• Journal of the Korean Institute of Educational Facilities
• /
• v.25 no.1
• /
• pp.13-24
• /
• 2018

Planning and designing training facilities has been developed from educational facilities, and increasingly diversified society has raised a need for facilities dedicated to training and education in a differentiated space separate from the one for regular works. This particular need of our times has led to the expansion of training facilities nationwide although they have something to be desired when it comes to planning and designing with sustainability associated with the locational characteristics of urban space as well as the unique types of facilities taken into account. Against this backdrop, this study will examined a variety of training facilities that have been established since 2000 through theoretical review and conduct intensive analysis on the characteristics of the planning aspects to suggest their significance and implications and to present the overall meanings and ramifications of planning approaches in consideration of new challenges modern training facilities are faced with, which have been revealed through architectural design competitions in recent years. The relevant implications are as follows. First, one of the locational advantages of training facilities, which is commanding beautiful scenery of the surrounding area, can be considered as intent to stress the aspect of a resort, one of the functions of any training facilities. As this study has demonstrated, many training facilities are located near around a beach or a lake. Second, training facilities can be classified into three different types in terms of their location: urban, suburban and resort and such locational characteristics are directly related to intended programs and differentiated links with target users. Third, the architectural styles of training institutes are differentiated in terms of harmonious arrangement between beautiful natural scenery and buildings in consideration of the layout characteristics of major facilities and the distance of ramps in and out of the facilities along with architectural features, including the transparency of building elevation and the type of slopes of roof structure. Fourth, the individual lodging buildings feature a variety of types depending on the design concept with different roles depending on the directional aspects such as the connection of ramps and the relations with the outside. Fifth, outdoor space plans are differentiated according to the intended purpose of training facilities. When it comes to gym facilities, for example, different outdoor space plans are found to be made depending on the original design concept such as outdoor playground-centered planning or golf facilities.

• Journal of the Korean GEO-environmental Society
• /
• v.15 no.6
• /
• pp.67-73
• /
• 2014

Landslide disasters including debris flows are the one of the most frequent natural disasters in Korea, and losses of lives and property damages due to these catastrophic events have been increased every year. Various mitigation programs and related policies have been conducted in order to respond and prepare landslide disasters. Most landslide reduction programs are, however, focused on recovery actions after the disasters and lead to unrealistic consequences to the affected people and their properties. The main objective of this study, therefore, is to evaluate the landslide hazard based on the identification of geomorphological features, which is for the preparedness of the landslide disasters. Two methodologies, SINMAP and vector dispersion analyses are used to simulate those characteristics where landslides are actually located. Results showed that both methods well discriminate geomorphic features between stable and unstable domains. This proves that geomorphological characteristics well describe a relationship with the existing landslide hazard. SINMAP analysis which is based on the consecutive model considering external factors like infiltration is well identify the landslide hazard especially for debris flow type landslides rather than vector dispersion focusing on a specific area. Combining with other methods focusing specific characteristics of geomorphological feature, accurate landslide hazard assessments are implemented.

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

• Korean Journal of Heritage: History & Science
• /
• v.54 no.4
• /
• pp.90-111
• /
• 2021

Seojangdaeyajodo is a drawing of Jangyongyoung's military night training on February 12 (lunar leap month), 1795. Focusing on the Seojangdaeyajodo, the positions and roles of the low-ranking soldier belonging to Jangyongyoung, and the composition and characteristics of military uniforms for each role were examined. The results ascertained by the historical research on the military uniforms are as follows. Deungronggun, noeja, sunryeongsu and daegisu who were placed in front of the king's Seojangdae were the low-ranking soldiers belonging to Jangyongyoung. The soldiers who escorted the king around Seojangdae were lowranking soldiers belonging to Jangyongyoung. The military uniform of the deungronggun was consisted of a jeolrip, a black heopsu, red gweja, indigo jeondae, white haengjeon and black shoes. The low-ranking soldier's heopsu suggested that it could also be a sochangui. He carried a sword and a red lantern. Noeja were divided into a sinjeonsu and a jujangsu. The military uniform of the noeja was consisted of a Jujeolrip, a black heopsu, red gweja, indigo jeondae, white haengjeon, and black shoes. Sunryeongsu were divided into a sinsigisu and a younggisu. The military uniform of the sunryeongsu was consisted of a jeongeon, a black heopsu, red gweja, indigo jeondae white haengjeon and black shoes. He carried a sword and a red lantern. The military uniform of the daegisu was consisted of a jeongeon, a black heopsu, blue gweja, indigo jeondae, white haengjeon and black shoes. He carried a sword and a flag. The soldiers surrounding Seojangdae and the seongjeonggun defending the fortress were the Chogun. The military uniform of the chogun was consisted of a jeolrip, a black heopsu, houi, indigo jeondae, white haengjeon and straw shoes. Houi was applying the five directional colors: the east is blue, the west is white, the south is red, and the north is black. He carried a sword and a gun. It was presented as an illustration of costumes that could produce contents by reflecting on these historical results. The basic principle of the illustration was to present the standards for 3D content production or actual production. Samples of form, color, and material according to the times and status were presented. The front, the side, and the back of each costume and the feature were presented, and the colors were presented in RGB and CMYK.