• Geomechanics and Engineering
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• v.11 no.1
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• pp.59-76
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• 2016

Penetration problems in geomechanics are common. Usually the soil is heavily disturbed around the penetrating bodies and large deformations and distortions can occur. The simulation of the installation of displacement piles is a good example of the interest of these types of problems for geomechanics. In this paper the Material Point Method is used to overcome the difficulties associated with the simulations of problems involving large deformation and full displacement type penetration. Recent modifications of the Material Point Method known as Generalized Interpolation Material Point and the Convected Particle Domain Interpolation are also used and evaluated in some of the examples. Herein a footing submitted to large settlements is presented and simulated, together with the processes associated to a driven pile under undrained conditions. The displacements of the soil surrounding the pile are compared with those obtained by the Small Strain Path Method. In addition, the Modified Cam Clay model is implemented in a code of MPM and used to simulate the process of driving a pile in dry sand. Good and rather encouraging agreement is found between compared data.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.253-263
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• 1993

Recent interest in Taguchi's methods have led to developments of joint modelling of the mean and dispersion in generalized linear models. Since a single data transformation cannot produce all the necessary conditions for an analysis, for the analysis of the Taguchi data, the use of the generalized linear models is preferred to a commonly used data transformation method. In this paper, we will illustrate this point and provide GLIM macros to implement the joint modelling of the mean and dispersion in generalized linear models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.467-476
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• 2020

Indoor space has been one of the focal points for geospatial research as various factors such as increasing demands for application and demand for adaptive response in emergencies have arisen. IndoorGML (Indoor Geography Markup Language) has provided a standardized method of representing the topological aspect of micro-scale environments, with its extensive specifications and flexible applicability. However, as more real-world problems and needs demand attention, suggestions to improve this standard, such as representing IndoorPOI (Indoor Points of Interest), have arisen. Hence, existing algorithms and functionalities that we use on perceiving these indoor spaces must also adapt to accommodate said improvements. In this study, we explore how to define spatial neighborhoods in indoor spaces represented by an integrated IndoorGML and IndoorPOI data. We revisit existing approaches to combine the aforementioned datasets and refine previous approaches to perform neighborhood spatial queries in 3D. We implement the proposed algorithm in three use cases using sample datasets representing a real-world structure to demonstrate its effectiveness for performing indoor spatial analysis.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of the Korea Fashion and Costume Design Association
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• v.13 no.3
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• pp.69-80
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• 2011

The purpose of this study was to clothing behavior according to the self-esteem, body cathexis in multicultural familie adolescents. The data was collected by using questionnaire survey based on pre-tests, and main survey conducted in Seoul, Gyeonggi province and Chungcheong province. The 236 participants came from 135 multicultural families and 101 Korean Self-esteem, body cathexis, clothing behavior were examined through 5-point likert scale. 3 factors including clothing interest, social approval and clothing conformity were used as clothing behavior. The analysis of the collected data was conducted by using SPSS 18.0. The results of this study is as follows: First, multi-cultural family adolescent showed higher self-esteem, body cathexis, than Korean family adolescent. Second, self-esteem were positively correlated, with body cathexis in both multi-cultural family and Korean family adolescent. Third, in multi-cultural family adolescent, body cathexis were positively correlated with clothing interest, social approval and clothing conformity. In Korean family adolescent, body cathexis were positively correlated with social approval and body cathexis were negatively correlated with clothing conformity.

• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.498-506
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• 2014

With the development of smart transportation, people are likely to find their paths by using navigation and map application. However, the existing retrieval system cannot output the correct retrieval result due to the inaccurate query. In order to remedy this problem, set-based POI search algorithm was proposed. Subsequently, additionally a method for measuring POI name similarity and POI search algorithm supporting classifying duplicate characters were proposed. These algorithms tried to compensate the insufficient part of the compensate set-based POI search algorithm. In this paper, accuracy improvement methods for measuring string similarity in POI data retrieval system are proposed. By formulization, similarity measurement scheme is systematized and generalized with the development of transportation. As a result, it improves the accuracy of the retrieval result. From the experimental results, we can observe that our accuracy improvement methods show better performance than the previous algorithms.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.849-858
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• 2019

Recent developments in computer graphics technology and image processing technology have increased interest in point cloud technology for inputting real space and object information as three-dimensional data. In particular, point cloud technology can accurately provide spatial information, and has attracted a great deal of interest in the field of autonomous vehicles and AR (Augmented Reality)/VR (Virtual Reality). However, in order to provide users with 3D point cloud contents that require more data than conventional 2D images, various technology developments are required. In order to solve these problems, an international standardization organization, MPEG(Moving Picture Experts Group), is in the process of discussing efficient compression and transmission schemes. In this paper, we provide a region division transfer method of 3D point cloud content through extension of existing MPEG-DASH (Dynamic Adaptive Streaming over HTTP)-SRD (Spatial Relationship Description) technology, quality parameters are further defined in the signaling message so that the quality parameters can be selectively determined according to the user's request. We also design a verification platform for ROUTE (Real Time Object Delivery Over Unidirectional Transport)/DASH based heterogeneous network environment and use the results to validate the proposed technology.

• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.696-702
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• 2014

This study was conducted to provide resonable price of timber that come out from certified forests for sustainable forest management. To accomplish this study objectives, we analyzed compound interest method, willingness to pay (WTP) and price sensitivity measurement (PSM) when buying certified pulp. In case of compound interest method, we used prime cost by average price per ha of each area (Hongcheon, Inje, Shinnam) and unit price that add up the pulp price and investment costs. Interest rate reflects 2 to 6% and investment period apply to 5 years. WTP and PSM data were collected from questionnaire survey. As a result, if apply to interest rate of 2% and investment period of 3 years, result values are quite similar to WTP of 5% and optimal pricing point of PSM. That also showed similar pattern in each area.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.6
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• pp.57-63
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• 2005

According to increasing of demand for high accurate and quick measuring technology, they became to interest in industrial photogrammetry that can satisfy with this needs. This study is examined O-2A aircraft to check the application of Industrial Photogrammetry technology. To measure the aircraft alignment, the check points marked on it were used without exact aircraft design data. And to check any deformation of its external original feature, Pro-spot system has been used.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.167-175
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• 2017

The purpose of this study was to investigate the convergence factors affecting behavior of oral health improvement perceived by the subjective oral health awareness perception factors in some college students. The questionnaire was conducted from April 10, 2017 to April 30, 2013 for 300 students at S university and the and the 290 final data were used. Oral health knowledge, oral health importance measured by 5-point, oral health status and oral health interest are 11.46, 1.71, 2.78, 2.52 respectively. Significant differences from experience of oral health education for oral health knowledge, oral health importance, oral health interest have been observed. The convergence factors influencing acts of oral health promotion were oral health status(${\beta}=-0.188$) and oral health interest(${\beta}=-0.265$) the higer the oral health status and oral health interest, the better acts of oral health promotion. Therefore, effective oral health education programs should be developed to increase oral health knowledge and interest in oral health and to change behavior and attitude.