• Korean Security Journal
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• no.51
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• pp.81-100
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• 2017

Recently, Crime patterns in our society are diversifying as followed on the urbanization of population and the influx of immgrants. Existing murder, kidnap, sexual assault, etc. Especially, the crimes such as school violence, dating violence, domestic violence, violent abuse and even social hatred a crime, motiveless crime are spreading into every phase of national life. Due to the social situation, the sharp increase in demand for personal protection, the scale of private security industry has been constantly expanded. Following this trend, the personal protective regulations in Security Industry Act has been revised several times since the it was enacted in 1995. However, despite the fact that the legal and institutional aspects should have been amended and improved systematically according to the industrial development, the regulations adopted initially adopted has been maintained so far, which have resulted in various problems as they could not coincide with the purpose of private security, being divorced from the reality of private security industry and social changes. Especially, in the case of personal protection service and facility security service, the legal requirements of both services are identical with each other in terms. Such legal systems may cause confusion to security businesses and employees, or the police managing and supervising them, regarding the scope and duties of security services. In order to improve such problems, the regulations of permission requirement that the personal protective regulations in Security Industry Act should be revised system. In this study, relevant personal protection provisions prescribed in the Security Industry Act have been reviewed critically in this paper. And also the regulations were review of those personal protection provisions enacted in security industry Act, so that the improvement plan for the personal protection provisions that are apposite to the cases in this country could be suggested in order to amend the current laws and provide real grounds for the law enforcement.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.544-553
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• 2014

Activity-based models analyze individuals' various daily activities that are identified as a decision-making unit for transportation planning. In other words, it is the model that determines the types of activities according to the social, economic and situational characteristics of the groups with the same activity patterns and predicts individuals' activity time, distance, spatial movement and transportation mode. The activity-based model is a method of estimating more efficient and realistic demand in transportation forecasting because traffic is regarded as a complex decision-making process that an individual and other people participate in. In this paper, we grasp the factors affecting choice behavior of activity pattern and analyze choice behavior of activity pattern based on multi-dimensional characteristic of each person. First, we classify activity types of reviewing the trip chain and activity purpose. Next, we identified preferable activity types using complicated characteristics of main agent of activity. We concluded that choice behavior of activity pattern is dependent on complex characteristics of each agent, and further multi-dimensional characteristics of each person are affected over the whole decision process of activity schedule.

• Korean Journal of Environmental Agriculture
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• v.31 no.4
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• pp.359-367
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• 2012

BACKGROUND: As natural wetlands are decreased by increment of human demand, the importance of rice fields as an alternative habitat for waterbirds is well documented. However, the relationship between waterbirds use and management practice of rice fields has not been fully understood. The present study attempted to understand the changes in temporal abundance of waterbirds and their preference for habitat types in rice fields all year round. METHODS AND RESULTS: Waterbirds census were conducted in rice fields around Asan bay in Korea during April 2009-March 2010 and April 2011-March 2012. In the bird counts, the locations of the observed birds on a 1/2,500 map were recorded along with the local habitat type (paddy, ditch, levee, road). Thirty five species of waterbirds recorded in the rice fields during the survey period and three major groups (shorebirds, herons, and waterfowls) were characterized according to season and micro-habitat use. Shorebirds visited a flooded paddy for feeding during their spring migration season (April-May), and herons used the rice field as feeding sites during their breeding periods (April-October). Most waterfowls were observed in a dry paddy to feed a fallen rice seed and stubs during the winter season (September-March). Waterbird groups selectively used micro-habitats in rice field. Shorebirds and waterfowls mainly preferred at rice paddies, while herons were attracted to most habitat types. CONCLUSION(S): Rice fields supported various waterbirds all year round and waterbird communities using the rice fields were dramatically changed according to seasonal change of rice field condition.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.248-257
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• 2023

Because of various societal changes, such as the recent improvement in income levels and extension of the flexible work system, the demand for forest recreation activities and their use patterns are undergoing a change. Accordingly, it is necessary to identify the characteristics of each type through the segmentation of the overall forest recreation and welfare markets and to plan differentiated policies for each market type. This study classifies the forest recreation and welfare activities according to four types of users (i.e., passive usage type, ordinary type, active lover type, and indifferent type) using the Latent Class Analysis and examines their demographic and socioeconomic characteristics to explain the differences between the groups. Three policy implications were derived from the results obtained: 1) the group experiencing forest recreation welfare is subdivided; 2) the socioeconomic characteristics that distinguish the groups undertaking forest recreation activities were identified; and 3) the policy targets and characteristics that can increase the experience of forest recreation welfare were identified. This study is insightful as it suggests differentiated policies for each group and proposes policy measures to move to the desirable group.

• Korean Journal of Heritage: History & Science
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• v.56 no.2
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• pp.62-78
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• 2023

The act of burning incense originated from Buddhist rituals and customs, and gradually formed its own culture. In the Goryeo Dynasty, in addition to religious and national rituals, incense came to be enjoyed more generally and widely. In particular, Goryeo literati enjoyed the elegant lifestyle of staying home and burning incense. This was part of a regional culture shared across East Asia. Such incense burning applied the same methods as were used during the same period in China. In collections of writings from the Goryeo Dynasty, it can be seen that incense methods such as gyeok-hwa-hoon-hyang (隔火熏香) and jeon-hyang (篆香) were used. A particular method of incense influenced the size and shape of the incense burner utilized. Small incense burners suitable for simple everyday incense were used, such as the hyangwan (香垸), a cup (wine glass)-shaped burner. White porcelain incense burners from Song were discovered in Gaegyeong, and celadon incense burners from Goryeo were made in the same shape. This phenomenon shows that there was great demand for ceramic incense burners in Goryeo in the 12th and 13th centuries. During this period, incense burners that imitated metalware were produced, and some applied the techniques and patterns of Goryeo celadon. The Goryeo Dynasty-era incense burner was basically a necessity for use in various rituals, but gradually came to be widely used also by individuals.

• Korean Journal of Microbiology
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• v.49 no.1
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• pp.30-37
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• 2013

This study was carried out to assess the spatial and tidal effects on the water quality in the lower reaches of Taewha River, Ulsan, Korea and to understand the environmental factors affecting winter algal bloom in the river. From May, 2010 to January, 2011, water samples were collected at five locations (New Samho Bridge, Old Samho Bridge, Mungjung Stream, Taewha Bridge, and Mungchon Bridge) along the river at high and low tides of spring tide. We measured environmental parameters including salinity, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), chlorophyll a (Chl a) and various nutrient concentrations. Salinity increased towards the downstream direction. Average values of Chl a concentrations ranged $10-26mg/m^3$ at high tide and $11-53mg/m^3$ at low tide depending on sampling locations. It was noteworthy that there were strong increases in Chl a concentrations during the November 21 to December 22 sampling period especially at the Taewha Bridge. At the location, Chl a concentrations were measured as $138-296mg/m^3$ for the period; Rhodomonas lacustris of class Cryptophyceae was the dominant algal species. Chl a concentrations at the Taewha Bridge were positively correlated with such parameters as salinity, BOD, DO, COD, pH, and T-N, and negatively correlated with temperature and $NO_3{^-}$-N. On the other hand, at the Mungchon Bridge the highest concentration of Chl a was $55mg/m^3$ on August 25, and Chl a concentrations were positively correlated with $NH_3$-N, T-N, $PO_4{^{3-}}$-P, T-P, and heterotrophic plate counts. The results suggested that water quality in the lower Taewha River fluctuated a lot with the sampling locations and the patterns of algal blooms were different between Taewha Bridge and Mungchon Bridge sampling locations.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• 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 Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.223-252
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• 2008

In this study, we attempt to propose a choice-based diffusion model with switching cost, which can be used to forecast the dynamic substitution and competition among previous and new products at both individual-level and aggregate level, especially when market data for new products is insufficient. Additionally, we apply the proposed model to the empirical case of substitution and competition among Analog Cable TV that represents previous fixed charged broadcasting service and Digital Cable TV and Internet Protocol TV (IPTV) that are new ones, verify the validities of our proposed model, and finally derive related empirical implications. For empirical application, we obtained data from survey conducted as follows. Survey was administered by Dongseo Research to 1,000 adults aging from 20 to 60 living in Seoul, Korea, in May of 2007, under the title of 'Demand analysis of next generation fixed interactive broadcasting services'. Conjoint survey modified as follows, was used. First, as the traditional approach in conjoint analysis, we extracted 16 hypothetical alternative cards from the orthogonal design using important attributes and levels of next generation interactive broadcasting services which were determined by previous literature review and experts' comments. Again, we divided 16 conjoint cards into 4 groups, and thus composed 4 choice sets with 4 alternatives each. Therefore, each respondent faces 4 different hypothetical choice situations. In addition to this, we added two ways of modification. First, we asked the respondents to include the status-quo broadcasting services they subscribe to, as another alternative in each choice set. As a result, respondents choose the most preferred alternative among 5 alternatives consisting of 1 alternative with current subscription and 4 hypothetical alternatives in 4 choice sets. Modification of traditional conjoint survey in this way enabled us to estimate the factors related to switching cost or switching threshold in addition to the effects of attributes. Also, by using both revealed preference data(1 alternative with current subscription) and stated preference data (4 hypothetical alternatives), additional advantages in terms of the estimation properties and more conservative and realistic forecast, can be achieved. Second, we asked the respondents to choose the most preferred alternative while considering their expected adoption timing or switching timing. Respondents are asked to report their expected adoption or switching timing among 14 half-year points after the introduction of next generation broadcasting services. As a result, for each respondent, 14 observations with 5 alternatives for each period, are obtained, which results in panel-type data. Finally, this panel-type data consisting of $4{\ast}14{\ast}1000=56000$observations is used for estimation of the individual-level consumer adoption model. From the results obtained by empirical application, in case of forecasting the demand of new products without considering existence of previous product(s) and(or) switching cost factors, it is found that overestimated speed of diffusion at introductory stage or distorted predictions can be obtained, and as such, validities of our proposed model in which both existence of previous products and switching cost factors are properly considered, are verified. Also, it is found that proposed model can produce flexible patterns of market evolution depending on the degree of the effects of consumer preferences for the attributes of the alternatives on individual-level state transition, rather than following S-shaped curve assumed a priori. Empirically, it is found that in various scenarios with diverse combinations of prices, IPTV is more likely to take advantageous positions over Digital Cable TV in obtaining subscribers. Meanwhile, despite inferiorities in many technological attributes, Analog Cable TV, which is regarded as previous product in our analysis, is likely to be substituted by new services gradually rather than abruptly thanks to the advantage in low service charge and existence of high switching cost in fixed charged broadcasting service market.