• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.309-318
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• 2001

In this paper, we describe methods that analyze a human gesture. A human interface(HI) system for analyzing gesture extracts the head and hand regions after taking image sequence of and operators continuous behavior using CCD cameras. As gestures are accomplished with operators head and hands motion, we extract the head and hand regions to analyze gestures and calculate geometrical information of extracted skin regions. The analysis of head motion is possible by obtaining the face direction. We assume that head is ellipsoid with 3D coordinates to locate the face features likes eyes, nose and mouth on its surface. If was know the center of feature points, the angle of the center in the ellipsoid is the direction of the face. The hand region obtained from preprocessing is able to include hands as well as arms. For extracting only the hand region from preprocessing, we should find the wrist line to divide the hand and arm regions. After distinguishing the hand region by the wrist line, we model the hand region as an ellipse for the analysis of hand data. Also, the finger part is represented as a long and narrow shape. We extract hand information such as size, position, and shape.

• Particle and aerosol research
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• v.9 no.3
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• pp.163-171
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• 2013

Soot particles emitted from combustion processes are often coated by non-absorbing organic materials, which enhance the global warming effect of soot particles. It is of importance to study the condensation characteristics of soot particles experimentally and theoretically to reduce the uncertainty of the climate impact of soot particles. In this study, the condensational growth of soot particles in a tubular coater was modeled by a one-dimensional (1D) plug flow model and a two-dimensional (2D) laminar flow model. The effects of 2D heat and mass transports on the predicted particle growth were investigated. The temperature and coating material vapor concentration distributions in radial direction, which the 1D model could not accounted for, affected substantially the particle growth in the coater. Under the simulated conditions, the differences between the temperatures and vapor concentrations near the wall and at the tube center were large. The neglect of these variations by the 1D model resulted in a large error in modeling the mass transfer and aerosol dynamics occurring in the coater. The 1D model predicted the average temperature and vapor concentration quite accurately but overestimated the average diameter of the growing particles considerably. At the outermost grid, at which condensation begins earliest due to the lowest temperature and saturation vapor concentration, condensing vapor was exhausted rapidly because of the competition between condensations on the wall and on the particle surface, decreasing the growth rate. At the center of the tube, on the other hand, the growth rate was low due to high temperature and saturation vapor concentration. The effects of Brownian diffusion and thermophoresis were not high enough to transport the coating material vapor quickly from the tube center to the wall. The 1D model based on perfect radial mixing could not take into account this phenomenon, resulting in a much higher growth rate than what the 2D model predicted. The result of this study indicates that contrary to a previous report for a thermodenuder, 2D heat and mass transports must be taken into account to model accurately the condensational particle growth in a coater.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.353-362
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• 2012

3D building modeling from airborne Lidar without model regularization may cause positional errors or topological inconsistency in building models. Regularization of 3D building models, on the other hand, restricts the types of models which can be reconstructed. To resolve these issues, this paper modelled 3D buildings from airborne Lidar by building model regularization which considers more various types of buildings. Building points are first segmented into roof planes by clustering in feature space and segmentation in object space. Then, 3D building models are reconstructed by consecutive adjustment of planes, lines, and points to satisfy parallelism, symmetry, and consistency between model components. The experimental results demonstrated that the method could make more various types of 3d building models with regularity. The effects of regularization on the positional accuracies of models were also analyzed quantitatively.

• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.619-628
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• 2011

Normally vision-based 3D human pose recognition technology is used to method for convey human gesture in HCI(Human-Computer Interaction). 2D pose model based recognition method recognizes simple 2D human pose in particular environment. On the other hand, 3D pose model which describes 3D human body skeletal structure can recognize more complex 3D pose than 2D pose model in because it can use joint angle and shape information of body part. In this paper, we describe a development of interactive game contents using pose recognition interface that using 3D human body joint information. Our system was proposed for the purpose that users can control the game contents with body motion without any additional equipment. Poses are recognized comparing current input pose and predefined pose template which is consist of 14 human body joint 3D information. We implement the game contents with the our pose recognition system and make sure about the efficiency of our proposed system. In the future, we will improve the system that can be recognized poses in various environments robustly.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.19-23
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• 2024

Patients with amputations of both hands need prosthetic hands that serve both cosmetic and functional purposes, and research on prosthetic hands using electromyography of remaining muscles is active, but there is still the problem of high cost. In this study, an artificial prosthetic hand was manufactured and its performance was evaluated using low-cost parts and software such as a surface electromyography sensor, machine learning software Edge Impulse, Arduino Nano 33 BLE, and 3D printing. Using signals acquired with surface electromyography sensors and subjected to digital signal processing through Edge Impulse, the flexing movement signals of each finger were transmitted to the fingers of the prosthetic hand model through training to determine the type of finger movement using machine learning. When the digital signal processing conditions were set to a notch filter of 60 Hz, a bandpass filter of 10-300 Hz, and a sampling frequency of 1,000 Hz, the accuracy of machine learning was the highest at 82.1%. The possibility of being confused between each finger flexion movement was highest for the ring finger, with a 44.7% chance of being confused with the movement of the index finger. More research is needed to successfully develop a low-cost prosthetic hand.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.05a
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• pp.135-138
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• 2004

Since the upsetting type is superior to an extrusion type to get the dimensional accuracy of cold forged spur gear, the upsetting type process far spur gear cold forging has been studied. FE analysis of upsetting type process fur spur gear cold forging was performed to investigate about flow pattern of workpiece and die stress. To analyze the elastic characteristics of die, both rigid and elastic material model were used during loading stage. Under-filled defects were detected In lower portions of spur gear forged by upsetting type in experimental. When the elastic material model for die was used, the under-filled defects could be predicted. On the other hand, if the material model of die was rigid, the defects could not been presented because the die deflection was not considered.

• Korean Journal of Computational Design and Engineering
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• v.22 no.1
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• pp.89-99
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• 2017

In order to make a virtual endoscopy system effective for exploring the interior of the 3D model of a human organ, it is necessary to generate an accurate navigation path located inside the 3D model and to obtain consistent camera position and pose estimation along the path. In this paper, we propose an approach to virtual navigation of blood vessels, which makes proper use of orthogonal contours and skeleton curves. The approach generates the orthogonal contours and the skeleton curves from the 3D mesh model and its voxel model, all of which represent the blood vessels. For a navigation zone specified by two nodes on the skeleton curves, it computes the shortest path between the two nodes, estimates the positions and poses of a virtual camera at the nodes in the navigation zone, and interpolates the positions and poses to make the camera move smoothly along the path. In addition to keyboard and mouse input, intuitive hand gestures determined by the Leap Motion SDK are used as user interface for virtual navigation of the blood vessels. The proposed approach provides easy and accurate means for the user to examine the interior of 3D blood vessels without any collisions between the camera and their surface. With a simple user study, we present illustrative examples of applying the approach to 3D mesh models of various blood vessels in order to show its quality and usefulness.

• Conservation Science in Museum
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• v.22
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• pp.15-26
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• 2019

This study was focused on the three-dimensional convergence modeling that can multilaterally analyze internal and external shapes of the Sam-Chongtong Hand Canon by optical precision scanning optimized for acquiring the surface shape and X-ray CT scanning used for obtaining the internal shape. First, the scanning results were converted by compatible extension, after which three-dimensional deviation analysis was conducted to verify mutual conformities. Accordingly, most (56.98%) deviations between the two scanning models was found be ±0.1mm. This result did not influence registration and merging based on the ICP algorithm. The merged data exhibited the external surface color, detailed shapes, internal width, and structure of the hand canon. The three-dimensional model based on optical surface scanning and X-ray CT scanning can be used for traditional technique interpretation as well as digital documentation of cultural heritage. In the future, it will contribute to deliver accessible scientific information of exhibits for visitors.

• Archives of Plastic Surgery
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• v.47 no.5
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• pp.428-434
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• 2020

Background Three-dimensional (3D) model printing improves visualization of anatomical structures in space compared to two-dimensional (2D) data and creates an exact model of the surgical site that can be used for reference during surgery. There is limited evidence on the effects of using 3D models in microsurgical reconstruction on improving clinical outcomes. Methods A retrospective review of patients undergoing reconstructive breast microsurgery procedures from 2017 to 2019 who received computed tomography angiography (CTA) scans only or with 3D models for preoperative surgical planning were performed. Preoperative decision-making to undergo a deep inferior epigastric perforator (DIEP) versus muscle-sparing transverse rectus abdominis myocutaneous (MS-TRAM) flap, as well as whether the decision changed during flap harvest and postoperative complications were tracked based on the preoperative imaging used. In addition, we describe three example cases showing direct application of 3D mold as an accurate model to guide intraoperative dissection in complex microsurgical reconstruction. Results Fifty-eight abdominal-based breast free-flaps performed using conventional CTA were compared with a matched cohort of 58 breast free-flaps performed with 3D model print. There was no flap loss in either group. There was a significant reduction in flap harvest time with use of 3D model (CTA vs. 3D, 117.7±14.2 minutes vs. 109.8±11.6 minutes; P=0.001). In addition, there was no change in preoperative decision on type of flap harvested in all cases in 3D print group (0%), compared with 24.1% change in conventional CTA group. Conclusions Use of 3D print model improves accuracy of preoperative planning and reduces flap harvest time with similar postoperative complications in complex microsurgical reconstruction.