• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.287-299
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• 2008

People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.851-859
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• 2011

In this paper, we propose the method that automatically extracts the silhouette and the joints of consecutive input image, and track joints to trace object for interaction between human and computer. Also the proposed method presents the action of human being to map human body using joints. To implement the algorithm, we model human body using 14 joints to refer to body size. The proposed method converts RGB color image acquired through a single camera to hue, saturation, value images and extracts body's silhouette using the difference between the background and input. Then we automatically extracts joints using the corner points of the extracted silhouette and the data of body's model. The motion of object is tracted by applying block-matching method to areas around joints among all image and the human's motion is mapped using positions of joints. The proposed method is applied to the test videos and the result shows that the proposed method automatically extracts joints and effectively maps human body by the detected joints. Also the human's action is aptly expressed to reflect locations of the joints

• The KIPS Transactions:PartA
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• v.13A no.4 s.101
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• pp.371-380
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• 2006

This paper utilizing model picture of finished clothes in fashion design field various material (textile fabrics) doing Draping directly can invent new design, and do not produce direction sample or poetic theme width and confirm clothes work to simulation. Also, construct database about model and material image and embodied system that can confirm Mapping result by real time. And propose clothes simulation system to dress to 3D human body model of imagination because using several cloth pieces first by process to do so that can do simulation dressing abstracted poetic theme width to 3D model here. Proposed system creates 3D model who put clothes by physical simulation that do fetters to mass-spring model after read 3D human body model file and 2D foundation pattern file. System of this treatise examines collision between triangle that compose human body model for realistic simulation and triangle that compose clothes and achieved reaction processing. Because number of triangle to compose human body is very much, this collision examination and reaction processing need much times. To solve this problem, treatise that see could create realistic picture by method to diminish collision public prosecutor and reaction processing number, and could dress clothes to imagination human body model within water plant taking advantage of Octree space sharing techniques.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.322-333
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• 2022

The Avatar robot, which is one of the teleoperation robots, aims to enable users to feel the robot as a part of the body to intuitively and naturally perform various tasks. Considering the purpose of the avatar robot, an end-effector identical to a human hand is advantageous, but a robotic hand with human hand level performance has not yet been developed. In this paper we propose a new 3-finger robotic hand with human-avatar hand posture mapping algorithm which were integrated with TOCABI-AVATAR, one of the teleoperation system. Due to the flexible rolling contact joints and tendon driven mechanism applied to the finger, the finger could implement adaptive grasping and absorb the impact force caused by unexpected contacts. In addition, human-avatar hand mapping algorithm using five calibration hand postures propose to compensate physical differences between operators. Using the TOCABI-AVATAR system with the robotic hands and mapping algorithm, the operator can perform 13 out of 16 hand postures of grasping taxonomy and 4 gestures. In addition, using the system, we participated in the ANA AVATAR XPRIZE Semi-final and successfully performed three scenarios which including various social interactions as well as object manipulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2042-2059
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• 2019

Digital shadow puppet has traditionally relied on expensive motion capture equipments and complex design. In this paper, a low-cost driven technique is presented, that captures human pose estimation data with simple camera from real scenarios, and use them to drive virtual Chinese shadow play in a 2.5D scene. We propose a special method for extracting human pose data for driving virtual Chinese shadow play, which is called 2.5D human pose estimation. Firstly, we use the 3D human pose estimation method to obtain the initial data. In the process of the following transformation, we treat the depth feature as an implicit feature, and map body joints to the range of constraints. We call the obtain pose data as 2.5D pose data. However, the 2.5D pose data can not better control the shadow puppet directly, due to the difference in motion pattern and composition structure between real pose and shadow puppet. To this end, the 2.5D pose data transformation is carried out in the implicit pose mapping space based on self-network and the final 2.5D pose expression data is produced for animating shadow puppets. Experimental results have demonstrated the effectiveness of our new method.

• Investigative Magnetic Resonance Imaging
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• v.23 no.1
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• pp.17-25
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• 2019

We discuss recent advances in Gd-based $T_1$-weighted MR contrast agents for the mapping of cellular pH. The pH plays a critical role in various biological processes. During the past two decades, several MR contrast agents of strategic importance for pH-mapping have been developed. Some of these agents shed light on the pH fluctuation in the tumor microenvironment. A pH-responsive self-assembled contrast agent facilitates the visualization of tumor size as small as $3mm^3$. Optimization of various parameters is crucial for the development of pH-responsive contrast agents. In due course, the new contrast agents may provide significant insight into pH fluctuations in the human body.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• Fashion & Textile Research Journal
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• v.6 no.4
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• pp.503-510
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• 2004

The study is one of fundamental researches for the development of future smart clothing and textile products with blocking properties from electromagnetic waves by analyzing human physical symptoms in using electromagnetic products in such an environments. Among various textiles in the experiment, nano silver has shown the best blocking performance from electromagnetic waves, which decreases depending on the distance. The power spectrum distribution and the incidence of electroencephalogram between blocking materials and aural stimuli has shown that, ${\beta}$, wave appeared to be active in all channels except for $T_4$, whereas all waves appeared with processed materials and especially with nano silver silk(NSS), ${\alpha}$, ${\beta}$, ${\theta}$, ${\gamma}$ waves appeared active in all regions. As for the brain mapping of ${\alpha}$ wave according to time, there found a strong activity in $P_3$, $P_4$ of the parietal lobe, with all materials on all time regions. With silk nylon metal(SNM) and NSS, it appeared strong in $F_3$, $F_4$ as well. As for ${\beta}$, wave, the activity appeared strong in frontal lobe before 7min. 30sec, where it tends to diminish abruptly in 7min. 30sec. to 13min. 30sec. region. After 13min., it regained gradually. With NSS, it appeared strong in all areas except for the farthest $T_4$. The appearance of ${\nu}$ wave can be deduced as it can affect human body with its toxic property while the silver particles become nano-sized. Therefore, the study conducted with human participants requires a proper particle size of it which would not penetrate cellular tissues and a proper binder and binding treatment for it, to prevent the physical fatigues and the potential diseases. However, it is highly required for back-up researches to verify various aspects in applying nano silver to textile products.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.611-619
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• 2008

2.5D Mapping system that embody in this paper can make new design by doing draping to live various texture and model picture image of fashion clothes by pattern, and can confirm clothes work to simulation without producing direction sample or product directly. Also, the system can support function that can forecast fabric design and state of end article exactly, and the system can bring competitive power elevation of fashion industry and cost-cutting effect by doing draping using database of fabric and model picture image. 2.5D Mapping system composed and embodied by mesh warp algorithm module, light and shade extraction and application module, mapping path extraction module, mesh creation and transformation module, and 2.5D mapping module for more natural draping. Future work plans to study 3D fashion design system that graft together 3D clothes technology and 3D human body embodiment technology to do based on embodiment technology of 2.5D mapping system and overcomes expression limit of 2.5D mapping technology.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.960-966
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• 2009

A manifold is used to represent a relationship between high-dimensional data samples in low-dimensional space. In human pose estimation, it is created in low-dimensional space for processing image and 3D body configuration data. Manifold learning is to build a manifold. But it is vulnerable to silhouette variations. Such silhouette variations are occurred due to view-change, person-change, distance-change, and noises. Representing silhouette variations in a single manifold is impossible. In this paper, we focus a silhouette variation problem occurred by view-change. In previous view invariant pose estimation methods based on manifold learning, there were two ways. One is modeling manifolds for all view points. The other is to extract view factors from mapping functions. But these methods do not support one by one mapping for silhouettes and corresponding body configurations because of unsupervised learning. Modeling manifold and extracting view factors are very complex. So we propose a method based on triple manifolds. These are view manifold, pose manifold, and body configuration manifold. In order to build manifolds, we employ biased manifold learning. After building manifolds, we learn mapping functions among spaces (2D image space, pose manifold space, view manifold space, body configuration manifold space, 3D body configuration space). In our experiments, we could estimate various body poses from 24 view points.