• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.75-100
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• 2018

Mobile devices have become an important channel for news content usage in our daily life. However, online news content readers' resistance to online news monetization is more serious than other digital content businesses, such as webtoons, music sources, videos, and games. Since major portal sites distribute online news content free of charge to increase their traffics, customers have been accustomed to free news content; hence this makes online news providers more difficult to switch their policies on business models (i.e., monetization policy). As a result, most online news providers are highly dependent on the advertising business model, which can lead to increasing number of false, exaggerated, or sensational advertisements inside the news website to maximize their advertising revenue. To reduce this advertising dependencies, many online news providers had attempted to switch their 'free' readers to 'paid' users, but most of them failed. However, recently, some online news media have been successfully applying the Pay-What-You-Want (PWYW) payment model, which allows readers to voluntarily pay fees for their favorite news content. These successful cases shed some lights to the managers of online news content provider regarding that the PWYW model can serve as an alternative business model. In this study, therefore, we collected 379 online news articles from Ohmynews.com that has been successfully employing the PWYW model, and analyzed the comparative importance of systematic attributes of online news content on readers' voluntary payment. More specifically, we derived the six systematic attributes (i.e., Type of Article Title, Image Stimulation, Article Readability, Article Type, Dominant Emotion, and Article-Image Similarity) and three or four levels within each attribute based on previous studies. Then, we conducted content analysis to measure five attributes except Article Readability attribute, measured by Flesch readability score. Before conducting main content analysis, the face reliabilities of chosen attributes were measured by three doctoral level researchers with 37 sample articles, and inter-coder reliabilities of the three coders were verified. Then, the main content analysis was conducted for two months from March 2017 with 379 online news articles. All 379 articles were reviewed by the same three coders, and 65 articles that showed inconsistency among coders were excluded before employing conjoint analysis. Finally, we examined the comparative importance of those six systematic attributes (Study 1), and levels within each of the six attributes (Study 2) through conjoint analysis with 314 online news articles. From the results of conjoint analysis, we found that Article Readability, Article-Image Similarity, and Type of Article Title are the most significant factors affecting online news readers' voluntary payment. First, it can be interpreted that if the level of readability of an online news article is in line with the readers' level of readership, the readers will voluntarily pay more. Second, the similarity between the content of the article and the image within it enables the readers to increase the information acceptance and to transmit the message of the article more effectively. Third, readers expect that the article title would reveal the content of the article, and the expectation influences the understanding and satisfaction of the article. Therefore, it is necessary to write an article with an appropriate readability level, and use images and title well matched with the content to make readers voluntarily pay more. We also examined the comparative importance of levels within each attribute in more details. Based on findings of two studies, two major and nine minor propositions are suggested for future empirical research. This study has academic implications in that it is one of the first studies applying both content analysis and conjoint analysis together to examine readers' voluntary payment behavior, rather than their intention to pay. In addition, online news content creators, providers, and managers could find some practical insights from this research in terms of how they should produce news content to make readers voluntarily pay more for their online news content.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.177-198
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• 2018

Recently, as economic property it has become necessary to acquire and utilize the framework for water resource measurement and performance management as the property of water resources changes to hold "public property". To date, the evaluation of water technology has been carried out by feasibility study analysis or technology assessment based on net present value (NPV) or benefit-to-cost (B/C) effect, however it is not yet systemized in terms of valuation models to objectively assess an economic value of technology-based business to receive diffusion and feedback of research outcomes. Therefore, K-water (known as a government-supported public company in Korea) company feels the necessity to establish a technology valuation framework suitable for technical characteristics of water resources fields in charge and verify an exemplified case applied to the technology. The K-water evaluation technology applied to this study, as a public interest goods, can be used as a tool to measure the value and achievement contributed to society and to manage them. Therefore, by calculating the value in which the subject technology contributed to the entire society as a public resource, we make use of it as a basis information for the advertising medium of performance on the influence effect of the benefits or the necessity of cost input, and then secure the legitimacy for large-scale R&D cost input in terms of the characteristics of public technology. Hence, K-water company, one of the public corporation in Korea which deals with public goods of 'water resources', will be able to establish a commercialization strategy for business operation and prepare for a basis for the performance calculation of input R&D cost. In this study, K-water has developed a web-based technology valuation model for public interest type water resources based on the technology evaluation system that is suitable for the characteristics of a technology in water resources fields. In particular, by utilizing the evaluation methodology of the Institute of Advanced Industrial Science and Technology (AIST) in Japan to match the expense items to the expense accounts based on the related benefit items, we proposed the so-called 'K-water's proprietary model' which involves the 'cost-benefit' approach and the FCF (Free Cash Flow), and ultimately led to build a pipeline on the K-water research performance management system and then verify the practical case of a technology related to "desalination". We analyze the embedded design logic and evaluation process of web-based valuation system that reflects characteristics of water resources technology, reference information and database(D/B)-associated logic for each model to calculate public interest-based and profit-based technology values in technology integrated management system. We review the hybrid evaluation module that reflects the quantitative index of the qualitative evaluation indices reflecting the unique characteristics of water resources and the visualized user-interface (UI) of the actual web-based evaluation, which both are appended for calculating the business value based on financial data to the existing web-based technology valuation systems in other fields. K-water's technology valuation model is evaluated by distinguishing between public-interest type and profitable-type water technology. First, evaluation modules in profit-type technology valuation model are designed based on 'profitability of technology'. For example, the technology inventory K-water holds has a number of profit-oriented technologies such as water treatment membranes. On the other hand, the public interest-type technology valuation is designed to evaluate the public-interest oriented technology such as the dam, which reflects the characteristics of public benefits and costs. In order to examine the appropriateness of the cost-benefit based public utility valuation model (i.e. K-water specific technology valuation model) presented in this study, we applied to practical cases from calculation of benefit-to-cost analysis on water resource technology with 20 years of lifetime. In future we will additionally conduct verifying the K-water public utility-based valuation model by each business model which reflects various business environmental characteristics.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.100-108
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• 2012

The purpose of the study was to evaluate the influence of shading and ventilation on Mean Radiant Temperature(MRT) of the outdoor space at a summer outdoor. The Wind Speed(WS), Air Temperature(AT) and Globe Temperature(GT) were recorded every minute from $1^{st}$ of May to the $30^{th}$ of September 2011 at a height of 1.2m above in four experimental plots with different shading and ventilating conditions, with a measuring system consisting of a vane type anemometer(Barini Design's BDTH), Resistance Temperature Detector(RTD, Pt-100), standard black globe(${\O}$ 150mm) and data acquisition systems(National Instrument's Labview and Compfile Techs' Moacon). To implement four different ventilating and shading conditions, three hexahedral steel frames, and one natural plot were established in the open grass field. Two of the steel frames had a dimension of $3m(W){\times}3m(L){\times}1.5m(H)$ and every vertical side covered with transparent polyethylene film to prevent lateral ventilation(Ventilation Blocking Plot: VP), and an additional shading curtain was applied on the top side of a frame(Shading and Ventilation Blocking Plot: SVP). The third was $1.5m(W){\times}1.5m(L){\times}1.5m(H)$, only the top side of which was covered by the shading curtain without the lateral film(Shading Plot: SP). The last plot was natural condition without any kind of shading and wind blocking material(Natural Open Plot: NP). Based on the 13,262 records of 44 sunny days, the time serial difference of AT and GT for 24 hour were analyzed and compared, and statistical analysis was done based on the 7,172 records of daytime period from 7 A.M. to 8 P.M., while the relation between the MRT and solar radiation and wind speed was analyzed based on the records of the hottest period from 11 A.M. to 4 P.M.. The major findings were as follows: 1. The peak AT was $40.8^{\circ}C$ at VP and $35.6^{\circ}C$ at SP showing the difference about $5^{\circ}C$, but the difference of average AT was very small within${\pm}1^{\circ}C$. 2. The difference of the peak GT was $12^{\circ}C$ showing $52.5^{\circ}C$ at VP and $40.6^{\circ}C$ at SP, while the gap of average GT between the two plots was $6^{\circ}C$. Comparing all four plots including NP and SVP, it can be said that the shading decrease $6^{\circ}C$ GT while the wind blocking increase $3^{\circ}C$ GT. 3. According to the calculated MRT, the shading has a cooling effect in reducing a maximum of $13^{\circ}C$ and average $9^{\circ}C$ MRT, while the wind blocking has heating effect of increasing average $3^{\circ}C$ MRT. In other words, the MRT of the shaded area with natural ventilation could be cooler than the wind blocking the sunny site to about $16^{\circ}C$ MRT maximum. 4. The regression and correlation tests showed that the shading is more important than the ventilation in reducing the MRT, while both of them do an important role in improving the outdoor thermal comfort. In summary, the results of this study showed that the shade is the first and the ventilation is the second important factor in terms of improving outdoor thermal comfort in summer daylight hours. Therefore, it can be apparently said that the more shade by the forest, shading trees etc., the more effective in conditioning the microclimate of an outdoor space reducing the useless or even harmful heat energy for human activities. Furthermore, the delicately designed wind corridor or outdoor ventilation system can improve even the thermal environment of urban area.

• Cartoon and Animation Studies
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• s.50
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• pp.349-382
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• 2018

China is a great civilization which is a combination of various ethnic groups with long history change. As one of these important components of traditional culture, the lucky shape has been going through the ideological upheaval of the history change of China. Up to now, it has become the important parts which can stimulate the emotion of Chinese nation. The lucky shape becomes the basis of the rich traditional culture by long history of the Chinese nation. Even say it is the centre of this traditional culture resource. The lucky shape is a way of expressing the Chinese history and national emotions. It is the important part of people's living habits, emotion, as well as the cultural background. What's more, it has the value of beliefs of Surname totem. Meanwhile, it also has the function of passing on information. The symbol of information finally was created by the being of lucky shape to indicate its conceptual content. There are various kinds of lucky shapes. It will have its limitations when researching all kinds of them professionally. So, here the lucky shape of FISH will be researched. The shape of fish is the first good shape created by the Chinese nation. It is about 6000 years. Its special shape and lucky meaning embody the peculiar inherent culture and intension of the Chinese nation. It's the important component of the Chinese traditional culture. The traditional shape of fish was focused on the continuation of history and the patterns recognition, etc. It seldom indicated the meaning of the shape into the using of the modern design. So by searching the lucky meaning & the way of fish shape, the purpose of the search is to explore the real analysis of value of the fish shape in the modern enterprise identity design. The way of search is through the development of the history, the evolvement and the meaning of lucky of the traditional fish shape to analyse the symbolic meaning and the cultural meaning from all levels in nation, culture, art and life, etc. And by using the huge living example of the enterprise identity design of the traditional shape of the fish to analyse that how it works in positive way by those enterprise which is based on the trust with good image. In the modern Chinese enterprise identity design, the lucky image will be reinterpreted in the modern way. It will be proofed by the national perceptual knowledge of the consumer and the way of enlarge the goodwill of corporate image. It will be the conclusion. The traditional fish shape is the important core of modern design.So this search is taken through the instance of the design of enterprise image of the traditional fish shape to analysis the idea of the majority Chinese people of the traditional luck and the influence of corporation which based on trust and credibility. In modern image design of Chinese corporation, the auspicious sign reappear. The question survey is taken by people through the perceptual knowledge of the consumer and the cognition the enterprise image. According the result, people can speculate the improvement of consumer's recognition and the possibility of development of traditional concept.

• Journal of Nutrition and Health
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• v.42 no.6
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• pp.536-546
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• 2009

This study was done to investigate the effect of stress on appetite and eating habits, and other health-related behaviors. The subjects of this study consisted of 188 males and 224 females in Ulsan area. The results were as follows: When stressed, 56% (n = 231) of the subjects experienced a change in appetite and of these, 32% (n = 132) experienced an increased appetite. Stress-induced eating may be one factor contributing to the development of obesity. There was a gender-specific response to stress in which women are more likely to use food to deal with stress, whereas men are more likely to use alcohol consumption or smoking. It was found that types of stressors were individual (52.9%), social (50.7%), family relations (34.5%), work demands (34.2%) and physical environment (32.3%). Stress-induced symptoms of the subjects were anxiety (38.3%), headache (36.7%) and neck or shoulder aches (36.2%), and females experienced those symptoms more than males. Those older than 50 years had a higher eating habit score and lower stress score compared with younger subjects. There were significant differences between sex, age, occupation, family type, BMI, exercise, sleeping hours and eating habits or stress level. This study may be helpful in advancing findings in this area to better provide health professionals with appropriate counseling tools to improve the health of all individuals.

• Journal of Astronomy and Space Sciences
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• v.20 no.2
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• pp.109-122
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• 2003

To examine the causal relationship between geomagnetic storm and substorm, we investigate the correlation between dispersionless particle injection rate of proton flux observed from geosynchronous satellites, which is known to be a typical indicator of the substorm expansion activity, and Dst index during magnetic storms. We utilize geomagnetic storms occurred during the period of 1996 ~ 2000 and categorize them into three classes in terms of the minimum value of the Dst index ($Dst_{min}$); intense ($-200nT{$\leq$}Dst_{min}{$\leq$}-100nT$), moderate($-100nT{\leq}Dst_{min}{\leq}-50nT$), and small ($-50nT{\leq}Dst_{min}{\leq}-30nT$) -30nT)storms. We use the proton flux of the energy range from 50 keV to 670 keV, the major constituents of the ring current particles, observed from the LANL geosynchronous satellites located within the local time sector from 18:00 MLT to 04:00 MLT. We also examine the flux ratio ($f_{max}/f_{ave}$) to estimate particle energy injection rate into the inner magnetosphere, with $f_{ave}$ and $f_{max}$ being the flux levels during quiet and onset levels, respectively. The total energy injection rate into the inner magnetosphere can not be estimated from particle measurements by one or two satellites. However, the total energy injection rate should be at least proportional to the flux ratio and the injection frequency. Thus we propose a quantity, “total energy injection parameter (TEIP)”, defined by the product of the flux ratio and the injection frequency as an indicator of the injected energy into the inner magnetosphere. To investigate the phase dependence of the substorm contribution to the development of magnetic storm, we examine the correlations during the two intervals, main and recovery phase of storm separately. Several interesting tendencies are noted particularly during the main phase of storm. First, the average particle injection frequency tends to increase with the storm size with the correlation coefficient being 0.83. Second, the flux ratio ($f_{max}/f_{ave}$) tends to be higher during large storms. The correlation coefficient between $Dst_{min}$ and the flux ratio is generally high, for example, 0.74 for the 75~113 keV energy channel. Third, it is also worth mentioning that there is a high correlation between the TEIP and $Dst_{min}$ with the highest coefficient (0.80) being recorded for the energy channel of 75~113 keV, the typical particle energies of the ring current belt. Fourth, the particle injection during the recovery phase tends to make the storms longer. It is particularly the case for intense storms. These characteristics observed during the main phase of the magnetic storm indicate that substorm expansion activity is closely associated with the development of mangetic storm.

• Journal of radiological science and technology
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• v.26 no.2
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• pp.37-42
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• 2003

Purpose : Computed tomographic equipment is essential for diagnosis by means of radiation. With passage of time and development of science computed tomographic was developed time and again and in future examination by means of this equipment is expected to increase. In this connection these authors measured rate of scatter ray generation at front of lead glass for patients within control room of computed tomographic equipment room and outside of entrance door for exit and entrance of patients and attempted to ind out method for minimizing exposure to scatter ray. Material and Method : From November 2001 twenty five units of computed tomographic equipments which were already installed and operation by 13 general hospitals and university hospitals in Seoul were subjected to this study. As condition of photographing those recommended by manufacturer for measuring exposure to sauter ray was use. At the time objects used DALI CT Radiation Dose Test Phantom fot Head (${\oint}16\;cm$ Plexglas) and Phantom for Stomache(${\oint}32\;cm$ Plexglas) were used. For measurement of scatter ray Reader (Radiation Monitor Controller Model 2026) and G-M Survey were used to Survey Meter of Radical Corporation, model $20{\times}5-1800$, Electrometer/Ion Chamber, S/N 21740. Spots for measurement of scatter ray included front of lead glass for patients within control room of computed tomographic equipment room which is place where most of work by gradiographic personnel are carried out and is outside of entrance door for exit and entrance of patients and their guardians and at spot 100 cm off from isocenter at the time of scanning the object. The results : Work environment within computed tomography room which was installed and under operation by each hospital showed considerable difference depending on circumstances of pertinent hospitals and status of scatter ray was as follows. 1) From isocenter of computed tomographic equipment to lead glass for patients within control room average distance was 377 cm. At that time scatter ray showed diverse distribution from spot where no presence was detected to spot where about 100 mR/week was detected. But it met requirement of weekly tolerance $2.58{\times}10^{-5}\;C/kg$(100 mR/week). 2) From isocenter of computed tomographic equipment to outside of entrance door where patients and their guardians exit and enter was 439 cm in average, At that time scatter ray showed diverse distribution from spot where almost no presence was detected to spot with different level but in most of cases it satisfied requirement of weekly tolerance of $2.58{\times}10^{-6}\;C/kg$(100 mR/week). 3) At the time of scanning object amount of scatter ray at spot with 100 cm distance from isocenter showed considerable difference depending on equipments. Conclusion : Use of computed tomographic equipment as one for generation of radiation for diagnosis is increasing daily. Compared to other general X-ray photographing field of diagnosis is very high but there is a high possibility of exposure to radiation and scatter ray. To be free from scatter ray at computed tomographic equipment room even by slight degree it is essential to secure sufficient space and more effort should be exerted for development of variety of skills to enable maximum photographic image at minimum cost.