• Korean Security Journal
• /
• no.33
• /
• pp.51-80
• /
• 2012

This study is to summarize main contents which was mentioned by Beomseok Kim' doctoral dissertation. The purpose of this study focuses on presenting the outplacement countermeasure and retention level examination analysis about outplacement competency of special security government official through implement of questionnaire method. The questionnaire for retention level examination including four groups of outplacement competency and twenty subcategories was implemented in the object of six hundered persons relevant to outplacement more than forty age and five grade administration official of special security government officials, who have outplacement experiences as outplacement successors, outplacement losers, and outplacement expectants, in order to achieve this research purpose effectively. The questionnaire examination items are four groups of outplacement competency and twenty subcategories which are the group of knowledge competency & four subcategories including expert knowledge, outplacement knowledge, self comprehension, and organization comprehension, the group of skill competency & nine subcategories including job skill competency, job performance skill, problem-solving skill, reforming skill, communication skill, organization management skill, crisis management skill, career development skill, and human network application skill, the group of attitude-emotion competency & seven subcategories including positive attitude, active attitude, responsibility, professionalism, devoting-sacrificing attitude, affinity, and self-controlling ability, and the group of value-ethics competency & two subcategories including ethical consciousness and morality. The respondents highly regard twenty-two outplacement competency and they consider themselves well-qualified for the subcategories valued over 4.0 such as the professional knowledge, active attitude, responsibility, ethics and morality while they mark the other subcategories below average still need to be improved. Thus, the following is suggestions for successful outplacement. First, individual effort is essential to strengthen their capabilities based on accurate self evaluation, for which the awareness and concept need to be redefined to help them face up to the reality by readjusting career goal to a realistic level. Second, active career development plan to improve shortcoming in terms of outplacement competency is required. Third, it is necessary to establish the infrastructure related to outplacement training such as ON-OFF Line training system and facilities for learning to reinforce user-oriented outplacement training as a regular training course before during after the retirement.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.1-16
• /
• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.5
• /
• pp.293-299
• /
• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.46 no.2
• /
• pp.37-51
• /
• 2018

Recently, urban parks are required to actively participate with residents in order to strengthen social functions and maintain sustainable management. This study analyzed the formation process of volunteer resident groups (Chamsamo) in the neighborhood parks in which local residents can participate in an ongoing basis based on the solidarity of a daily living space. The important factors in the activation of resident activity are derived from 5 aspects including resources, local area, resident group capacity, resident group role, and public support. The results of the study are as follows. 1) Life-friendly resources: It was important to find life-friendly resources such as 'Chamsaem' in the park. The combined resources of continuous human activities provided various benefits to the residents. This has led to stronger attachment and community activities to continue to utilize attractive resources in the park. 2) Sharing Common Daily Spaces and Expansion: As the Chamsamo activities were centered around the neighborhood, the network of activists in the local community expanded. This led to continued resident interest and favorable participation as well as to the regional expansion of Chamsamo activities. 3) Park management as part of everyday life: Park management became a part of everyday life, and pleasant park management was facilitated by utilizing the talents of the residents, who carried out diverse activities and constantly streamlined their hard labor. 4) Chamsamo's Leadership Linking Residents and the Public Sector through Leading Park Management Activities: Chamsamo served as a middle leader in linking the public sector and its users. 5) Role and Support of the Public Sector: In order to be able to sustain the activities of residents, the government's willingness to support the resident-led activities of the park in planning and operating the public sector was required. In the public management system of the park, support for residents' activities such as financing, education, and consulting was necessary.

• Asia pacific journal of information systems
• /
• v.19 no.2
• /
• pp.157-178
• /
• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of Korean Society of Transportation
• /
• v.24 no.2 s.88
• /
• pp.91-100
• /
• 2006

This paper examines the situation and tasks of UK rail privatization, especially focusing on after the Hatfield rail accident. Earlier research which focused on the UK's Privatization had little knowledge of the explanations for recent changes. Moreover they had difficulty making a direct comparison between national rail and the privatized rail. Therefore we aye left without a good explanation which has a comprehensive perspective. I attempt to show the change in the rail privatization Process and its outcome, focusing on after the Hatfield rail accident. This Paper argues that the UK's vail privatization process has a regulatory framework which is too complicated with overlapping responsibilities that brought about inefficiency, increasing costs and a superficial safety regime. Especially the planning of rail and infrastructure maintenance did not come to play an appropriate role. However after 2000, the government took charge of setting the strategy for railways, and the Office of Rail Regulation covered safety performance and cost. explain that these changes present a good opportunity to solve the problem of passing the buck for poor performance. Through the analysis, I find that the passenger rail network is well-suited to deliver long distance business and commuters and that the subsidy from the government is decreasing. However, performance, for example punctuality and reliability. should be improved. Especially the Hatfield rail accident caused a reduction in the satisfaction of passengers. In future. the problems of rising costs and monopoly franchise system should be addressed.

• Journal of Korean Society of Transportation
• /
• v.24 no.2 s.88
• /
• pp.65-78
• /
• 2006

This paper investigates the cost structure ot the Korea and Japan railroad industry with respect to density, scale and scope economies as well as productivity growth rate using a generalized trans)og multiproduct cost function model. The paper then assumes that the Korea and Japan railway companies pi·educe three outputs (incumbent railway passenger-kilometers. Shinkansen passenger-kilometers, ton-kilometers of freight) using four input factors (labor, fuel, maintenance, rolling stock and capital). The specified cost function includes foul other independent variables: track lengths to reflect network effects, two dummies to reflect nation and ownership effects, and time trend as a proxy for technical change. The simultaneous equation system consisting of a cost function and three input share equations is estimated with the Zellner's iterative seemingly unrelated regression. The unbalanced panel data used in the paper, a total of 154 observations. are collected from the annual records of the Korea National Railroad (KNR) for the yews $1977{\sim}2003$, Japan National Railways (JNR) for the years $1977{\sim}1984$. seven Japan Railways (JR's) for the years $1987{\sim}2003$. The findings show that the Korean and Japanese railways exhibit product-specific and overall economies of density but product-specific diseconomies of scale with respect to incumbent railway passenger-kilometers, Shinkansen-kilometers and ton-kilometers. However, the railways experience mild overall economies of scale which result from economies of scope associated with the joint production of incumbent railway/Shinkansen and feight, freight/incumbent railway and Shinkansen except Shinkansen/incumbent railway and freight. In addition, the economies of density and scale in the KNR, JR east, JR central, and JR west companies at the point of the years $1990{\sim}2003$ average is generally analogous to the above results at the point of sample average. There also appear to be economies of ssope associated with the joint Production of the incumbent railway and Shinkansen in JR central but diseconomies of scope in JR East and JR West. The findings also indicate that the productivity growth rate of the privately-owned JR's is larger than that of the government-owned KNR.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.17 no.1
• /
• pp.25-34
• /
• 2015

In this research, we applied a procedure of quality control (QC) to the agro-meteorological data measured at the Suwon weather station of Korea Meteorological Administration (KMA). The QC was conducted through six steps based on the KMA Real-time Quality control system for Meteorological Observation Data (RQMOD) and four steps based on the International Soil Moisture Network (ISMN) QC modules. In addition, we set up our own empirical method to remove erroneous data which could not be filtered by the RQMOD and ISMN methods. After all these QC procedures, a well-refined agro-meteorological dataset was complied at both air and soil temperatures. Our research suggests that soil moisture requires more detailed and reliable grounds to remove doubtful data, especially in winter with its abnormal variations. The raw data and the data after QC are now available at the NCAM website (http://ncam.kr/page/req/agri_weather.php).

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.67-88
• /
• 2019

Webtoon is a Korean-style digital comics platform that distributes comics content produced using the characteristic elements of the Internet in a form that can be consumed online. With the recent rapid growth of the webtoon industry and the exponential increase in the supply of webtoon content, the need for effective webtoon content recommendation measures is growing. Webtoons are digital content products that combine pictorial, literary and digital elements. Therefore, webtoons stimulate consumer sentiment by making readers have fun and engaging and empathizing with the situations in which webtoons are produced. In this context, it can be expected that the sentiment that webtoons evoke to consumers will serve as an important criterion for consumers' choice of webtoons. However, there is a lack of research to improve webtoons' recommendation performance by utilizing consumer sentiment. This study is aimed at developing consumer sentiment pattern maps that can support effective recommendations of webtoon content, focusing on consumer sentiments that have not been fully discussed previously. Metadata and consumer sentiments data were collected for 200 works serviced on the Korean webtoon platform 'Naver Webtoon' to conduct this study. 488 sentiment terms were collected for 127 works, excluding those that did not meet the purpose of the analysis. Next, similar or duplicate terms were combined or abstracted in accordance with the bottom-up approach. As a result, we have built webtoons specialized sentiment-index, which are reduced to a total of 63 emotive adjectives. By performing exploratory factor analysis on the constructed sentiment-index, we have derived three important dimensions for classifying webtoon types. The exploratory factor analysis was performed through the Principal Component Analysis (PCA) using varimax factor rotation. The three dimensions were named 'Immersion', 'Touch' and 'Irritant' respectively. Based on this, K-Means clustering was performed and the entire webtoons were classified into four types. Each type was named 'Snack', 'Drama', 'Irritant', and 'Romance'. For each type of webtoon, we wrote webtoon-sentiment 2-Mode network graphs and looked at the characteristics of the sentiment pattern appearing for each type. In addition, through profiling analysis, we were able to derive meaningful strategic implications for each type of webtoon. First, The 'Snack' cluster is a collection of webtoons that are fast-paced and highly entertaining. Many consumers are interested in these webtoons, but they don't rate them well. Also, consumers mostly use simple expressions of sentiment when talking about these webtoons. Webtoons belonging to 'Snack' are expected to appeal to modern people who want to consume content easily and quickly during short travel time, such as commuting time. Secondly, webtoons belonging to 'Drama' are expected to evoke realistic and everyday sentiments rather than exaggerated and light comic ones. When consumers talk about webtoons belonging to a 'Drama' cluster in online, they are found to express a variety of sentiments. It is appropriate to establish an OSMU(One source multi-use) strategy to extend these webtoons to other content such as movies and TV series. Third, the sentiment pattern map of 'Irritant' shows the sentiments that discourage customer interest by stimulating discomfort. Webtoons that evoke these sentiments are hard to get public attention. Artists should pay attention to these sentiments that cause inconvenience to consumers in creating webtoons. Finally, Webtoons belonging to 'Romance' do not evoke a variety of consumer sentiments, but they are interpreted as touching consumers. They are expected to be consumed as 'healing content' targeted at consumers with high levels of stress or mental fatigue in their lives. The results of this study are meaningful in that it identifies the applicability of consumer sentiment in the areas of recommendation and classification of webtoons, and provides guidelines to help members of webtoons' ecosystem better understand consumers and formulate strategies.

• Journal of Korean Society of Transportation
• /
• v.19 no.1
• /
• pp.101-114
• /
• 2001

Transportation enterprises should maintain constant and qualitative operation. Thus, in short period, transportation enterprises don't change supply in accordance with demand. In the result, transportation enterprises don't reduce operation in spite of management deficit at will. In freight transportation type, less-than-truckload(LTL) has more relation with above transportation feature than truckload(TL) does. Because freight transportation supply of TL is more flexible than that of LTL in correspondence of freight transportation demand. Relating to above mention, it appears that shortage of road and freight terminal of LTL is larger than that of TL. Especially in road and freight terminal comparison, shortage of freight terminal is larger than that of road. Shortage of road is the largest in 1990, and improved after-ward. But shortage of freight terminal is serious lately. So freight terminal needs more expansion than road, and shows better investment condition than road. Freight terminal expansion brings road expansion in LTL, on the contrary, freight terminal expansion substitutes freight terminal for road in TL. In transportation revenue, freight terminal's contribution to LTL is larger than that to TL. However, when we adjust quasi-fixed factor - road and freight terminal - to optimal level in the long run, in TL, diseconomies of scale becomes large, but in LTL, economies of scale becomes large. Consequently, it is necessary for TL to make counterplans to activate management of small size enterprises and owner drivers. And LTL should make use of economies of scale by solving the problem, such as nonprofit route, excess of rental freight handling of office, insufficiency of freight terminal, shortage of driver, and unpreparedness of freight insurance.