• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.262-268
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• 2017

The energy consumption of buildings is approximately 20.5% of the total energy consumption, and the interest in energy efficiency and low consumption of the building is increasing. Several studies have performed energy analysis and evaluation. Energy analysis and evaluation are effective when applied in the initial design phase. In the initial design phase, however, the energy performance is evaluated using general level information, such as glazing area and surface area. Therefore, the evaluation results of the detailed design stage, which is based on the drawings, including detailed information of the materials and facilities, will be different. Thus far, most studies have reported the analysis and evaluation at the detailed design stage, where detailed information about the materials installed in the building becomes clear. Therefore, it is possible to improve the accuracy of the energy environment analysis if the energy environment information generated during the life cycle of the building can be established and accurate information can be provided in the analysis at the initial design stage using a probability / statistical method. On the other hand, historical data on energy use has not been established in Korea. Therefore, this study performed energy environment analysis to construct the energy environment historical data. As a result of the research, information classification system, information model, and service model for acquiring and providing energy environment information that can be used for building lifecycle information of buildings are presented and used as the basic data. The results can be utilized in the historical data management system so that the reliability of analysis can be improved by supplementing the input information at the initial design stage. If the historical data is stacked, it can be used as learning data in methods, such as probability / statistics or artificial intelligence for energy environment analysis in the initial design stage.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• The Korean Nurse
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• v.34 no.3
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• pp.85-98
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• 1995

The purpose of this study is to provide basic data regarding effective learning opportunities in childbirth education classes. Also analysis of the data indicates the optimum conditions for the welfare and improvements in the promotion of health in childbearing mothers. The results of this study are as follows; 1) The average age of the subjects in this study was 30.6 years and the total number of subjects was 58 pregnant women. The average number of children was one and 84.5% of the subjects were unemployed even though 63.8% of them held over bachelor's degrees. It was found that 22.4% of the subjects were living in an extended family. Also 61.5% of them were living with parents-in-law. The number of pregnancies were calssified as one, two, or three to nine times with the percentages of 58.7%, 22.4% and 18.9%, respectively. Further, 72.4% of the subjects had no abortion experience and 15.5% had one aborion experience. While 89.7% of the subjects planned to feed their babies with breastmilk, mixed feeding were used by only 22.4% of the sample. These data were collected at about 6 months after delivery. Thus one can see that a low rate of breastfeeding was common. 2) The length of one period of childbirth education is four weeks. It was found that 36.2% of the subjects participated in childbirth education only once, where as 13.8% participated four times and 19% of the subjects participated in this class more than four times. pregnant at least once. Further, 75.9% of the participants were participated in this education through their own will. Their motivation for participation developed through information, advertisement and posters which contained information on childbirth education. Those with unplanned pregnancies 92.9% participated after a suggestion by the nurses. The number of participants in terms of percentage according to the childbirth education contents can be classified as following. The most active participation was shown in preparation of delivery(77.6%), postpartrm management(56.9%) fetal development(37.6%) and physiology of pregnancy(17.2%). It was found that 75.9% of the subjects were willing to participate again if they were given a chance. The reason can be summarized as following: The content of the education is very helpful(47.7%). Scientific knowledge can be obtained through this program(20.5%). Participation helps in achieving psychological stability(9.1%). Participation enables one to establish a friendly relationship with other participants(6.8%) of the sample. 24.1% of the participants did not want to participate again. The reasons can be as following: They do not want another baby(42.9%). The first paricipation in childbirth education gave enough knowledge about childbirth(21.4%). Another reason for not want to participate again was because they had a cesarean birth(14.3%). Only 7.1% of them responded with a negative view. A response that they do not need childbirth education after their operation can be traced back to the general belief that childbirth education is the place where one prepares for natural birth through the Lamaze breathing technique. Of the subjects, 91.4% suggested that this program could be recommended to other childbearing mothers, because this program gave educational content along with psychological stability for childbearing women. Of the subjects 41.4% did not see any efforts towards the welfare of the baby, where as 88.2% did. Among the subjects 58.6% made some effort to eliminate the discomfort of labor by breathing and imagination and breathing and walking. Further 41.7% of the 24 subjects did not do anything toward the welfare of the baby, because they did have a cesarean section so that they didn't have a chance even though they had been educated about childbirth. Also 33.3% of the subjects did not do anything toward the welfare of the baby, because they lacked a willingness. After leaving the hospital, only 75.9% of the subjects did some exercises. The subjects who tried participate this program with their husband accounted for 20.7% of the sample. Interviewing with the subjects solved some of the uneasiness and. fear of delivery, increased self-confidence in parenting and active coping in the delivery process.

• The Journal of Korean Society for School & Community Health Education
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• v.1 no.1
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• pp.11-43
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• 2000

The purpose of this study was, through analyzing the previous researches, to grasp the present status of environment of school building(ESB), research the sundry records of each element and, through comparative analysis of the standard of ESB in Korea, the United States, and Japan, select the normative standard of ESB, to clarify the point at issue presented in Regulation of Construction & facility Management for Elementary and and Secondary School in Korea, and to suggest an alternative preliminary standard of ESB. To carry out a research for this purpose, these were required: 1. to investigate the existing present status of ESB, 2. to make a comparative analysis of the standard of ESB in each country, 3. to suggest the normative standard of preliminary standard of ESB, 4. to analyze the controversial points of the standard of ESB in Korea, 5. to suggest an alternative preliminary standard of ESB. The conclusions were as follows: 1. Putting, through analyzing the previous researches, the existing present status of ESB together, it seemed that lighting environment, indoor air environment and noise environment were all in poor conditions. 2. In the result of a comparative analysis of the standard of ESB in Korea, Japan and the United States, in Korea the factors of each lighting and indoor air environment were not presented properly, in Japan, in lighting environment aspect, the standard on natural lighting and the factors on brightness were not presented., and in the USA the essential factors of each environment were throughly presented. In the comparison of the standards on each factor, Korea showed that the standard level presented was less properly prescribed than those of the USA and Japan but it also showed that the standard levels prescribed in the USA and in Japan were mostly similar to the standard levels in records investigated. 3. With the result of the normative standard selection on School Builiding environment factor of prescribed in this study, the controversial points of the standard of ESB in Korea were analyzed and the result was utilized to suggest new preliminary standard of ESB. 4. As the result of the analysis of the controversial points of the standard of ESB in Korea, it was found that the standard of ESB in Korea should be established on a basis of School Health Act and be concretely presented in School Health Regulation and School Health Rule. The factors of each environment was improperly presented in the existing standard of ESB in Korea. Moreover the standard of them was inferior to that of the records investigated and those of in the USA and in Japan and it also showed that the standard of it in Korea was improper to maintain Comfortable Learning Environment. 5. A suggested preliminary standard of ESB acquired through above study as follows: 1) In this study a new kind of preliminary standard of ESB is divided into lighting environment, indoor air environment, noise environment, odor environment and for above classification, reasonable factor and standard should be established and the controling way on each standard and countermeasures against it should be considered. 2) In lighting environment, the factors of natural lighting are divided into daylight rate, brightness, glare. In the standard on each factor, daylight rate should secure 5% of a mean daylight rate and 2% of a minimum daylight rate, brightness ratio of maximum illumination to minimum illumination should be under 10:1, and in glare there should not be an occurrence factor from a reflector outside of the classroom. And the factors of unnatural lighting are illumination, brightness, and glare. In the standard on each factor, illumination should be 750 lux or more, brightness ratio should be under 3 to 1, and glare should not occur. And Optimal reflection rate(%) of Colors and Facilities of Classroom which influences lighting environment should be considered. 3) In indoor air environment factors, thermal factors are divided into (1) room temperature, (2) relative humidity, (3) room air movement, (4) radiation heat, and harmful gases (5) CO, (6) $CO_2$ that are proceeded from using the heating fuel such as oval briquettes, firewood, charcoal being used in most of the classroom, and finally (7) dust. In the standard on each factor, the next are necessary; room temperature: $16^{\circ}C{\sim}26^{\circ}C$(summer : $E.T18.9{\sim}23.8^{\circ}C$, winter: $E.T16.7{\sim}21.7^{\circ}C$), relative humidity: $30{\sim}80%$, room air movement: under 0.5m/sec, radiation heat: under $5^{\circ}C$ gap between dry-bulb temperature and wet-bulb temperature, below 1000 ppm of ca and below 10ppm of $CO_2$, dust: below 0.10 $mg/m^3$ of Volume of dust in indoor air, and ventilation standard($CO_2$) for purification of indoor air : once/6 min.(about 7 times/40 min.) in an airtight classroom. 4) In the standard on noise environment, noise level should be under 40 dB(A) and the noise measuring way and the countermeasures against it should be considered. 5) In the standard on odor environment, odor level under Physical Method should be under 2 degrees, and the inspecting way and the countermeasures against it should be considered.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.334-367
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• 2017

The ICT industry has been the main driver of Korea's economy with international competitiveness and is expected to be the growth engine that will revitalize the currently depressed economy. A broad range of different perspectives and opinions on the industry exist in Korea and overseas. Some of these are stereotypes, not all of which are based on objective evidence. Stereotypes refer to widely-held fixed opinions on a specific group and do not necessarily have negative connotations. However, they should not be viewed lightly because they can substantially affect decision-making process. In this regard, this study sought to review the stereotypes of ICT industry and identify objective and relative stereotypes. In the study, a decision-tree analysis was conducted on a survey result of 3,300 small and medium-sized enterprises (SMEs) in order to identify Korean ICT companies' characteristics that distinguish them from other technology companies. The decision-tree analysis, a data mining process based on machine learning, took a total of 291 variables into account in 10 subjects such as: corporate business in general, technology development activities as well as organization and people in technology development. Identifying the variables that distinguish ICT companies from other technology companies with the decision-tree analysis, the study then came up with a list of objective stereotypes of ICT companies. The findings from the stereotypes of Korean ICT companies are as follows. First, the companies are in need of technology policies that help R&D planning and market penetration. Second, policies must better support the companies working to sell new products or explore new business. Third, the companies need policies that support secure protection of development outcomes and proper management of IP rights. Fourth, the administrative procedures related to governmental support for ICT companies' R&D projects must be simplified. It is hoped that the outcome of this study will provide meaningful guidance in establishment, implementation and evaluation of technology policies for ICT SMEs, particularly to policymakers or researchers in relevant government agencies who determine R&D policies for ICT SMEs.

• Journal of dental hygiene science
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• v.6 no.1
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• pp.11-17
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• 2006

The purpose of this study was to help prevent the turnover of competent dental hygienists in a bid to boost the efficiency of personnel management for dental health care workers and provide higher-quality oral health services. After relevant literature and data were reviewed, a survey was conducted on dental hygienists, who worked at dental institutes, for approximately four months from September to December 2004 to identify what affected their turnover. The findings of the study were as below: 1. Regarding turnover experience, 39.7 percent of the dental hygienists investigated had such an experience. As to turnover frequency, those who took up another employment once made up the largest group(28.2%), followed by twice(8.0%) and three times(2.9%). The most dominant turnover reason was working conditions(66.7%), followed by seeking being hired by larger institutes(36.2%), pay(21.7%), relationship with dentists(11.6%) and commuting distance(11.6%). 2. As for their hope for turnover, 82.8 percent hoped to take up another employment, and working conditions were cited as the most common reason(44.4%), followed by pay(33.3%), commuting distance(18.1%), marriage(13.2%), health/use of leisure time(11.8%), and commuting time(10.4%). 3. Concerning preference for future workplace, 38.5 percent, the largest group, wanted to work at public health clinics. As to a preferred term of working as dental hygienists, 50.0 percent, the greatest group, hoped to serve as dental hygienists until they are financially secure. 34.5 percent, the second largest group, intended to keep working until they reach the age limit. In regard to their responsibility for family economy, 47.7 percent, the greatest percentage, shouldered the partial responsibility for that, and 31.6 percent assumed no responsibility. 4. As to their intention to quit working as dental hygienists, 61.5 percent were willing to do that, and marriage(29.0%) was singled out as the most frequent reason, followed by working conditions(27.1%), child birth(22.4%), health/housework(18.7%), pay(15.9%) and learning/use of free time(15.0%).

• Journal of Yeungnam Medical Science
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• v.2 no.1
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• pp.103-111
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• 1985

Childhood epilepsy which has high prevalence rate and inception rate is one of the commonest problem encountered in pediatrician. In contrast with epilepsy of adult, in childhood epilepsy, more variable and varying manifestations are found because the factors of age, growth and development exert their influences in the manifestations and the courses of childhood epilepsy. Moreover epileptic children have associated problems such as physical and mental handicaps, psychologicaldisorders and learning disability. For these reasons pediatrician who deals with epileptic children experiences difficulties in making diagnosis and managing them. In order to improve understanding and management of childhood epilepsy, authors reviewed 103 cases of epileptic patients seen at pediatric department of Yeungnam University Hospital retrospectively. The patients were classified according to the type of epileptic seizure. Suspected causes of epilepsy, associated conditions of epileptic patients, age incidence and the findings of brain CT were reviewed. Large numbers of epileptic patients (61.2%) developed their first seizures under the age of 5. The most frequent type of epileptic seizure was generalized ionic-clonic, tonic, clonic seizure (49.5%), followed by simple partial seizure with secondary generalization (17.5%), simple partial seizure (7.8%), a typical absence (5.8%) and unclassified seizure (5.8%). In 83.5% of patients, we could not find specific cause of it, but in 16.5% of cases, history of neonatal hypoxia (4.9%), meningitis (3.9%), prematurity (1.9%), small for gestational age (1.0%), CO poisoning (1.0%), encephalopathy (1.0%), DPT vaccination (1.0%), cerebrovascular accident (1.0%) and neonatal jaundice (1.0%) were found, 30 cases of patients had associated diseases such as mental retardation, hyperactivity, delayed motor milestones or their combinations. The major abnormal findings of brain CT performed in 42 cases were cortical atrophy, cerebral infarction, hydrocephalus and brain swelling. This review stressed better designed classification of epilepsy is needed and with promotion of medical care, prevention of epilepsy is possible in some cases. Also it is stressed that childhood epilepsy requires multidisplinary therapy and brain CT is helpful in the evaluation of epilepsy with limitation in therapeutic aspects.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.