• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.407-415
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• 2009

In this research, to identify the degree of preference and participation in leisure sports, over 19-year-old adults living in the capital region were collected as subjects, and among them, 250 persons were chosen by purposive sampling method. Using SPSSWIN 16.0, I analyzed the collected data by reliability analysis, paired sample t-test, PPA(preference performance analysis) method. The conclusion is the following. First, the first quadrant showed wind surfing, skins-cuba, water-ski, and yacht, and as a result of this, it is necessary to improve the degree of participation in this field, the second quadrant showed golf, racketball snow board, ski, fishing, and climbing, and these field need to specific attention to maintain continuous attention, for they have a great deal of attendance, the third quadrant, showed water sleigh, cart, balloon, sky-diving, orienteering, hang gliding, model plane, and survival game. As the degree of preference and participation in these sports is low, it is recommended that more effort should be made for these sports. Finally, in the forth quadrant, there are tracking, mountain bike, inline skates, rafting, and cycling. As it's the participation is high, while that of preference is low, it is important to maintain the policy of the participation in leisure sports. Sencondly, the degree of preference and participation in leisure sport activicties acording to socio demographic characteristics differentiate.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.420-424
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• 2009

In this research, to identify the degree of preference and participation in leisure sports, over 19-year-old adults living in the capital region were collected as subjects, and among them, 250 persons were chosen by sampling. Using SPSSWIN 16.0, I analyzed the collected data by Reliability Analysis, Paired Sample T-Test, and IP A method. The conclusion is the following. First, the first quadrant showed skins-cuba, water-ski, wind surfing, and clay-pigeon shooting, and as a result of this, it is necessary to improve the degree of participation in this field. Secondly, the second quadrant showed golf, snow board, ski, fishing, and climbing, and these field need to specific attention to maintain continuous attention, for they have a great deal of attendance. Thirdly, in the third quadrant, there are water sleigh, cart, balloon, sky-diving, orienteering, hang gliding, model plane, and survival game. As the degree of preference and participation in these sports is low, it is recommended that more effort should be made for these sports. Finally, in the forth quadrant, there are tracking, mountain bike, inline skates, rafting, and cycling. As it's the participation is high, while that of preference is low, it is important to maintain the policy of the participation in leisure sports. In conclusion, for the sports which is preferred that is difficult to join right now, most of all, it is necessary that we should pay attention to and invest the social infrastructure in which main items cannot be joined now.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.4
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• pp.51-59
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• 2019

This study aims to examine the design and construction methods of movable Pavilions. Through the literature analysis, the setting up of the construction background, location and direction, size and composition, materials and construction methods were analyzed. The results are as follows; First, the movable pavilion is designed to enjoy a wide range of views. It was a creation that reflected the way in which the ideal life was pursued based on the experience of enjoying scenery rather than owning one's own house and running a pavilion. Second, the formation of movable pavilion was intended to enjoy the scenery by season without restrictions on time and place. It can also relieve the hassle of having to move tools to enjoy the wind every time. Third, the movable pavilion faces to a place with good scenery and determines its position and direction. Most of them were built on a small scale and divided the space for viewing the scenery, playing GO(Baduk), writing poems, and playing musical instruments. Also, wood was used mainly. To reduce the load, roofs and walls were constructed with light materials such as bamboo, straw, thick sheet of oil, and cotton cloth. The construction method was mainly used by the method of fastening for easy coupling and dismantling. When a building was constructed on the upper part of a ship or cart, the wooden structure of a regular pavilion was constructed. Fourth, when comparing the design and construction characteristics of ordinary pavilion and movable pavilion, the movable pavilion is easy to see for contrast purposes, so there is no limit to setting the location and direction. Instead, more stringent systems and techniques were called for, because as mobility forces should be considered, structurally measures to withstand loads, and they should satisfy their function and form as pavilion.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.249-258
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• 2004

오늘날 활발하게 이루어지고 있는 유비쿼터스 컴퓨팅 관련 기술 연구는 사용자가 시간과 장소에 구애받지 않고 네트워크에 접근해 다양한 컴퓨터 관련 서비스를 제공 받을 수 있는 방법에 초점을 맞추고 있다. 이 처럼 시간과 공간의 한계를 뛰어 넘은 네트워크로의 자유로운 접근은 일상 생활의 패러다임을 바꾸어 놓게 될 것이다. 유비쿼터스 컴퓨팅 기술을 통해 가장 큰 변화가 일어나는 분야는 일반 가정환경에서 일어나는 인텔리전트 홈 네트워크 (Intelligent Home Network) 라고 할 수 있다. 집에 들어오면, 자동으로 문을 열어주고, 불을 켜주며, 놓쳤던 TV 프로그램을 자동으로 녹화해 놓았다가 원하는 시간에 보여주고, 적당한 시간에 목욕물을 미리 받아준다. 또한 집밖으로 나가기 전, 일기예보에 따라 우산을 챙겨주고, 일정을 확인시켜주며 입고 나갈 옷을 골라줄 수도 있다. 이 모든 일들이 유비쿼터스 컴퓨팅 기술이 가져올 인텔리전트 홈 네트워크의 모습이다. 그러나, 모든 사용자에게 효과적인 서비스를 제공하기 위해서는 홈 네트워크 상의 자원 관리에서 일어날 수 있는 에이전트들간의 자원 접근 권한 충돌을 효율적으로 방지할 수 있는 기술이 필요하다. 유비쿼터스 컴퓨팅 환경에서 자원관리 특성은 점유의 연속성, 자원 사이의 연관성, 그리고 자원과 사용자 사 사이의 연계성의 3 가지 특성을 지니고 있다. 본 논문에서는 유비쿼터스 컴퓨팅 환경에서 일어날 수 있는 자원 충돌 상황을 효율적으로 처리하기 위한 자원 협상 방법을 제안한다. 본 방법은 자원 관리 특성을 바탕으로 시간논리에 기반을 둔 자원 선점과 분배 규칙으로 구성된다.트 시스템은 b-Cart를 기반으로 할 것으로 예측할 수 있다.타났다. 또한, 스네이크의 초기 제어점을 얼굴은 44개, 눈은 16개, 입은 24개로 지정하여 MER추출에 성공한 영상에 대해 스네이크 알고리즘을 수행한 결과, 추출된 영역의 오차율은 각각 2.2%, 2.6%, 2.5%로 나타났다.해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data b

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.181-188
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• 2014

The objective of this study was to analyze the microbiological hazards of Astragalus membranaceus Bunge on the post-harvest processing. Samples from processing equipments (cleaner, water, cart, table, tray and packaging machine), personal hygiene (hand) and harvested crops (before washing, after washing, after sorting, and after drying) were collected from four farms (A, B, C, and D) located in Chungchengbuk-do, Korea. The samples were analyzed for sanitary indication bacteria and pathogenic bacteria. First, total aerobic bacteria and coliform in processing facilities were detected at the levels of 0.93~4.86 and 0.33~2.28 log CFU/$100cm^2$ and/mL respectively. In particular, microbial contamination in hand (5.43~6.11 and 2.52~4.12 log CFU/Hand) showed higher than processing equipments. Among the pathogenic bacteria, Bacillus cereus was detected at the levels of 0.33~2.41 log CFU/$100cm^2$, 1.48~3.27 log CFU/Hand and 0.67~3.65 log CFU/g in equipments, hands, and plants and Staphylococcus aureus were detected in cleaner, table, hand and harvested crops (before washing and after sorting) by qualitative test. Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella spp. were not detected. These results indicated that personal hygiene and processing equipments should be managed to reduce the microbial contamination of A. membranaceus Bunge. Therefore, management system such as good agricultural practices (GAP) criteria is needed for hygienic agricultural products.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.9
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• pp.1447-1456
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• 2014

The purpose of this study was to compare perception of school foodservice facilities and utilities in dietitians and school nutrition teachers in the Gyeongnam area between schools that improved foodservice facilities and utilities versus those who did not. From July 23 to Aug 31, 2012, 391 questionnaires were distributed, and 289 questionnaires were obtained. A total of 275 questionnaires were used for the final analysis, excluding improper ones. The results of this study were as follows. First, among 275 schools, 90 schools (32.7%) improved school foodservice facilities and utilities while 175 schools (67.3%) did not. Second, schools with improved facilities had a more well-equipped receiving room (P<0.01), preparation room (P<0.001), dishwashing room (P<0.001), storage room for supplies (P<0.001), rest-room for school foodservice employees (P<0.05), locker room (P<0.01), shower room (P<0.001), laundry room (P<0.001), boiler room (P<0.05), and room for serving cart (P<0.05) than schools with no improvement. Third, total perception score of school foodservice facilities area from schools with improved facilities (1.71) was significantly higher than that (1.60) of school without improvement (P<0.001). Fourth, total satisfaction (3.32) of school foodservice facilities and utilities in school with improved facilities was significantly higher than that (2.62) of schools without improvement (P<0.01). Fifth, schools with improved facilities had a better equipped of floor (P<0.05), entrance (P<0.001), drain (P<0.001), water supply (P<0.01), lighting (P<0.001), hand washing (P<0.001), foodservice management room (P<0.001), locker room (P<0.001), rest-room and shower room (P<0.001), and preparation room (P<0.001) than schools without improvement. However, there was no significant difference in terms of walls and ceilings, windows, ventilation, and storage. In conclusion, school foodservice facilities and utilities improvement should conducted as soon as possible.