• The Journal of Engineering Geology
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• v.29 no.2
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• pp.85-97
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• 2019

It can be observed that steep slopes ($65^{\circ}$ to $80^{\circ}$) consist of rock masses were kept stable for a long time. In rock-mass slopes with similar ground condition, steeper slopes than 1 : 0.5 ($63^{\circ}$) may be applied if the discontinuities of rock-mass slope are distributed in a direction favorable to the stability of the slope. In making a decision the angle of the slope, if the preliminary rock mass conditions applicable to steep slope are quantitatively setup, they may be used as guidance in design practice. In this study, the above rock mass was defined as a good continuum rock mass and the quantitative setup criterion range was proposed using RMR, SMR and GSI classifications for the purpose of providing engineering standard for good continuum rock mass conditions. The methods of study are as follows. The stable slope at steep slopes ($65^{\circ}$ to $80^{\circ}$) for each rock type was selected as the study area, and RMR, SMR and GSI were classified to reflect the face mapping results. The results were reviewed by applying the calculated shear strength to the stable analysis of the current state of rock mass slope using the Hoek-Brown failure criterion. It is intended to verify the validity of the preliminary criterion as a rock mass condition that remains stable on a steep slope. Based on the analysis and review by the above research method, it was analyzed that a good continuum rock mass slope can be set to Basic RMR ${\geq}50$ (45 in sedimentary rock), GSI and SMR ${\geq}45$. The safety factor of the LEM is between Fs = 14.08 and 67.50 (average 32.9), and the displacement of the FEM is 0.13 to 0.64 mm (average 0.27 mm). This can be seen as a result of quantitative representation and verification of the stability of a good continuum rock mass slope that has been maintained stable for a long period of time with steep slopes ($65^{\circ}$ to $80^{\circ}$). The setup guideline for a good continuum rock mass slope will be able to establish a more detailed setup standard when the data are accumulated, and it is also a further study project. If stable even on steep slopes of 1 : 0.1 to 0.3, the upper limit of steep slopes is 1 : 0.3 with reference to the overseas design standards and report, thus giving the benefit of ensuring economic and eco-friendlyness. Also, the development of excavation technology and plantation technology and various eco-friendly slope design techniques will help overcome psychological anxiety and rapid weathering and relaxation due to steep slope construction.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.21-36
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• 2019

Dead fuel moisture content is a key variable in fire danger rating as it affects fire ignition and behavior. This study evaluates simple regression models estimating the moisture content of standardized 10-h fuel stick (10-h FMC) at three sites with different characteristics(urban and outside/inside the forest). Equilibrium moisture content (EMC) was used as an independent variable, and in-situ measured 10-h FMC was used as a dependent variable and validation data. 10-h FMC spatial distribution maps were created for dates with the most frequent fire occurrence during 2013-2018. Also, 10-h FMC values of the dates were analyzed to investigate under which 10-h FMC condition forest fire is likely to occur. As the results, fitted equations could explain considerable part of the variance in 10-h FMC (62~78%). Compared to the validation data, the models performed well with R2 ranged from 0.53 to 0.68, root mean squared error (RMSE) ranged from 2.52% to 3.43%, and bias ranged from -0.41% to 1.10%. When the 10-h FMC model fitted for one site was applied to the other sites, $R^2$ was maintained as the same while RMSE and bias increased up to 5.13% and 3.68%, respectively. The major deficiency of the 10-h FMC model was that it poorly caught the difference in the drying process after rainfall between 10-h FMC and EMC. From the analysis of 10-h FMC during the dates fire occurred, more than 70% of the fires occurred under a 10-h FMC condition of less than 10.5%. Overall, the present study suggested a simple model estimating 10-h FMC with acceptable performance. Applying the 10-h FMC model to the automatic mountain weather observation system was successfully tested to produce a national-scale 10-h FMC spatial distribution map. This data will be fundamental information for forest fire research, and will support the policy maker.

• Korean Journal of Heritage: History & Science
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• v.55 no.4
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• pp.180-193
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• 2022

The East Nine Royal Tombs is a representative place in the Royal Tombs of Joseon (a World Heritage Site). It consists of 1,289 stone artifacts including 979 related stone structures, 310 stone statues, and objects. Most of the stone structures in the East Nine Royal Tombs are composed of biotite granite, but some tombs are composed of light red granite. As a result of magnetic susceptibility measurement, the average data from Geonwolleung to Mongneung, excluding Hyeolleung, were similar, so it is estimated that stones were obtained from the same quarry. In the case of Sungneung, Sureung, and Gyeongneung, the range of susceptibility measurement is widely distributed. It assumed that the newly produced stones were mixed in the moving and construction process. Also, stones might be gathered from different quarries. As a result of a conservation status investigation, both the mound member and the ridge stone had the highest damage rate due to peeling and granular decomposition according to surface weathering. In the case of surface discoloration, yellowing and soils were found in the burial mound members. Yellowing, blackening, and soil were identified in the ridge stone structures. Bio-degradation is the major factor of deterioration of the East Nine Royal Tombs and the conservation status of the tombs were detected as grades 4 to 5. It seems that it is easy for the environment of the royal tombs to form soil for the microorganisms and fine conditions for continuous moisture. In the case of structures, they are in relatively good condition. As a result of a comprehensive damage rating for each tomb, the overall condition is good, but the Geonwolleung Royal Tomb and Hyeolleung Tomb, which were created in the early period, had relatively high weathering ratings. Stone objects in East Nine Royal Tombs have lost many pieces and gateway members due to surface deterioration. Also, secondary damage is ongoing. Each damage factor of the stone artifacts of the East Nine Royal Tombs combines to cause various and continuous damages. Therefore, it is necessary to establish regular conservation status data of the stone artifacts for efficient management after processing as well as conservation treatment of the royal tombs, and specific management manuals and systems. This study investigated the conservation status of stone structures in the East Nine Royal Tombs, a World Heritage Site, and systematically classified them to provide priority and necessity for conservation processing. We look forward to establishing a plan for the conservation and management of the East Nine Royal Tombs with this database in the future.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• Journal of radiological science and technology
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• v.8 no.2
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• pp.21-28
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• 1985

We serveyed the actual condition of business of the departments of radiology of 45 health conters (except 3) in the area of Seoul, Kyungki and Inchon from March, 1984 to November, 1984. The results are summarized as follows : 1. T.O. of the radiologic technologist is three persons in each health center of Seoul area, and one person in each one of Kyungki and Inchon area. P.O. is 2-5 persons in Seoul area, 1-2 persons in Kyungki or Inchon area. 2. The number of all the radiologic technologists employed now is 75 persons, and among all of them, when analized by position class 7th is 54.7%, class 8th 28.0%, class 9th is 13.3%, and class 6th is 2.7%, and by sex, female is 68.0%, male is 32.0%, by educational background, for the most part, junior college graduates come to 73.3%, by age group 60% of them is in their twenties, 16.0% is in their thirties and forties, 8.0% is in their fifties, and by career after certificate 60% have the career of 1-5 years, 13.3% have the one of 6-7 years or mor than 21 years, and 6.7% have the one of 11-15 years of 16-20 years. 3. All the diagnostic x-ray equipment being kept is 62, and among them flxing equipment is 71.0%, portable equipment is 29.0% and by rating of X-ray equipment, maximum tube current 100 mA is 46.8%, maximum KV 100KVP is 72.6%, the most part. 4. Photofluorographic camera and hood are equipped in every health center. While, as to the radiographic cassettes, $14{\times}14"$ cassetts are equipped in every health center, but cassettes of other sizes are in half of them. 5. Bucky's table is equipped in 11.9% health centers, the automatic processor is in 21.4%, the photofluorographic film changer is 9.5%, the grid is 73.8%, the protective apron is in 88.1%, and the protective glove is in 57.1% health centers. 6. The number of the people who got the x-ray examination for one year (by the year 1989) is the most, 1,000-6,000 in direct radiography of the chest, or 15,0001-45,000 in the health centers of Seoul area, 5,000-20,000 in Kyungki and Inchon area in photofluorography of the chest. Moreover, other radiographies are being taken extremely limitedly in all health centers. 7. In processing types of x-ray film, automatic processing is used in 9 health centers (21.4%), manual tank processing is in 30 (71.4%), and manual tray processing in 3 (7.2%). 8. As for collimation of x-ray exposure field, "continual using restricted by a subject size" has the most part, 78.6% "restricted using at every radiography" has 19%, and the case of "never considered" has 2.4% response. 9. As for the dosimeter used for radiation control, film badge (35.7%) and pocket dosimeter (26.2%) are used, and in 38.1% health centers the dosimeter is not equipped at all. Consideration of the previous radiation exposure is being done in only one health center. 10. Reading of radiographs is mainly depended on the radiologists electively (45.2%) or on the genral practitioners(45.2%).

• Journal of the korean academy of Pediatric Dentistry
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• v.27 no.4
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• pp.505-516
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• 2000

Chloral hydrate is one of the most widely used sedative agents to control the difficult-to-treat young age group in the dental clinic. We are often frustrated to see the patient still awake and cry with agitation even after far more than the normal onset time of Chloral hydrate. In such a case, the patient has to be rescheduled for another sedation visit with different agents and/or routes which greatly disappoints the guardians. This study was designed to test the efficacy of one sedative regimen that can possibly help the clinician complete scheduled treatment without postponement. We have tried sleep induction with mixed gas of Enflurane(2vol%) and $N_2O(50%)-O_2(50%)$ for $60\sim120$ seconds to 35 patients of those who failed to respond properly to the dose(70mg/kg)of oral Chloral hydrate. The Result of this regimen was compare to those of two oral regimen of Chloral hydrate/Hydroxyzine and Chloral hydrate only Analyses of result on vital signs and behavior pattern were performed. The outcome of the study suggest that sleep induction by a short inhalation of low dose of $Enflurane/N_2O-O_2$ provide dentist with suitable condition for the completion of scheduled treatment in the patient who failed to oral Chloral hydrate. Evidence of adverse effect was not detected or reported during and/or after the procedures.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.21 no.2
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• pp.63-71
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• 2010

Objectives : The aim of this study was to investigate the effect of the clinical and demographic variables such as body weight, dosage, family history of attention-deficit hyperactivity disorder (ADHD), and psychiatric co-morbidity on the side-effects of OROS-Methylphenidate (OROS-MPH), and to evaluate the relationship between drug response and side effect severity. Methods : A total of 144 children (ages 6-18) with diagnosed ADHD were treated with OROS-MPH. Children were examined at baseline and after 1, 3, 6, 9, and 12 weeks of each treatment condition. The stimulant drug side effect rating scale (SERS), pulse rate, systolic blood pressure, diastolic blood pressure, and electrocardiogram (ECG) were evaluated to assess side effect profiles. Changes in these parameters from baseline were examined and analyzed. Results : Anorexia (30.95%) and insomnia (13.10%) were the most commonly reported side effects during this study. Insomnia and loss of appetite score increased at one week follow-up, but was sustained or decreased as treatment progressed. Small but significant increases in pulse rate and diastolic blood pressure were observed during treatment ; however, no clinically meaningful changes in ECG parameters were noted during the study. Low body weight, high dosage of OROS-MPH, and family history of ADHD were associated with cardiovascular side effect. In contrast, there was no significant relationship between OROS-MPH treatment response and the severity of side effect and no difference resulted between the responder and non-responder groups with respect to OROS-MPH dosage in the 12 weeks of follow-up. Conclusion : To the best of our knowledge, this study is the first Korean study to investigate comprehensive side effect profiles and their correlates in OROS-MPH treatment for ADHD children. OROS-MPH was well tolerated with no clinically significant side effects during the treatment period. In conclusion, low body weight, high dosage of OROSMPH, and family history of ADHD could be used as predictive factors in increasing pulse rate and blood pressure.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.