• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.05a
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• pp.59-63
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• 2000

At military bases, environmental restoration activities resulting from oil contamination are growing concerns of preventing adverse effects on human health and environments. Its technologies are still under developing stage through some countries such as United States and Germany. This study is focused on developing model for a decision-maker to assist the restoration priority under the situation of limited resources such as budget and time. The Model, named the Base Restoration Priority Decision model(BRP model), is composed of the three factors : oil contaminants receptors, and the potential migration pathways. Each risk rating of factor is combined in the 27 matrix blocks and set immediate, moderate, and delayed action category designated restoration priority. This is categorized to group sites into three degree using the simplest of assessment system. As a result, the model will be able to apply to the effective allocation of resources for the restoration by any decision-maker because the model is easy to understand. Also, the continuous study will have established risk assessment system for the restoration of contaminated military with this study as the starting point.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.231-242
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• 2014

On the Pre-Housing-Sale Systems there are many risks that developers might not fulfill the pre-sale obligations. In korea, in order to protect the people who bought houses from these risk, the Housing Sold Guarantee System was introduced and has been operated. Even though this system if there is accident in the pre-sale warranty business, several problems, such as damages caused to the people who bought the houses, occurs. Therefore, research is needed to Housing Sold Guarantee accident factor. But there are few study about it. This study attempted to analyze influencers on the possibility of the accident. We employ 3,026 data which Korea Housing Guarantee Co., Ltd manages and analyze them empirically, using business characteristics, housing market characteristics, and regional characteristics. Especially this study used to the binary logistic regression model. The results of analysis showed that the accident rate of Housing Sold Guarantee had been effected on the business type, house type, project financing guarantee, operator credit rating, housing market, and regional characteristics.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.649-661
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• 2020

Cut-slope management system (CSMS) has been investigated all slopes on the road of the whole country to evaluate risk rating of each slope. Based on this evaluation, the decision-making for maintenance can be conducted, and this procedure will be helpful to establish a consistent and efficient policy of safe road. CSMS has updated the database of all slopes annually, and this database is constructed based on a basic and detailed investigation. In the database, there are two type of data: first one is an objective data such as slopes' location, height, width, length, and information about underground and bedrock, etc; second one is subjective data, which is decided by experts based on those objective data, e.g., degree of emergency and risk, maintenance solution, etc. The purpose of this study is identifying an data application plan to utilize those CSMS data. For this purpose, logistic regression, which is a basic machine-learning method to construct a prediction model, is performed to predict a judging-type variable (i.e., subjective data) based on objective data. The constructed logistic model shows the accurate prediction, and this model can be used to judge a priority of slopes for detailed investigation. Also, it is anticipated that the prediction model can filter unusual data by comparing with a prediction value.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• The Journal of Internal Korean Medicine
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• v.44 no.4
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• pp.603-620
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• 2023

Objectives: This study reviewed randomized controlled trials (RCTs) investigating the efficacy of herbal medicine on sleep disorders associated with Parkinson's disease and suggests a better research process. Methods: We searched for RCTs for herbal medicine treatments for sleep disorders related to Parkinson's disease on July 31, 2023 using eight databases (PubMed, Embase, the Cochrane library, China National Knowledge Infrastructure [CNKI], the Research Information Service System [RISS], Science ON, the Oriental Medicine Advanced Searching Integrated System [OASIS], and the Korea Citation Index [KCI]). Cochrane's risk of bias tool was used to assess the quality of the RCTs. Results: A total of 16 RCTs met all the inclusion criteria, and in most reports, the treatment group showed a significant improvement in sleep disorders compared to the control group. Total effective rate (TER), Pittsburgh Sleep Quality Index (PSQI), Unified Parkinson's Disease Rating Scale (UPDRS), TCM Symptom Score (TSS), Parkinson's Disease Sleep Scale (PDSS), etc., were used as evaluation indicators. Conclusion: Herbal medicine is a potential treatment for sleep disorders associated with Parkinson's disease. However, the selected RCTs were of poor quality, and it is necessary to perform more systematic studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.285-288
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• 2010

Chinese hackers hack the e-commerce site by bypass South Korea IP to connect to the third country, finance damaging a violation incident that fake account. 7.7.DDoS attack was the case of a hacker attack that paralyzed the country's main site. In this paper, the analysis is about vulnerabilities that breaches by hackers and DDoS attacks. Hacker's attacks and attacks on the sign of correlation analysis is share the risk rating for in real time, Red, Orange, Yellow, Green. Create a blacklist of hackers and real-time attack will be studied security and air conditioning systems that attacks and defend. By studying generate forensic data and confirmed in court as evidence of accountability through IP traceback and detection about packet after Incident, contribute to the national incident response and development of forensic techniques.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• The Journal of the Korean bone and joint tumor society
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• v.2 no.1
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• pp.8-17
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• 1996

The purpose of this study is a comparative evaluation of range motion, especially extension deficit between the group of total patellectomy and that of intact patella, after reconstruction of the patellar tendon in the prosthetic replacement of a proximal tibia. Between 1990 and 1994, 15 patients who had a primary malignancy on proximal tibia were operated on. All patients were evaluated clinically and radiographically. Two patients were excluded because one had a deep infection treated with arthrodesis of the knee and the other was a composite allograft. The mean follow-up of the 13 patients was 27 months(15-47), including 10 osteosarcomas, 1 chondrosarcoma, 1 malignant fibrous histiocytoma and 1 malignant giant cell tumor. Eleven patients had a resection of the proximal tibia and 2 had an extracapsular total knee resection with distal femur. Reconstruction of the defect was done in 8 cases with a custom-made Link Endo-Model Total Rotation Knee Joint Prosthesis, and in 5 with How Medica Modular Resection System (HMRS). We used two methods to reconstruct the ligamentum patellae. Fixation of the patellar tendon to the prosthesis only with suturing and/or stapling(group SS) was done in 7. Transposition of gastrocnemius muscle to enhance fixation and to cover the prosthesis(group TG) was done in 6. Regardless of fixation methods, total patellectomy was done in 5 either to lengthen the patellar tendon or to make primary skin closure easier or for both. In 8 cases, patella was left intact or resurfaced with polyethylene prosthesis. Active extension was measured while the patient was in a sitting position. There is no statistically meaningful difference in terms of extension deficit (Wilcoxon rank test, p=0.8800) between patellectomy group and intact patella group, and between group of fixation only with suturing and that of gastrocnemius transposition. Two cases of extension deficit over 30 degree were seen in group SS and in the group of intact patella. Conclusively, total patellectomy could be an option without increasing the risk of extension deficit when primary skin closure is difficult or patellar tendon is a little bit short to be fixed. There is no rating in the Enneking system of functional evaluation that this finding into consideration.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.1
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• pp.175-204
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• 2018

Recently, the aviation leisure business has been legislated, and related industries have become active base with increasing the light sports aircraft within the legislation system. However, in the light sports aircraft safety problem, it is often mentioned that the flight is in violation of the regulations, the lack of safety consciousness of the operator and lack of ability, and the personal operators have a risk of accident of light aircraft such as insufficient safety management and poor maintenance. At present, the maintenance of light sports aircraft is carried out by the A & P mechanic in accordance with the relevant laws and regulations, but it is difficult to say that it is equipped with qualification and expertise. It is not a legal issue to undertake light sports aircraft maintenance work on the regulation system. However, the problem of reliability and appropriateness is constantly being raised because airplanes, light sports aircraft, and ultra-light vehicle are classified and serviced in a legal method. Although legal and institutional frameworks for light sports aircraft are separated, much of it is stipulated in the aviation law provisions. Light sports aircraft maintenance work also follows the current aircraft maintenance system. In the United States, Europe, and Australia where General Aviation developed, legal and institutional devices related to maintenance of light aircraft were introduced, and specialized maintenance tasks are covered in the light aircraft mechanics system. As a result of analysis of domestic and foreign laws and regulations, it is necessary to introduce the qualification system for maintenance of light aircraft. In advanced aviation countries such as the United States, Europe, and Australia, a light sports aircraft repairman system is installed to perform safety management. This is to cope with changes in the operating environment of the new light sports aircraft. This study does not suggest the need for a light aircraft repairman system. From the viewpoint of the legal system, the examination of the relevant laws and regulations revealed that the supplementary part of the system is necessary. It is also require that the necessity of introduction is raised in comparison with overseas cases. Based on these results, it is necessary to introduce the system into the light aircraft repairman system, and suggestions for how to improve it are suggested.

• Journal of Construction Engineering and Project Management
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• v.8 no.4
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• pp.1-24
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• 2018

In the last decade, the use of Building Information Modeling (BIM) as a new technology has been applied with traditional Computer-aided design implementations in an increasing number of architecture, engineering, and construction projects and applications. Its employment alongside construction management, can be a valuable tool in helping move these activities and projects forward in a more efficient and time-effective manner. The traditional stakeholders, i.e., Owner, A/E and the Contractor are involved in this BIM system that is used in almost every activity of construction projects, such as design, cost estimate and scheduling. This article extracts major features of the application of BIM from perspective of participating BIM components, along with the different phrases, and applies to them a logistic analysis using a fuzzy performance tree, quantifying these phrases to judge the effectiveness of the BIM techniques employed. That is to say, these fuzzy performance trees with fuzzy logic concepts can properly translate the linguistic rating into numeric expressions, and are thus employed in evaluating the influence of BIM applications as a mathematical process. The rotational fuzzy models are used to represent the membership functions of the performance values and their corresponding weights. Illustrations of the use of this fuzzy BIM performance tree are presented in the study for the uninitiated users. The results of these processes are an evaluation of BIM project performance as highly positive. The quantification of the performance ratings for the individual factors is a significant contributor to this assessment, capable of parsing vernacular language into numerical data for a more accurate and precise use in performance analysis. It is hoped that fuzzy performance trees and fuzzy set analysis can be used as a tool for the quality and risk analysis for other construction techniques in the future. Baldwin's rotational models are used to represent the membership functions of the fuzzy sets. Three scenarios are presented using fuzzy MEAN, AND and OR gates from the lowest to intermediate levels of the tree, and fuzzy SUM gate to relate the intermediate level to the top component of the tree, i.e., BIM application final performance. The use of fuzzy MEAN for lower levels and fuzzy SUM gates to reach the top level suggests the most realistic and accurate results. The methodology (fuzzy performance tree) described in this paper is appropriate to implement in today's construction industry when limited objective data is presented and it is heavily relied on experts' subjective judgment.