• Cartoon and Animation Studies
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• s.49
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• pp.247-265
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• 2017

With the increasingly diverse, sophisticated and complex character animations that can be represented in 3D animations, the importance of rigging, which can most directly affect animating quality, is becoming more and more important. In addition, rapidity is another crucial aspect of 3D animation production. So, the importance of technical director's role which is accurate and rapid handling of rigging pipeline building and immediate application and, corrections of errors during the longest and manpower consuming animation production is more becoming key. Baek Ji Won and Kim Jae-woong (2014) said, "The technical director is adding new importance to the new job, which is created by 3D animation, in conjunction with the limited production period, manpower, budget and production process." Most major overseas studios are developing in-house software to handle rigging and animation processes. Software development code is used to freely develop and modify production pipelines in accordance with the direction of the work. They are making efforts to build an optimal environment for animators. However, too many efforts and ineffective efforts have been made to develop, adapt, and stabilize the rigging process for small producers, creators, and students who do not have the capacity to develop their own in-house software or hire a technical director. This study suggests the most suitable auto-rigging tool among the many auto-rigging tools released in the market, and suggests the most accurate and quick auto-rigging process setting method for those who have insufficient knowledge about 3D character rigging. The efficiency of use of auto-rigging tool was examined.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.1
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• pp.106-112
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• 2011

Purpose: Until now, the process was improved by the needs of experimenters personally. But recently, suggestion system in hospital has been activated in various ways. So the department of nuclear medicine laboratory is also aware of the need of operation improvement using suggestion system. It is intend to assist in the development by sharing excellent suggestion cases with other hospitals. Material & Method: A total of 124 suggestion cases from January 2007 to March 2010 were analyzed. Suggestion cases were divided into customer satisfaction, cost reduction, improved testing methods, equipment, environmental improvement, and computational system. Result: Suggestion cases of environmental improvement and computational system were accounted for 26.6% as 33 cases, respectively. Suggestion for customer satisfaction is 25.8% as 32 in a total of 124 cases. Conclusion: Activation of the awareness of operation improvement is induced by suggestion system. By securing system of operation improvement, employees' ideas can lead to the production and systematization. Furthermore, it enhances hospital competitiveness and promotes the development of the hospital.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.493-504
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• 2020

The objectives of this study was to examine the effect of Interactive Video Game on cognitive information processing the elderly. Sixty elderly were attended in this study. Their ages ranged from 65 to 70, with a mean age of 67.60 years. The subjects were randomly assigned to one of three experimental conditions: (1) interactive video game group (n=20), (2) aerobic exercise group (n=20), (3) control group (n=20). The experimental design of this study was analyzed using two-way ANOVAs with repeated measures of groups and time. Cognitive function was assessed by neuroelectrical response, and ERP analysis. The results of the study showed that the interactive video game group and aerobic exercise group showed no significant statistical differences in the response time, response accuracy, amplitude and potential of the performance of the exercise in cognitive function and ERP analysis, but improved the interaction video game group and aerobic exercise (walking) group over the control group. It was concluded that long-term aerobic exercise like interactive video game is associated with attenuation of cognitive decline in the elderly.

• Journal of Navigation and Port Research
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• v.34 no.5
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• pp.367-373
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• 2010

When the numerical analysis is carried out, it is necessary to set proper elements as a feature of analysis domains for more accurate simulations. In this study, Distinct Element Method(DEM) is applied, only considering repulsive force and tensile force except for frictional force and resisting force of particle. When the filled particles with initial Quad-tree type is relocated by DEM, a blank space existing among the particles can be minimized because the shape of particle is circular. Finally, it is the effective feature that the centroidal disposion of the particles is similar to an equilateral triangle. Triangular mesh are formed by using the Delaunay triangular technique on these relocated particles, the quality of triangular mesh is more improved by carrying out Laplace interpolations. The compared result of Aspect Ratio before and after the Laplace interpolation is shown that although the quality of triangular mesh made by DEM is good, the later triangular mesh are higher quality than the formers. In this study, although the developed technique takes a longer calculational time than the previous technique to generate triangular mesh, it is considered that the applicable possibility is very high in the generation of finite element mesh about wave analysis and various numerical simulation to need a complex or reappearance of exact topography.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.553-560
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• 2020

This paper presents an algorithm for identifying and eliminating errors by seagrasses in coastal bathymetry surveying using drone and HD camera. Survey errors due to seagrasses were identified, segmentated and eliminated using a L∗a∗b color space model. Bathymetry survey using a drone and HD camera has many advantages over conventional survey methods such as ship-board acoustic sounder or manual level survey which are time consuming and expensive. However, errors caused by sea bed reflectance due to seagrasses habitat hamper the development of new surveying tool. Seagrasses are the flowering plants which start to grow in November and flourish to maximum density until April in Korea. We developed a new algorithm for identifying seagrasses habitat locations and eliminating errors due to seagrasses to get the accurate depth survey data. We tested our algorithm at Wolpo beach. Bathymetry survey data which were obtained using a drone with HD camera and calibrated to eliminate errors due to seagrasses, were compared with depth survey data obtained using ship-board multi-beam acoustic sounder. The abnormal bathymetry data which are defined as the excess of 1.5 times of a standard deviation of random errors, are composed of 8.6% of the test site of area of 200 m by 300 m. By applying the developed algorithm, 92% of abnnormal bathymetry data were successfully eliminated and 33% of RMS errors were reduced.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.573-581
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• 2020

Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Journal of the Korean Geotechnical Society
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• v.38 no.9
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• pp.69-77
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• 2022

Several studies have been conducted to analyze the risk of ground subsidence occurring in urban areas. Recently, the correlation between the density of underground utilities (i.e., the quantity of buried utilities in the analysis area) and the recorded ground subsidence has been explored to analyze such risk through. Choi et al. (2021) proposed an algorithm to optimize the correlation between the ground subsidence and normalized linear density of underground pipelines. In this study, the optimization algorithm was modified for analysis based on the risk grade. The analysis results using the modified optimization algorithm were compared with the correlation analysis results between the density of underground utilities and recorded ground subsidence presented by Choi et al. (2021). Compared with Choi et al. (2021), three analysis results showed equal or higher accuracy in the correlation analysis with recorded ground subsidence according to risk grade. In particular, for R100, it was divided into five grades and compared with the ratio of the recorded ground subsidence that occurred in grades 4 or higher. As a result, Choi et al. (2021) showed that 86% of recorded ground subsidence occurred in grades 4 or higher, whereas this study showed 93%. It was confirmed that the accuracy of the modified optimization algorithm was improved. The modified optimization algorithm can be applied to develop a ground subsidence risk map for each grade in an urban area, which can be used as basic data for decision-making for underground utility maintenance.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.681-688
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• 2022

In this study, the process of drone photography, automatic volume calculation, total floor area conversion, and waste calculation was constructed as a QGIS plug-in to predict the demolition waste (DW) generated in an aged area where drawing information or building information is uncertain. Through a case study, the high consistency between the automatically calculated volume using the drone and the BIM volume based on the field measurement was confirmed. Field application was carried out for the planned demolition work site, and the consistency between the drone-based volume and the actual measurement-BIM-based volume was reconfirmed. The waste generation unit was applied and the amount of DW was calculated by setting the floor height and building type, and the entire process was completed within 6 hours. Although the difference between building information and building objects through drones occurred according to the setting of temporary structures, loads, and floor heights, it was found that the actual amount of DW was generated more than the initial estimate. It is expected that measures to improve the accuracy of volume and floor area conversion will be required through case studies in the future.