• Survey Research
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• v.6 no.2
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• pp.75-101
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• 2005

This study introduces and analyzes the data from Directory of Korean NGOs, which was published in 1997 and again in 200, to conduct a quantitative research on the growth of civic organization in South Korea. This paper focus on the information on membership size and founding year which are essential indicators for the growth of organizations. Missing rates on those two indicators are checked to evaluate the quality of data. We examine the changes in membership size between the two time periods, 1996 and 1999. It shows that there is a considerable decrease in the membership size for civic and advocary organizations that are oriented to national issues. It suggests the competition among the organizations over limited resources, which is consistent with an assumption of ecological theory of organization on non-linear growth pattern. Using founding year data from 1945 to 1996, we estimate pseudo growth curves of civic organizations based on logistic growth curve model to discuss different growth patterns of organizations across areas of activities.

• Earthquakes and Structures
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• v.12 no.1
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• pp.119-133
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• 2017

Several theoretical and analytical formulations for the prediction of shear strength in reinforced concrete (RC) beam-to-column joints have been recently developed. Some of these predictive models are included in the most recent seismic codes and currently used in practical design. On the other hand, the influence of the stiffness and strength degradations in RC joints on the seismic performance of RC framed buildings has been only marginally studied, and it is generally neglected in practice-oriented seismic analysis. To investigate such influence, this paper proposes a numerical description for representing the cyclic response of RC exterior joints. This is then used in nonlinear numerical simulations of RC frames subjected to earthquake loading. According to the proposed strategy, RC joints are modelled using nonlinear rotational spring elements with strength and stiffness degradations and limited ductility under cyclic loading. The proposed joint model has been firstly calibrated against the results from experimental tests on 12 RC exterior joints. Subsequently, nonlinear static and dynamic analyses have been carried out on two-, three- and four-storey RC frames, which represent realistic existing structures designed according to old standards. The numerical results confirm that the global seismic response of the analysed RC frames is strongly affected by the hysteretic damage in the beam-to-column joints, which determines the failure mode of the frames. This highlights that neglecting the effects of joints damage may potentially lead to non-conservative seismic assessment of existing RC framed structures.

• Journal of Preventive Medicine and Public Health
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• v.33 no.4
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• pp.469-476
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• 2000

Objectives : To analyze differences in health behaviors among the social strata in Korea by using the 1995 National Health and Health Behavior Survey Data. Methods : Study Participants numbered 2,352 men and 1,016 women aged between 15-64 years old, with housewives, students and non-waged family workers excluded. Health behaviors in this study were defined according to the recommendations of the Alameda 7 study. The measure of health behaviors was based on the Health Practices Index(HPI; 0-5 range, with the exclusion of snacking between meals and regularly eating breakfast) developed by the Alameda County research. The significance of the relationship between social strata and HPI was assessed by considering the adjusted means from the multi-variate model. Results : For men, incidence rates of never having smoked, no/moderate use of alcohol, regular exercise, and regular 7-8 hours sleep per night were higher in the upper social strate. Meanwhile, for women, incidence rates of never having smoked, no/moderate use of alcohol, appropriate weight, regular exercise, and regular 7-8 hours sleep per night were higher in the upper strata. HPI varied significantly among social strata in both sexes (p<0.001), a result which held true when adjusted for age, education, income, social insurance type, marital status and region. Conclusions : Health behaviors assessed by Health Practices Index(HPI) varied significantly among social strata for both sexes. Therefore, the existing gap in health behaviors among social strata can be corrected more effectively by target oriented health promotional activities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7A
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• pp.649-658
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• 2006

Recently, Software Development was applied to new-approach methods as a various form : client-server system and web-programing, object-orient concept, distributed development with a network environments. On the other hand, it be concerned about the distributed development technology and increasing of object-oriented methodology. These technology is spread out the software quality and improve of software production, reduction of the software develop working. Futures, we considered about the distributed software development technique with a many workstation. In this paper, we discussed optimal release problem based on a stochastic differential equation model for the distributed Software development environments. In the past, the software reliability applied to quality a rough guess with a software development process and approach by the estimation of reliability for a test progress. But, in this paper, we decided to optimal release times two method: first, SRGM with an error counting model in fault detection phase by NHPP. Second, fault detection is change of continuous random variable by SDE(stochastic differential equation). Here, we decide to optimal release time as a minimum cost form the detected failure data and debugging fault data during the system test phase and operational phase. Especially, we discussed to limitation of reliability considering of total software cost probability distribution.

• Journal of Wetlands Research
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• v.22 no.3
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• pp.187-193
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• 2020

The increase in the area of impervious water due to the recent abnormal weather conditions and rapid urbanization led to a decrease in the amount of low current, resulting in an increase in the amount of surface runoff. Increased surface runoff is causing erosion, destruction of underwater ecosystems, human and property damage in urban areas due to flooding of urban river. The damage has been increasing in Korea recently due to localized heavy rains, typhoons and floods. As a countermeasure, the Busan Metropolitan Government will proceed with the creation of the Eco-Delta City waterfront zone in Busan with the aim of creating a future-oriented waterfront city from 2012 to 2023. Therefore, the current urban river conditions and precipitation data were collected by utilizing SWMM developed by the Environment Protection Agency, and the target basin was selected to simulate flood damage. Measures to reduce flood damage in various cases were proposed using simulated data. It is a method to establish a disaster prevention plan for each case by establishing scenario for measures to reduce flood damage. Considering structural and non-structural measures by performing an analysis of the drainage door with a 30-year frequency of 80 minutes duration, the expansion effect of the drainage pump station is considered to be greater than that of the expansion of the drainage door, and 8 scenarios and corresponding alternatives were planned in combination with the pre-excluding method, which is a non-structural disaster prevention measure. As a result of the evaluation of each alternative, it was determined that 100㎥/s of the pump station expansion and the pre-excluding EL.(-)1.5m were the best alternatives.

• Asia Marketing Journal
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• v.14 no.2
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• pp.157-176
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• 2012

Brand switching data frequently used in market structure analysis is adequate to analyze non- durable goods, because it can capture competition between specific two brands. But brand switching data sometimes can not be used to analyze goods like automobiles having long term duration because one of main assumptions that consumer preference toward brand attributes is not changed against time can be violated. Therefore a new type of data which can precisely capture competition among durable goods is needed. Another problem of using brand switching data collected from actual purchase behavior is short of explanation why consumers consider different set of brands. Considering above problems, main purpose of this study is to analyze market structure for durable goods with consideration set. The author uses exploratory approach and latent class clustering to identify market structure based on heterogeneous consideration set among consumers. Then the relationship between some factors and consideration set formation is analyzed. Some benefits and two demographic variables - age and income - are selected as factors based on consumer behavior theory. The author analyzed USA automotive market with top 11 brands using exploratory approach and latent class clustering. 2,500 respondents are randomly selected from the total sample and used for analysis. Six models concerning market structure are established to test. Model 1 means non-structured market and model 6 means market structure composed of six sub-markets. It is exploratory approach because any hypothetical market structure is not defined. The result showed that model 1 is insufficient to fit data. It implies that USA automotive market is a structured market. Model 3 with three market structures is significant and identified as the optimal market structure in USA automotive market. Three sub markets are named as USA brands, Asian Brands, and European Brands. And it implies that country of origin effect may exist in USA automotive market. Comparison between modal classification by derived market structures and probabilistic classification by research model was conducted to test how model 3 can correctly classify respondents. The model classify 97% of respondents exactly. The result of this study is different from those of previous research. Previous research used confirmatory approach. Car type and price were chosen as criteria for market structuring and car type-price structure was revealed as the optimal structure for USA automotive market. But this research used exploratory approach without hypothetical market structures. It is not concluded yet which approach is superior. For confirmatory approach, hypothetical market structures should be established exhaustively, because the optimal market structure is selected among hypothetical structures. On the other hand, exploratory approach has a potential problem that validity for derived optimal market structure is somewhat difficult to verify. There also exist market boundary difference between this research and previous research. While previous research analyzed seven car brands, this research analyzed eleven car brands. Both researches seemed to represent entire car market, because cumulative market shares for analyzed brands exceeds 50%. But market boundary difference might affect the different results. Though both researches showed different results, it is obvious that country of origin effect among brands should be considered as important criteria to analyze USA automotive market structure. This research tried to explain heterogeneity of consideration sets among consumers using benefits and two demographic factors, sex and income. Benefit works as a key variable for consumer decision process, and also works as an important criterion in market segmentation. Three factors - trust/safety, image/fun to drive, and economy - are identified among nine benefit related measure. Then the relationship between market structures and independent variables is analyzed using multinomial regression. Independent variables are three benefit factors and two demographic factors. The result showed that all independent variables can be used to explain why there exist different market structures in USA automotive market. For example, a male consumer who perceives all benefits important and has lower income tends to consider domestic brands more than European brands. And the result also showed benefits, sex, and income have an effect to consideration set formation. Though it is generally perceived that a consumer who has higher income is likely to purchase a high priced car, it is notable that American consumers perceived benefits of domestic brands much positive regardless of income. Male consumers especially showed higher loyalty for domestic brands. Managerial implications of this research are as follow. Though implication may be confined to the USA automotive market, the effect of sex on automotive buying behavior should be analyzed. The automotive market is traditionally conceived as male consumers oriented market. But the proportion of female consumers has grown over the years in the automotive market. It is natural outcome that Volvo and Hyundai motors recently developed new cars which are targeted for women market. Secondly, the model used in this research can be applied easier than that of previous researches. Exploratory approach has many advantages except difficulty to apply for practice, because it tends to accompany with complicated model and to require various types of data. The data needed for the model in this research are a few items such as purchased brands, consideration set, some benefits, and some demographic factors and easy to collect from consumers.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.105-113
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• 2021

Due to the recent coronavirus outbreak, many universities in Korea have started to implement remote education. Accordingly, the Ministry of Education has stated its plans to continuously encourage and maintain remote learning as the future innovation model for education and suggested the need for a diverse range of remote learning models. However, studies on the development of practical learning models have not been carried out actively until now. Particularly, there are not many case studies in the field of design, especially regarding mobile website development. As means to improve the newly designed practice environment, this study therefore proposes the "modoo" project that offers domain creation and online marketing services. As a result of this study, the researcher suggests the use of a mixed(blending) teaching method and realized that the effectiveness of education multiplies when project-based learning and flipped learning is combined appropriately. The research methodology was divided into two big sections, education content and operations, and the effect was evaluated using the course evaluations. The study results confirmed that the applicability will increase given that learning satisfaction levels increased by more than 5% compared to face-to-face learning.

• Korean Chemical Engineering Research
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• v.44 no.1
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• pp.65-72
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• 2006

Recent technical advances in the biorecognition engineering and the microparticle fabrication may enable us to develop the single step purification using magnetic particle, because of its simplicity, efficacy, ease of automation, and process economics. In this study, we used commercial magnetic particles from Seradyn, Inc. (Indianapolis, USA). It was ca. 2.8 micron in diameter, consisted of polystyrene core and magnetite coating, and its surface had carboxyl groups. The model, capture protein was IgG and anti-IgG was used as the ligand molecule. We studied the different surfaces ('nude', ester-activated, and anti-IgG coated) for their biorecognition of IgG. At a high pH condition, we could reduce non-specific binding. Also anti-IgG immobilized magnetic particle could capture IgG more selectively. We attempted 'oriented immobilization' of anti-IgG, in which the polysaccharides moiety near the C-terminus was selectively oxidized and linked to the hydrazine-coated MP, to improve the efficacy of biorecognitive binding. Using this method, the IgG capturing ability was improved by ca. 2 fold. From the binary mixture of the IgG-insulin, IgG could be more selectively captured. In summary, the oriented immobilization of oxidized anti-IgG proved to be as effective as the streptavidin-biotin system and yet simpler and cost-effective. This immobilization method can find its applications in protein biochips and biotargeting.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.