• Journal of Families and Better Life
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• v.20 no.5
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• pp.27-35
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• 2002

The purpose of this study was to analyze consumer's opinion and preference trend for luxurious apartments. Data were collected from 167 consumers visiting luxurious apartment model houses from Jan. 2000 to Nov. 2000. The findings of this study are as follows. 1) It is revealed that consumers are positive for luxurious apartments because of luxurious interior facilities and finishing materials convenient storage space and closet. It is found that they are willing to purchase the luxurious apartment if affordable. 2) It is revealed that consumers are negative in using imported materials in general. However, consumers are positive in using imported materials for kitchen and bathroom utilities because of better design and function. Also it is found that consumers are willing to purchase domestic materials if price and quality are same as imported one. 3) It is revealed that consumers prefer to have most spacious living room and master bed room which are facing South. They prefer to have built-in furniture in multi-in purpose room, children's bedroom and entrance hall in priority order. It is revealed that consumer want to choose their favorable interior plan and finishing materials among several options recommended by constructor.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.81-89
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• 2011

Low-carbon Green Growth is highlighted as the main issue from in and outof Korea. Recently Korean government and local goverment constructed a master plan and related database. Considering this as a starting point the carbon gross emission has become an important factor in the city planning and management of local goverment unit. This research was focused on the analysis of carbon gross emission and the environment of carbon occurrence using statistics and digital forest map for the Gyeonggi-do. Further research need to analysis the carbon absorption using satellite image for periodic database. These database will be available basic data for the policy making.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.10-21
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• 2017

The purposes of this study were to verify the effects of HMR (Home Meal Replacement) selection attributes on satisfaction and loyalty for HMR. In addition, the moderating role of customer value was examined among selection attributes of HMR, satisfaction and loyalty for HMR. Using a convenience sampling method, the data were collected from those who have bought HMR in Seoul and Kyonggi area. After a total of 235 responses were collected, 220 were used for the analyses. The multiple regression analyses were conducted to test the hypotheses. The results are as follows. First, it was found that product practicality and cooking convenience of HMR selection attributes had an effect on satisfaction of HMR but that ingredients safety and package & circulation period did not have an effect on satisfaction of HMR. Second, satisfaction of HMR significantly impacted loyalty for HMR. Third, in low group for customer value, product practicality of HMR selection attributes had an positive effect on satisfaction of HMR, and ingredients safety of HMR selection attributes had an negative effect on satisfaction of HMR. In high group for customer value, cooking convenience of HMR selection attributes had an positive effect on satisfaction of HMR. In low group as high group for customer value, satisfaction of HMR had a greater impact on loyalty for HMR.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.215-227
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• 2017

The development of information technology are leading to the rapid evolution of SNS (Social Networking Services) in various directions and industries. Initially, SNS have been used as the form of networking between user's groups. Currently SNS has been developed towards multiple purposes and platforms such as the promotion and the advertising of a company. There are many SNSs including QQ, Weibo, and Wechat and so on in China. However, the use of companies in terms of advertising and information sharing with the customers do not meet the trend in China. Especially, there were little researches for Chinese travel agencies how to utilize SNS for attracting new customers and making them contribute to the firm's performance. In this study, we try to investigate the impact of the firm's characteristics and the usage of SNS on the performance of Chinese travel agencies. Based on Top 100 China travel agencies in 2009, we analyzed the relationships between firm's characteristics and the usage of SNS, and firm's performances in 2013. We expect that our study can contribute to the increasing academic and practical needs on the empirical evidence of the impacts of the SNS utilizations on the firm's performance.

• Safety and Health at Work
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• v.6 no.1
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• pp.1-8
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• 2015

Time-loss injuries are still a major occurrence in Canada, injuring thousands of Canadian workers each year. With obesity rates on the rise across the country, as well as around the world, it is important that the possible effects of obesity in the workplace be fully understood, especially those effects linked to lost-time injuries. The aim of this paper was to evaluate predictors of workplace lost-time injuries and how they may be related to obesity or high body mass index by examining factors associated with lost-time injuries in the health care sector, a well-studied industry with the highest number of reported time loss injuries in Canada. A literature review focusing on lost-time injuries in Registered Nurses (RNs) was conducted using the keywords and terms: lost time injury, workers' compensation, occupational injury, workplace injury, injury, injuries, work, workplace, occupational, nurse, registered nurse, RN, health care, predictors, risk factors, risk, risks, cause, causes, obese, obesity, and body mass index. Data on predictors or factors associated with lost-time injuries in RNs were gathered and organized using Loisel's Work Disability Prevention Management Model and extrapolated upon using existing literature surrounding obesity in the Canadian workplace.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.222-228
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• 2017

The aims of the demand response market are stabilization of the power supply and improving of the reliability of the power system. The various applications of the energy storage system (ESS) in the railway systems are studied and implemented to raise the energy efficiency. It is one of the most important how to determine the obligation reduction capacity (ORC) in participation to the demand response market because it has an influence on the profit extremely. In this paper, when participating to the demand response market with demands in the urban railway, we calculated the available ORC and economically evaluated ESS based on the real load data.

• Journal of the Korean Institute of Rural Architecture
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• v.22 no.4
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• pp.51-62
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• 2020

Article 6 of the Landscape Act stipulates that "the Minister of Land, Infrastructure and Transport establishes a basic landscape policy plan every five years in order to create a beautiful and pleasant landscape, discover and support excellent landscapes." This is a comprehensive plan for landscape policy, a national plan that presents basic directions and strategies for the formation and continuous management of excellent national landscape. The basic landscape policy plan is a plan established every five years. It is an action plan that establishes a medium-term strategy for landscape policy and proposes specific implementation plans. Landscape-related policies are established in various fields, such as the central and local governments, and the private sector, and are based on mutual cooperation. Local cities voluntarily establish basic landscape plans, but in terms of integration, there are few cases in connection with urban regeneration. However, when the existing city establishes the basic landscape plan, the effect will be doubled if a new city is comprehensively constructed in relation to urban regeneration from the overall aspect of the city. Therefore, this study aims to provide data so that a master landscape plan can be established by analyzing and synthesizing the problems of the existing city centering on Jeonju, a representative local city.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.333-340
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• 2022

Recently, the use of large-capacity secondary batteries for electric vehicles is rapidly increasing, and accordingly, the demand for technologies and equipment for battery reliability evaluation is increasing significantly. The existing short circuit test equipment for evaluating the stability of the existing secondary battery consists of relays, MCs, and switches, so when a large current is energized during a short circuit, contact fusion failures occur frequently, resulting in high equipment maintenance and repair costs. There was a disadvantage that repeated testing was impossible. In this paper, we developed an electronic short circuit test device that realizes stable switching operation when a large-capacity power semiconductor switch is energized with a large current, and applied smart ICT technology to this electronic short circuit stability test system to achieve high speed and high precision through communication with the master. It is expected that the inspection history management system based on data measurement, database format and user interface will be utilized as essential inspection process equipment.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.173-180
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• 2022

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

• 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.