• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.221-234
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• 2023

This study was conducted to develop a habitat assessment system for the endangered wildlife II Kirengeshoma koreana Nakai through in-depth interviews with experts based on field surveys and environmental characteristics through spatial data analysis and literature research. Evaluation factors were selected based on the survey results of 31 variables for 23 K. koreana habitats. Afterwards, the importance and evaluation range for each assessment factor were selected. The selection of survey variables, assessment factors, importance of each factor and assessment range was conducted through in-depth interviews with experts at each stage. As a result, the assessment factors and importance were 16% for precipitation of the wettest month, 10% for vegetation zone, 12% for vegetation type, 10% for crown canopy vitality, 14% for tree layer coverage, 13% for drainage grade, 12% for soil depth, and 13% for distance to stream including dry stream. This study provides basic ecological information and a habitat assessment system for K. koreana habitats. Therefore, it can be used as primary data for preparing an endangered wildlife conservation policy, preparing a habitat improvement plan, and selecting an alternative habitat.

• Korean Journal of Environment and Ecology
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• v.31 no.2
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• pp.252-265
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• 2017

This study aims to analyze the landscape ecological characteristics of green spaces within built up area of high density and evaluate the potential applicability of green patches, thereby introducing urban garden for generating green networks in residence areas. To this end, Yeoksam-Dong was selected as the site area since it is classified as both green initiative zone and alienated area of park service in Seoul. First, the current condition of green spaces in Yeoksam-Dong was identified by five categories: Street trees, private garden, public pocket garden, rooftop garden, and park. Then, the landscape index analysis through FRAGSTATS and connectivity assessment via multi-buffer zone analysis were carried out for analyzing the green networks and evaluating the potential value of green space. The results showed that the degree to which green areas in the site were distributed is arranged in the order of street tree, private garden, public pocket garden, park, and rooftop garden. In case of the street trees whose total core area (TCA, $1,618m^2$) is as high as the park's ($1,128m^2$). Private garden has potential for green network in built up area of high density by gardening since the shape of the patches are irregular (ED = 78.1m/ha) and the average distance among the patches is close (ENN=33.9m). Public pocket garden has also potential for gardening according to the result that it was found to be distributed evenly (LPI=5.7%, SHEI=0.9) with exposing external disturbance ($TCA=66m^2$). For the green network, 84% of all the study site is covered by small green network in 50m butter range of connected green area. The effect of green network was expected through gardening in public pocket garden (27%) and street tree (26%). Accordingly, it is encouraged to actively utilize street tree, private gardens, and rooftop gardens and to establish the urban gardens like local-based community gardens in public pocket garden where a variety of activities can be carried out near residential areas. By doing so, green networks can effectively be established in built up area with high density. The results of this study can contribute positively to fostering the creation of various types of urban gardens.

• 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.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.5
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• pp.533-541
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• 2007

The Zienkiewicz-Zhu(Z/Z) error estimate is slightly modified for the hierarchical p-refinement, and is then applied to L-shaped plates subjected to bending to demonstrate its effectiveness. An adaptive procedure in finite element analysis is presented by p-refinement of meshes in conjunction with a posteriori error estimator that is based on the superconvergent patch recovery(SPR) technique. The modified Z/Z error estimate p-refinement is different from the conventional approach because the high order shape functions based on integrals of Legendre polynomials are used to interpolate displacements within an element, on the other hand, the same order of basis function based on Pascal's triangle tree is also used to interpolate recovered stresses. The least-square method is used to fit a polynomial to the stresses computed at the sampling points. The strategy of finding a nearly optimal distribution of polynomial degrees on a fixed finite element mesh is discussed such that a particular element has to be refined automatically to obtain an acceptable level of accuracy by increasing p-levels non-uniformly or selectively. It is noted that the error decreases rapidly with an increase in the number of degrees of freedom and the sequences of p-distributions obtained by the proposed error indicator closely follow the optimal trajectory.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.89-100
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• 2018

The precedent study stage of defense programs is a project stage that is conducted to support the determination of an efficient acquisition method of the weapon system determined by the requirement. In this study, the FTA/FMEA technique was used in the safety analysis process to identify elements to be conducted in the precedent study stage and a methodology for deriving the key review elements through conceptualization and tailoring was suggested. To supplement the key elements derived from the existing research, it is necessary to analyze various events that may arise from key elements. To accomplish this, the HAZOP technique for safety analysis in other industrial fields was used to supplement the results of kdy element derivation. We analyzed and modeled the execution procedure by establishing input/output information and association with the key elements of the precedent study stage derived by linking HAZOP/FTA/FMEA techniques. In addition, performance maturity was evaluated for performance of precedent study, and a risk-based response manual was generated based on inter-working information with key elements with low maturity. Based on the results of this study, it is possible to meet the performance, cost, and schedule of the project implementation through application of the key elements and procedures and the risk management response manual in the precedent study stage of the defense program.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.1
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• pp.41-48
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• 2008

Enhanced machine reliability has dramatically reduced the rate and number of railway accidents but for further reduction human error should be considered together that accounts for about 20% of the accidents. Therefore, the objective of this study was to suggest a new taxonomy of performance shaping factors (PSFs) that could be utilized to identify the causes of a human error associated with railway accidents. Four categories of human factor, task factor, environment factor, and organization factor and 14 sub-categories of physical state, psychological state, knowledge/experience/ability, information/communication, regulation/procedure, specific character of task, infrastructure, device/MMI, working environment, external environment, education, direction/management, system/atmosphere, and welfare/opportunity along with 131 specific factors was suggested by carefully reviewing 8 representative published taxonomy of Casualty Analysis Methodology for Maritime Operations (CASMET), Cognitive Reliability and Error Analysis Method (CREAM), Human Factors Analysis and Classification System (HFACS), Integrated Safety Investigation Methodology (ISIM), Korea-Human Performance Enhancement System (K-HPES), Rail safety and Standards Board (RSSB), $TapRoot^{(R)}$, and Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). Then these were applied to the case of the railway accident occurred between Komo and Kyungsan stations in 2003 for verification. Both cause decision chart and why-because tree were developed and modified to aid the analyst to find causal factors from the suggested taxonomy. The taxonomy was well suited so that eight causes were found to explain the driver's error in the accident. The taxonomy of PSFs suggested in this study could cover from latent factors to direct causes of human errors related with railway accidents with systematic categorization.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.177-212
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• 1993

My research is based on the parser for ill-formed input by Mellish in a paper in ACL 27th meeting Proceedings. 1989. My system is composed of two parsers:WFCP and IFCP. When WFCP fails to give the parse tree for the input sentence, the sentence is identified as ill-formed and is parsed by IFCP for error detection and recovery at the syntactic level. My system is indendent of grammatical rules. It does not take into account semantic ill-formedness. My system uses a grammar composed of 25 context-free rules. My system consistes of two major parsing strategies:top-down expection and bottem-up satisfaction. With top-down expectation. rules are retrieved under the inference condition and expaned by inactive arcs. When doing bottom-up parsing. my parser used two modes:Left-Right parsing and Right-to-Left parsing. My system repairs errors sucessfully when the input contains an omitted word or an unknown word substitued for a valid word. Left- corner and right-corner errors are more easily detected and repaired than ill-formed senteces where the error is in teh middle. The deviance note. with repair details, is kept in new inactive arcs which are generated by the error correction procedure. The implementation of my system is quite different from Mellish's. When rules are invoked. my system invokes all rules with minimal inference. My bottom up parsing strategy uses Left-to-Right mode and Right-to-Left mode. My system is bottom-up-parsing-oriented like the chart parser. Errors are repaired in two ways:using top-down hypothesis, and using Need-Chart which keeps the information of expectation and complection of expanded goals by rules. To reduce the number of top-down cycles. all rules are invoked simultaneously and this invocation information is kept in Need-Chart. This idea will be extended for the implementation of multiple error recovery system.

• Journal of Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.37-57
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• 2006

To support Location Based Services, the technology to store and search locations information of moving objects effectively was needed. And the study about indexes to manage these moving objects effectively has been done. As these indexes for moving objects was not considered for the objects which are moving along constrained networks such as road and railroad, indexes for the moving objects based on constrained networks was proposed. But these kinds of indexes have two problems as following. First, as the indexes for the moving objects based on constrained networks is divided according to time domain, when the places of moving objects from the present to the past are needed, the problem to search past indexes as well as present indexes occurs. Second, in this case, we should construct both present indexes and past indexes, so we have no other choice but to spend space cost and reconstruction cost additionally. This paper proposes A Unified Index for Moving Objects in Constrained Networks to solve these kinds of problems. As this proposed indexes support both present location and past location of moving objects, it can solve the current problems such as when we search present and past location of moving objects, we need a separate processing procedure. And as it consolidated the common parts of current location indexes and past location indexes, we can use less space cost and reconstruction cost than when we maintain indexes separately.

• Advances in pediatric surgery
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• v.16 no.2
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• pp.154-161
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• 2010

Pulmonary sequestration (PS) is a rare congenital malformation of the lower respiratory tract. The anomaly is characterized by absence of communication with the tracheobronchial tree and isolated blood supply from an anomalous systemic vessels. With the utilization of antenatal ultrasound, the diagnosis of asymptomatic neonatal PS has increased. Treatment options include observation, arterial embolization and surgical resection. The aim of the present study is to review the clinical course of PS and to share our experience with thoracoscopic resection. A total of 96 patients with PS were treated at Asan Children's Hospital between 1999 and 2010. The diagnosis of PS was established by CT in the cases managed by observation or embolization, and by tissue pathology in the surgical cases. Medical records and radiographic images were retrospectively reviewed. Thirty-nine patients were managed by embolization and 30 patients by surgery. The remaining 27 patients have been under observation without any procedures. Among 27 observation patients, 1 patient regressed completely and 10 patients were lost to follow up. Of the 39 embolizations patients, 2 had their lesion regress and sepsis was suspected after embolization. In 1 patient, the microcoil migrated to the iliac artery during the embolization procedure, and another patient developed renal abscess caused by renal artery embolization. Among 30 surgical cases, resection by thoracotomy was performed in 27 at the Department of Thoracic Surgery, and thoracoscopic resection in 3 at the Division of Pediatric Sugery. Only one wound complication ocurred. We conclud that surgical excision should be recommended for pulmonary sequestration, whether the sequestration is symptomatic or not because of the risk of infection, the low rate of natural regress, poor compliance, severe complications after embolization, and to exclude other pathology. In summary, thoracoscopic resection of the pulmonary sequestration is feasible, efficacious, safe and cosmetically superior even in neonatal period.