• Korean Security Journal
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• no.48
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• pp.147-176
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• 2016

Korean Peninsula is surrounded by ocean on three sides. Because of this geographical quality over 97% of export and import volumes are exchange by sea. Foreign ship and international passenger vessels carries foreign tourist and globalization and internationalization increases this trends. Leisure population grows with national income increase and interest of ocean. And accidents and incidents rates are also increases. Korea Coast Guard's jurisdiction area is 4.5 times bigger than our country. The length of coastline is 14,963km including islands. One patrol vessel is responsible for 24,068km and one coast guard substation is responsible for 94km. Efficient patrol activities can not be provided. This research focus on this problem. Analyze the status and trends of maritime crime and suggest efficient patrol activities. To deal with increasing maritime crime rate this study suggest to use geographical profile method which developed early 1900s in USA. This geographical profile analyse the spatial characteristic and mapping this result. With this result potential crime zone can be predicted. One of the result is hot spot management which gives data about habitual crime zone. In Korea National Police Agency adopt this method in 2008 and apply on patrol and crime prevention activity by analysis of different criteria. Korea National Police Agency analyse the crime rate with crime type, crime zone and potential crime zone, and hourly, regionally criteria. Korea Coast Guard need to adopt this method and apply on maritime to make maritime crime map, which shows type of crime with regional, periodical result. With this geographical profiling we can set a Criminal Point which shows the place where the crime often occurs. The Criminal Points are set with the data of numerous rates such as homicide, robbery, burglary, missing, collision which happened in ocean. Set this crime as the major crime and manage the data more thoroughly. I expect to enhance the reinforcement of marine crime using this Criminal Points. Because this points will give us efficient way to prevent the maritime crime by placing the patrol vessel where they needed most.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.1-23
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• 2009

The Survey on the Industrial Research and Development(R&D) is the primary source of information on R&D performed by Korea industrial sector. The results of the survey are used to assess trends in R&D expenditures. Government agencies, corporations, and research organizations use the data to investigate productivity determinants, formulate tax policy, and compare individual company performance with industry averages. Recently, Korea Industrial Technology Association(KOITA) has collected the data by complete enumeration. Koita has, currently, considered sample survey because the number of R&D institutions in industry has been dramatically increased. This study develops survey design for the industrial research and development(R&D) statistics by introducing a sample survey. Companies are divided into 8 groups according to the amount of R&D expenditures and firm size or type. We collect the sample from 24 or 8 sampling strata and compare the results with those of complete enumeration survey. The estimates from 24 sampling strata are not significantly different to the results of complete enumeration survey. We propose the survey design as follows: Companies are divided into 11 groups including the companies of which R&D expenditures are unknown. All large companies are included in the survey and medium and small companies are sampled from 70% and 3%. Simple random sampling (SRS) is applied to the small company partition since they show uniform distribution in R&D expenditures. The independent probability proportionate to size (PPS) sampling procedure may be applied to those companies identified as 'not R&D performers'. When respondents do not provide the requested information, estimates for the missing data are made using imputation algorithms. In the future study, new key variables should be developed in survey questionnaires.

• Journal of Nutrition and Health
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• v.52 no.6
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• pp.581-592
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• 2019

Purpose: This study investigated major dietary patterns among healthy Korean adults using cluster analysis and analyzed the relationship between energy intake and skeletal muscle mass. Methods: This study was conducted using the data from the 2008 ~ 2010 Korea National Health and Nutrition Survey. The data of 7,922 subjects aged 30 years and over, without any missing values, were included in the final analysis. K-means cluster analyses were conducted to identify the dietary patterns of the study subjects, which were based on the energy intake from 21 food groups using a 24-h recall method. The changes in energy intake with each dietary pattern, according to quartiles of skeletal muscle mass, were investigated. Results: Three dietary patterns were identified for both men and women: 'Flour, Animal fat', 'White rice' and 'Healthy mixed diet'. The association between energy intake and skeletal muscle mass for both men and women was significant only in the 'White rice' dietary pattern. In the 'White rice' pattern, the energy intake increased up to > 300 kcal from the lowest to the highest quartile of skeletal muscle mass after adjustment for covariates. Within the 'White rice' pattern, skeletal muscle mass was linearly associated with energy intake in all the age groups in men. Conclusion: Energy intake was significantly associated with changes in skeletal muscle mass only in the 'White rice' pattern. Furthermore, the degree of association between the change in skeletal muscle mass and energy intake differed according to gender. These results indicate that the association between skeletal muscle mass and energy intake may be specific to Korean people who are accustomed to a traditional Korean diet.

• Journal of Nutrition and Health
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• v.51 no.6
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• pp.567-579
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• 2018

Purpose: This study examined the mediation effects of dietary habits, nutrient intake, daily life in the relationship between the working hours of Korean shift workers and metabolic syndrome. Methods: Data were collected from the sixth (2013-2015) Korea National Health and Nutrition Examination Survey (KNHANES). The stochastic regression imputation was used to fill missing data. Statistical analysis was performed in Korean shift workers with metabolic syndrome using the SPSS 24 program for Windows and a structural equation model (SEM) using an analysis of moment structure (AMOS) 21.0 package. Results: The model fitted the data well in terms of the goodness of fit index (GFI) = 0.939, root mean square error of approximation (RMSEA) = 0.025, normed fit index (NFI) = 0.917, Tucker-Lewis index (TLI) = 0.984, comparative fit index (CFI) = 0.987, and adjusted goodness of fit index (AGFI) = 0.915. Specific mediation effect of dietary habits (p = 0.023) was statistically significant in the impact of the working hours of shift workers on nutrient intake, and specific mediation effect of daily life (p = 0.019) was statistically significant in the impact of the working hours of shift workers on metabolic syndrome. On the other hand, the dietary habits, nutrient intake and daily life had no significant multiple mediator effects on the working hours of shift workers with metabolic syndrome. Conclusion: The appropriate model suggests that working hours have direct effect on the daily life, which has the mediation effect on the risk of metabolic syndrome in shift workers.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.2
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• pp.102-109
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• 2019

Purpose: The purpose of this study was to evaluate the accuracy of three types of intraoral scanners and the accuracy of the single abutment and bridge abutment model. Materials and methods: In this study, a single abutment, and a bridge abutment with missing first molar was fabricated and set as the reference model. The reference model was scanned with an industrial three-dimensional scanner and set as reference scan data. The reference model was scanned five times using the three intraoral scanners (CS3600, CS3500, and EZIS PO). This was set as the evaluation scan data. In the three-dimensional analysis (Geomagic control X), the divided abutment region was selected and analyzed to verify the scan accuracy of the abutment. Statistical analysis was performed using SPSS software (${\alpha}=.05$). The accuracy of intraoral scanners was compared using the Kruskal-Wallis test and post-test was performed using the Pairwise test. The accuracy difference between the single abutment model and the bridge abutment model was analyzed by the Mann-Whitney U test. Results: The accuracy according to the intraoral scanner was significantly different (P < .05). The trueness of the single abutment model and the bridge abutment model showed a statistically significant difference and showed better trueness in the single abutment (P < .05). There was no significant difference in the precision (P = .616). Conclusion: As a result of comparing the accuracy of single and bridge abutments, the error of abutment scan increased with increasing scan area, and the accuracy of bridge abutment model was clinically acceptable in three types of intraoral scanners.

• Journal of Korean Home Economics Education Association
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• v.35 no.1
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• pp.1-14
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• 2023

The objective of the study was to examine the associations of dietary carbohydrate and fat intake with the prevalence of metabolic syndrome in Korean women. A cross-sectional study was employed based on data from the Korea National Health and Nutrition Examination (2007-2016). A total of 22,850 women aged 19 to 69 years were studied after excluding responses from pregnant or lactating women and those with missing metabolic values. Dietary intake data were collected with a 24-hour recall method. Dietary carbohydrate and fat intakes were divided into quintiles. After controlling for confounding variables, a multivariable logistic regression and general linear model were used. The findings indicated that HDL cholesterol levels were lower (p for trend<0.01), while triglyceride levels (p for trend=0.04), waist circumference (p for trend<0.01), and systolic blood pressure (p for trend<0.01) were higher among participants in the highest quintile of carbohydrate intake compared to those in the lowest quintile. Participants in the highest quintile of fat intake had lower waist circumference (p for trend=0.02), triglyceride level (p for trend<0.01), and systolic blood pressure (p for trend<0.01), while higher HDL cholesterol level (p for trend<0.01) compared to those in the lowest fat intake quintile. Metabolic syndrome was more likely to be present in the highest quintile of carbohydrates intake than in the lowest quintile (5^th quintile vs. 1^st quintile, OR: 1.32; 95% CI: 1.11 to 1.57). However, metabolic syndrome was less likely to be present in the highest quintile of fat intake than in the lowest quintile (5^th quintile vs. 1^st quintile, OR: 0.73; 95% CI: 0.61 to 0.86). This study revealed that high dietary carbohydrate intake and low dietary fat intake were associated with metabolic syndrome in Korean women.

• Korean Journal of Social Welfare Studies
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• v.41 no.4
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• pp.369-393
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• 2010

This study examines the factors influencing Korean international adoptee's search for their birthparents. Considering that the search for birthparents is general needs for adoptees, Korean government should support their searching activities and, first of all, understand their characteristics. The research model was constructed based on the results of previous studies, and the data set of conducted by ministry of health and welfare was reanalyzed for this study. The subjects of the survey were Korean-born adoptees (who are more than 16 years old) in North America, Europe, and Australia. The research questionnaire was translated to English and French, and the survey was conducted on line. A total of 290 questionnaires were included in the analysis. Since survey was conducted on line, the missing rate of the data was relatively high. So, multiply imputed five data sets were used for analysis. Among the variables included in research model, the age group of adoptees, experience of identity crisis in their life, the first time when they became actively interested in Korean roots, the age at the time of adoption, and the attitudes of adoptive parents toward their search were significantly related to their search for birthparents. Adoptees in the age group of 30~34 had more actively participated in search compared to their reference group (which is the age group of more than 35 years old). The earlier they became actively interested in Korean roots, they tended to be more active in searching activities. Also, the experience of identity crisis in life and the age at the time of adoption were positively related to their search. Although most of adoptive parents have supported their search, the adoptees who reported that they didn't know their adoptive parents' attitude toward search, or their parents deceased had more actively participated in search for their birthparents. Some implications for adoption policy and practice were discussed based on the results of the study.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.2
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• pp.45-57
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• 2024

Objectives: The purpose of this study is to determine the impact of artificial menopause on the diagnosis of thyroid cancer, breast cancer, and cervical cancer, and to provide basic data for cancer prevention and early diagnosis in women. Methods: Analysis was conducted using raw data from the 2011-2020 National Health and Nutrition Examination Survey. Among the 79,262 people surveyed in the 2011-2020 National Health and Nutrition Examination Survey, 10,207 people were selected as the final research subjects, excluding men, those under 18 years old, those over 80 years old, those who did not participate in the health survey, those with missing data, and those who were not in menopause. Among them, 248 people were diagnosed with thyroid cancer (2.7%), 225 people were diagnosed with breast cancer (2.5%), and 143 people were diagnosed with cervical cancer (21.5%). Results: First, there appeared to be differences between the thyroid cancer diagnosed group and the non-diagnosed group depending on educational level, childbirth experience, and menopause type. Second, there appeared to be differences between the breast cancer diagnosis group and the non-diagnosis group depending on educational level, menopause age, pregnancy experience, childbirth experience, subjective health status, and menopause type. Third, there appeared to be differences between the cervical cancer diagnosis group and the non-diagnosis group depending on menopause age, subjective health status, and menopause type. Fourth, compared to natural menopause, in the case of artificial menopause, the diagnosis probability of women increased by 2.010 times for thyroid cancer, 3.872 times for breast cancer, and 14.902 times for cervical cancer. Conclusion: For thyroid cancer, breast cancer, and cervical cancer, the probability of cancer diagnosis increases in the case of artificial menopause compared to natural menopause, so it is considered important to avoid experiencing artificial menopause to prevent cancer.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.