a new pressure-sinkage model based on dimensional analysis (DA) and results of bevameter tests. DA was applied to the test results in order to propose a new pressure-sinkage model by reducing physical quantitative parameters. As part of the work, a new bevameter was designed and built so that it could be successfully used to obtain a proper pressure-sinkage relation of Korean Lunar Soil Simulant (KLS-1). The new pressure-sinkage model was constructed by using three different sizes of flat plate diameters of the bevameter. The newly proposed model was compared successfully with other models for validation purposes.

• Journal of Radiation Protection and Research
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• v.47 no.3
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• pp.111-133
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• 2022

The exciting advancement related to the "modeling of digital human" in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.13-22
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• 2023

In this paper, we present a method for estimating full 3D shapes from given 2D silhouette images of human bodies. Because the silhouette only consists of the partial information on the true shape, it is an ill-posed problem. To address the problem, we use the statistical human shape space obtained from the existing large 3D human shape database. The method consists of three steps. First, we extract the boundary pixels and their appropriate normal vectors from the input silhouette images. Then, we initialize the correspondences of each pixel to the vertex of the statistically-deformable 3D human model. Finally, we numerically optimize the parameters of the statistical model to fit best to the given silhouettes. The viability and the robustness of the method is demonstrated with various experiments.