• Survey Research
• /
• v.10 no.2
• /
• pp.115-136
• /
• 2009

This paper examines the past tendencies of survey study on military organizations and explores the directions and alternatives of research study based on one of the main military characteristics of rank structure. Military survey study has limitations in confirming the credibility of the research results and in supporting implications on the diverse subjects due to the constraining research environment in the military. This study deduced survey designs and analytic methods fit for the research objective upon multidimensionally grasping the characteristics of the hierarchical rank structure which influences the survey process aid its result crucially. It proposes that the future direction of military survey study is to elaborate research method and multidimensionalize analytic methodology. More specifically. this paper insists that it is necessary to design method and process of the survey delicately and to develop analytic methodology which can analyze diverse factors.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.1
• /
• pp.35-44
• /
• 2020

The purpose of this study is to elaborate the request for proposal (RFP) for the localization parts development support project of core parts carried out by the Defense Agency for Technology and Quality. The RFP is the most important document throughout the localization parts project, including project announcement and developer selection, design and test of the development product, final evaluation, and standardization of the project. However, if the RFP is not established at the beginning of the project, there is an increased risk of business failure due to frequent changes by various reasons. In this study, we recognized the necessity of elaboration of RFP and applied the AHP method for quantitative elaboration. Eight requirements of the RFP related to the mechanical/electrical performance of localized development products and three elaboration methods for each requirement were designed in a hierarchical structure, and each weight was calculated by applying the 5-point scale AHP method. The AHP survey was conducted with 20 developers participating in the localization parts project, and the consistency ratio of the AHP survey result was less than 0.1. The elaboration method with the highest value among the calculated weights is classified, and the analysis results and future research directions of the elaboration method are presented.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.323-325
• /
• 2017

최근 지능형 지속 위협(APT)은 명확한 공격 대상과 정교한 프로그램을 사용하여 치밀하게 공격하는 사회공학적 공격 기법을 사용함으로 상업용 탐지기술의 지속적인 발전과 개발에도 빠르게 증가되고 있다. 기존의 탐지 기법은 알려진 악성코드에 대하여는 효과적으로 대응 가능하나 아무런 정보가 없는 제로데이 공격 등의 악성코드는 탐지하기 어렵다. 특히 최근의 악성코드들은 빠르게 변종을 만들어냄으로 기존의 탐지 기법으로는 한계가 있다. 따라서 본 연구에서는 악성코드에 대한 경로 및 유형, 공격 방법 등을 분석하고 이를 탐지하고 분석하는 선행 기술들 조사하여 DAST 기반의 콘텐츠재구성을 통한 무해화 기술을 제안하였다.

• Journal of the Economic Geographical Society of Korea
• /
• v.22 no.2
• /
• pp.196-213
• /
• 2019

This paper attempts to analyze the contribution of different socioeconomic factors such as income, age, gender, household composition, education and employment status etc. to the difference between the Capital and Non-Capital Regions in the net wealth inequality of household in Korea. To this end, a two-stage Oaxaca-Blinder type decomposition is employed regarding the regional gap in the inequality of net wealth based upon the Recentered Influence Function of the Gini index for 'the 2018 Household Finance and Living Conditions Survey.' Despite the shortcomings of the survey data on wealth, the findings reveal that regional differences in income, marriage status (divorce), job type (agriculture, forestry and fishery related, and technical and assembly), family type (multi-cultural) variables deepen the regional gap in the net-wealth inequality, but employment status (full-time), job type (administrative and specialized, and service sales), household size variables mitigate the gap, and that regional differences in life cycles play an offsetting role.

• KOMUNHWA
• /
• no.58
• /
• pp.25-84
• /
• 2001

This study investigated about the Panchuk Earth Wall that is the Hardened-Heaped Earth Wall, which was surveyed in the South Korea area. The Panchuk Earth Wall was classified into the 2 types depending on the existence and nonexistence of Seok-yeol(石列), t

• Proceedings of the Korean Association for Survey Research Conference
• /
• 2003.06a
• /
• pp.140-160
• /
• 2003

The purpose of this study is to identify and evaluate the competitiveness of ports in ASEAN(Association of Southeast Asian Nations), which plays a leading role in basing the hub of international logistics strategies as a countermeasure in changes of international logistics environments. This region represents most severe competition among Mega hub ports in the world in terms of container cargo throughput at the onset of the 21st century. The research method in this study accounted for over lapping between attributes, and introduced the HFP method that can perform mathematical operations. The scope of this study was strictly confined to the ports of ASEAN, which cover the top 100 of 350 container ports that were presented in Containerization International Yearbook 2002 wi th reference to container throughput. The results of this study show Singapore in the number one position. Even compared with major ports in Korea (after getting comparative ratings and applying tile same data and evaluation structure), the number one position still goes to Singapore and then Busan(2) and Manila(2), followed by Port Klang(4), Tanjung Priok(5), Tanjung Perak(6), Bangkok(7), Inchon(8), Laem Chabang(9) and Penang(9). In terms of the main contributions of this study, it is the first empirical study to apply the combined at tributes of detailed and representative attributes into the advanced HFP model which was enhanced by the KJ method to evaluate the port competitiveness in ASEAN. Up-to-now, none have comprehensively conducted researches with sophisticated port methodology that has discussed a variety of changes in port development and terminal transfers of major shipping lines. Moreover, through the comparative evaluation among major ports in Korea and ASEAN, the presentation of comparative competitiveness for Korean ports is a great achievement in this study. In order to reinforce this study, it needs further compensative research, including cost factors which could not be applied to modeling the subject ports by lack of consistently qualified data in ASEAN.

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

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.31-49
• /
• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• Korea journal of population studies
• /
• v.28 no.2
• /
• pp.219-243
• /
• 2005

This paper reviews research outputs in Korea Journal of Population Studies (KJPS) and the changes in their contents during the period of 1977-2004. In the early years of KJPS, various types of outputs were published, but changes have been made into a direction that research articles compose the main context of the journal. As the journal published twice a year, the total number of articles of the journal shows a trend of marked rise. An analysis of the themes of articles shows that the trend of research in Korean demography has changed hand in hand with transformations of the Korean society. The traditional topics such as population policy, fertility, mortality, and migration were popular before. Labor force, nuptiality, family, aging, regional studies are found to be more popular themes of research recently. Reflecting academic and social demands, KJPS has a broaden range of authors, such as professor, researcher, a government employee, post-doc, and graduate students. The articles written by those working in universities have increased continuously, while research outputs of research institutes have decreased recently. The data for analysis used in articles are varied from but concentrated in some materials - census, vital statistics and various survey data. In early years of KJPS, relatively simple techniques of analysis were adopted in the majority of articles. However, more sophisticated techniques including applied regression analysis, logistic analysis and analysis of survival ratio turn out to be more popular recently. Finally, several suggestions for the future research are presented in this paper.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.12 no.4
• /
• pp.307-316
• /
• 2010

Complex terrain refers to irregular surface properties of the earth that influence gradients in climate, lateral transfer of materials, landscape distribution in soils properties, habitat selection of organisms, and via human preferences, the patterning in development of land use. Complex terrain of mountainous areas represents ca. 20% of the Earth's terrestrial surface; and such regions provide fresh water to at least half of humankind. Most major river systems originate in such terrain, and their resources are often associated with socio-economic competition and political disputes. The goals of the TERRECO-IRTG focus on building a bridge between ecosystem understanding in complex terrain and spatial assessments of ecosystem performance with respect to derived ecosystem services. More specifically, a coordinated assessment framework will be developed from landscape to regional scale applications to quantify trade-offs and will be applied to determine how shifts in climate and land use in complex terrain influence naturally derived ecosystem services. Within the scope of TERRECO, the abiotic and biotic studies of water yield and quality, production and biodiversity, soil processing of materials and trace gas emissions in complex terrain are merged. There is a need to quantitatively understand 1) the ecosystem services derived in regions of complex terrain, 2) the process regulation occurred to maintain those services, and 3) the sensitivities defining thresholds critical in stability of these systems. The TERRECO-IRTG is dedicated to joint study of ecosystems in complex terrain from landscape to regional scales. Our objectives are to reveal the spatial patterns in driving variables of essential ecosystem processes involved in ecosystem services of complex terrain region and hence, to evaluate the resulting ecosystem services, and further to provide new tools for understanding and managing such areas.