• Journal of Korea Multimedia Society
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• v.25 no.4
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• pp.575-582
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• 2022

In the manufacturing environment, research to minimize robot collisions with human beings have been widespread, but in order to interact with robots, it is important to precisely recognize and predict human actions. In this research, after enhancing performance by applying group normalization to the Hourglass model to detect the operator motion, the skeleton was estimated and data were created using this model. And then, three types of operator's movements were recognized using LSTM. As results of the experiment, the accuracy was enhanced by 1% using group normalization, and the recognition accuracy was 99.6%.

• Journal of the Korea Computer Graphics Society
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• v.23 no.5
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• pp.29-38
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• 2017

We propose a method for tree-inspired chair modeling that can generate a tree-branch pattern in the skeleton of an arbitrary chair shape. Unlike existing methods that merge multiple-input models, the proposed method requires only one mesh as input, namely the contour mesh of the user's desired part, to model the chair with a branch pattern generated by tree-growth simulation. We propose a new method for the efficient extraction of the contour-mesh region in the tree-branch pattern. First, we extract the contour mesh based on the face area of the input mesh. We then use the front and back mesh information to generate a skeleton mesh that reconstructs the connection information. In addition, to obtain the tree-branch pattern matching the shape of the input model, we propose a three-way tree-growth simulation method that considers the tangent vector of the shape surface. The proposed method reveals a new type of furniture modeling by using an existing furniture model and simple parameter values to model tree branches shaped appropriately for the input model skeleton. Our experiments demonstrate the performance and effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.178-183
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• 2008

Human motion analysis is researched as a new method for human-robot interaction (HRI) because it concerns with the key techniques of HRI such as motion tracking and pose recognition. To analysis human motion, extracting features of human body from sequential images plays an important role. After finding the silhouette of human body from the sequential images obtained by CCD color camera, the skeleton model is frequently used in order to represent the human motion. In this paper, using the silhouette of human body, we propose the feature extraction method based on hybrid skeleton for detecting human motion. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• International Journal of CAD/CAM
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• v.7 no.1
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• pp.81-89
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• 2007

As the modular production is an important issue in globalized manufacturing industries, sub modules or parts of the final product are provided by many suppliers. Some part suppliers design their own products for themselves. In some cases, part supplier may provide the same type product to multiple 1-tier companies. Because all suppliers and 1-tier companies can not use the same CAD system in general case, the engineering change in the CAD model of one company could not propagate to related CAD models of other companies directly. Although they use the same CAD system, it is hard to share their CAD model with each other because of company security policy. In this paper, the neutral reference model, which consists of the neutral skeleton model and the external reference model, is proposed to apply a global top-down modeling approach to collaborating companies.

• Korean Journal of Computational Design and Engineering
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• v.13 no.4
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• pp.243-254
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• 2008

As modular production becoming increasingly widespread in globalized manufacturing industries, sub modules or parts of the final product are being provided by many suppliers. Some part suppliers design their own products for themselves. In some cases, part suppliers provide the same type of product to multiple OEM companies. Because all part suppliers and OEM companies typically cannot use the same CAD system, engineering change in the CAD model of one company cannot be directly propagated to related CAD models of other companies. Even if two companies use the same CAD system, it may be difficult to share their CAD model owing to corporate security policy. In this paper, a neutral reference model that consists of a neutral skeleton model and an external reference data model is proposed as a new medium for the sharing and propagation of engineering change information among collaborating companies.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.267-277
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• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.1199-1203
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• 2008

It has been recognized that unsaturated soil behavior plays an importantrole in geomechanics. In the last decade several constitutive models have been proposed and used in the analysis. Many of them, however, are constructed in the frame work of rate independent model such as elasto-plastic one. Although rate dependency is an important characteristics of soil for both saturated and unsaturated soils, very few models have been developed taking account of rate dependency. In the present paper, we have developed an elasto-viscoplastic model considering an effect of suction based on the overstress-type viscoplasticity with soil structure degradation. In the model, we have adopted an averaged pore pressure composed of pore water pressure and air pressure to determine the effective stress.

• Smart Media Journal
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• v.11 no.3
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• pp.90-101
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• 2022

Buses are a popular means of transportation. As such, thorough preparation is needed for passenger safety management. However, the safety system is insufficient because there are accidents such as a death accident occurred when the bus departed without recognizing the elderly approaching to get on in 2018. There is a safety system that prevents pinching accidents through sensors on the back door stairs, but such a system does not prevent accidents that occur in the process of getting on and off like the above accident. If it is possible to predict the intention of bus passengers to get on and off, it will help to develop a safety system to prevent such accidents. However, studies predicting the intention of passengers to get on and off are insufficient. Therefore, in this paper, we propose a 1×1 CNN3D-based getting on and off intention prediction model using skeleton keypoints of passengers extracted from the camera image attached to the bus through UDP-Pose. The proposed model shows approximately 1~2% higher accuracy than the RNN and LSTM models in predicting passenger's getting on and off intentions.

• Steel and Composite Structures
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• v.49 no.6
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• pp.615-631
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• 2023

To investigate the seismic behavior of steel slag self-stressing concrete-filled circular steel tubular (SSSCFCST) columns, 14 specimens were designed, namely, 10 SSSCFCST columns and four ordinary steel slag (SS) concrete (SSC)-filled circular steel tubular (SSCFCST) columns. Comparative tests were conducted under low reversed cyclic loading considering various parameters, such as the axial compression ratio, diameter-thickness ratio, shear-span ratio, and expansion ratio of SSC. The failure process of the specimens was observed, and hysteretic and skeleton curves were obtained. Next, the influence of these parameters on the hysteretic behavior of the SSSCFCST columns was analyzed. The self stress of SS considerably increased the bearing capacity and ductility of the specimens. Results indicated that specimens with a shear-span ratio of 1.83 exhibited compression bending failure, whereas those with shear-span ratios of 0.91 or 1.37 exhibited drum-shaped cracking failure. However, shear-bond failure occurred in the nonloading direction. The stiffness of the falling section of the specimens decreased with increasing shear-span ratio. The hysteretic curves exhibited a weak pinch phenomenon, and their shapes evolved from a full shuttle shape to a bow shape during loading. The skeleton curves of the specimens were nearly complete, progressing through elastic, elastoplastic, and plastic stages. Based on the experimental study and considering the effects of the SSC expansion rate, shear-span ratio, diameter-thickness ratio, and axial compression ratio on the seismic behavior, a peak displacement coefficient of 0.91 was introduced through regression analysis. A simplified method for calculating load-displacement skeleton curves was proposed and loading and unloading rules for SSSCFCST columns were provided. The load-displacement restorative force model of the specimens was established. These findings can serve as a guide for further research and practical application of SSSCFCST columns.