• Journal of Legislation Research
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• no.44
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• pp.343-386
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• 2013

"Financial ADR" system in South Korea can be represented by so-called "Financial Dispute Resolution System", in which Financial Supervisory Service (FSS) and Financial Dispute Resolution Committee are the principal actors in operation of the system, and this is discussed as an "Administrative Financial ADR System". The system has over 10-year history since it was introduced in around 1999. Nonetheless, it was not until when financial consumer protection began to be highlighted after the 2008 financial crisis that Financial ADR system actually started to draw attention in Korea. This was because interest has been rising in "Alternative Dispute Resolution (ADR)" as an institutional measure to protect financial consumers damaged via financial transactions. However, the current discussion on the domestic Financial ADR system shows an aspect that it is confined to who is to be a principal actor for the operation of Financial ADR institution with main regards to reorganization of supervisory system. This article aims to embody these facts in an institutional model by recognizing them as a problem and analyzing the features of the Financial ADR system, thereby clarifying problems of the system and presenting the direction of improvement. The Korean Financial ADR system can be judged as "administrative model integrated model consensual model quasi-judicial model non-prepositive Internal Dispute Resolution (IDR) model". However, at the same time, it is confronted with a task to overcome the two problems; the system is not equipped with institutional basis for securing its validity in spite of the adopted quasi-judicial effect model; and a burden of operating an integrated ADR system is considerable. From this perspective, the article suggests improvement plans for security of validity in the current system and for expansion of industry-control ADR system, in particular, a system of prepositive IDR model. Amongst them, it suggests further plans for securing the validity of the system as follows; promotion to expand the number of internal persons and to differentiate mediation procedures and effect; a plan to keep a financial institution from filing a lawsuit before an agreement recommendation or a mediation proposal is advised; and a plan to grant suspension of extinctive prescription as well as that of procedures of the lawsuit.

• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.605-619
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• 2019

The objective of this study was to analyze eutrophication characteristics, empirical model analysis, and variation of water quality according to monsoon intensity in Hapcheon Reservoir for 16 years from 2002 to 2017. Long-term annual water quality analysis showed that Hapcheon Reservoir was in a meso-nutrition to eutrophic condition, and the eutrophic state intensified after the summer monsoon. Annual rainfall volume (high vs. low rainfall) and the seasonal intensity in each year were the key factors that regulate the long-term water quality variation provided that there is no significant change of the point- and non-point source in the watershed. Dry years and wet years showed significant differences in the concentrations of TP, TN, BOD, and conductivity, indicating that precipitation had the most direct influence on nutrients and organic matter dynamics. Nutrient indicators (TP, TN), organic pollution indicators (BOD, COD), total suspended solids, and chlorophyll-a (Chl-a), which was an estimator of primary productivity, had significant positive relations (p<0.05) with precipitation. The Chl-a concentration, which is an indicator of green algae, was highly correlated with TP, TN, and BOD, which differed from other lakes that showed the lower Chl-a concentration when nutrients increased excessively. Empirical model analysis of log-transformed TN, TP, and Chl-a indicated that the Chl-a concentration was linearly regulated by phosphorus concentration, but not by nitrogen concentration. Spatial regression analysis of the riverine, transition, and lacustrine zones of $log_{10}TN$, $log_{10}TP$, and $log_{10}CHL$ showed that TN and Chl-a had significant relations (p<0.005) while TN and Chl-a had p > 0.05, indicating that phosphorus had a key role in the algal growth. Moreover, the higher correlation of both $log_{10}TP$ and $log_{10}TN$ to $log_{10}CHL$ in the riverine zone than the lacustrine zone indicated that there was little impact of inorganic suspended solids on the light limitation in the riverine zone.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.105-111
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• 2021

Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.367-385
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• 2022

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• International Commerce and Information Review
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• v.10 no.4
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• pp.457-491
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• 2008

As stage equipment export markets along with special lighting service lack the attraction for already globally established businesses, such markets can be viewed as an advantageous opportunity for SMEs as in general. In reality, global businesses tend to focus on large construction projects and this indicates relatively less substantial markets such as stage equipment and special lighting service export are more suitable for SME businesses. However, possible problems may be recognized as following; doubtful capabilities by such businesses to join in the vast and competitive global market and pursue manufacturing and service based export. This point is also supported by the fact that such in general SME businesses have substantially less experience in exporting products and services abroad. Realizing the distinctive features of the Korean economy, it is unarguable that every sector and area of global market must be regarded and monitored closely. Hence, it can be argued that there is an imminent need for establishment of supportive institution to assist export process of combination of stage equipments and special lighting service. This study emphasizes the need to improve export process of stage equipments, special lighting services as well as other related products and services which have been focused in domestic market only until now. Further, it also analyzed the potential prospect of such direction reconciling current crisis our manufacturing industry is facing. Even though it maybe regarded as one of the niche market for export of Korea in the short term view, stage equipment and special lighting service industry may rapidly grow as the global cultural industries have grown along with the increase of national income earnings overall. Due to such advantageous features, it can be expected that such industries will show strong growth in the near future. After analyzing the fact that Korea's plants (eg. powerplants) export sector is at its boom, there is a need to transform stage equipment and special lighting service export market into a primary market from a secondary(niche) market for SMEs. This study is viewed from the Korean economic and export sector aspect in the aim of seeking a solution to conquest our realistic limit in our export sector by developing a suitable export model. There have been cases of very few attempts to expand abroad by SMEs who have failed miserably due to their failure to adapt to foreign culture, practice and languages as well as substantial lack in experience in export marketing. Despite this, neglecting our manufacturing industry as it is which is showing its limit and problems is out of option therefore, it is imminent that we come up with an effective measure to address this problem and service export can be suggested as one of them. This study reveals manufacturing-service export model of stage equipment and special lighting service and its related areas is recognized as a field with a very strong future and furthermore, it is expected to bring synergy effects in manufacturing and services sector as well. Further, the operation strategy contains combination, composition and fusion(convergence) of manufacturing and service sectors which could derive various of export products which displays greater success probability or this export model. The outcome of this research is expected to become a useful source for enterprises related to such industry which are seeking a possible global expansion. Furthermore, it is also expected to become a catalyst which fastens the process of global expansion and not only that, we are firmly assured that this study will become an opportunity to improve our current policies and institutions related to this area's export market.

• The korean journal of orthodontics
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• v.38 no.5
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• pp.304-313
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• 2008

Objective: The purpose of this study was to evaluate the stress distribution in bone and displacement distribution of the miniscrew according to the length and number of the miniscrews used for the fixation of miniplate, and the direction of orthodontic force. Methods: Four types of finite element models were designed to show various lengths (6 mm, 4 mm) and number (3, 2) of 2 mm diameter miniscrew used for the fixation of six holes for a curvilinear miniplate. A traction force of 4 N was applied at $0^{\circ}$, $30^{\circ}$, $60^{\circ}$ and $90^{\circ}$ to an imaginary axis connecting the two most distal unfixed holes of the miniplate. Results: The smaller the number of the miniscrew and the shorter the length of the miniscrew, the more the maximum von Mises stress in the bone and maximum displacement of the miniscrew increased. Most von Mises stress in the bone was absorbed in the cortical portion rather than in the cancellous portion. The more the angle of the applied force to the imaginary axis increased, the more the maximum von Mises stress in the bone and maximum displacement of the miniscrew increased. The maximum von Mises stress in the bone and maximum displacement of the miniscrew were measured around the most distal screw-fixed area. Condusions: The results suggest that the miniplate system should be positioned in the rigid cortical bone with 3 miniscrews of 2 mm diameter and 6 mm length, and its imaginary axis placed as parallel as possible to the direction of orthodontic force to obtain good primary stability.

• Korean Journal of Ecology and Environment
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• v.45 no.4
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• pp.378-391
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• 2012

The objective of this study was to determine temporal patterns and longitudinal gradients of water chemistry at eight artificial reservoirs and ten streams within the Han-River watershed along the main axis of the headwaters to the downstreams during 2009~2010. Also, we evaluated chemical relations and their variations among major trophic variables such as total nitrogen (TN), total phosphorus (TP), and chlorophyll-a (CHL-a) and determined intense summer monsoon and annual precipitation effects on algal growth using empirical regression model. Stream water quality of TN, TP, and other parameters degradated toward the downstreams, and especially was largely impacted by point-sources of wastewater disposal plants near Jungrang Stream. In contrast, summer river runoff and rainwater improved the stream water quality of TP, TN, and ionic contents, measured as conductivity (EC) in the downstream reach. Empirical linear regression models of log-transformed CHL-a against log-transformed TN, TP, and TN : TP mass ratios in five reservoirs indicated that the variation of TP accounted 33.8% ($R^2$=0.338, p<0.001, slope=0.710) in the variation of CHL and the variation of TN accounted only 21.4% ($R^2$=0.214, p<0.001) in the CHL-a. Overall, our study suggests that, primary productions, estimated as CHL-a, were more determined by ambient phosphorus loading rather than nitrogen in the lentic systems of artificial reservoirs, and the stream water quality as lotic ecosystems were more influenced by a point-source locations of tributary streams and intense seasonal rainfall rather than a presence of artificial dam reservoirs along the main axis of the watershed.

• Korean Journal of Microbiology
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• v.41 no.2
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• pp.99-104
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• 2005

Corynebacterial clones which exert regulatory effects on the expression of the glyoxylate bypass genes were isolated using a reporter plasmid carrying the enteric lacZ fused to the aceB promoter of Corynebacterium glutamicum. Some clones carried common fragments as turned out by DNA mapping technique. Subcloning analysis followed by the measurement of $\beta-galactosidase$ activity in Escherichia coli identified the region responsible for the aceB-repressing activity. Sequence analysis of the DNA fragment identified two independent ORFs of ORF1 and ORF2. Among them, ORF2 was turned out to be responsible for the aceB-repressing activity. ORF1 encoded a 23,216 Da protein composed of 206 amino acids. Sequence similarity search indicated that the ORF may encode a ECF-type $\sigma$ factor and designated sigH. To identify the function of sigH, C. glutamicum sigH mutant was constructed by gene disruption technique and the sigH mutant showed growth retardation as compared to the wild type strain. In addition, the mutant strain showed sensitivity to oxidative-stress generating agent plumbagin. This result imply that sigH is probably involved in the stress response occurring during normal cell growth.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.