• The Journal of Engineering Geology
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• v.24 no.1
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• pp.111-122
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• 2014

For accurate laboratory evaluations of soil deposits, it is essential that the samples are undisturbed. An artificial ground-freezing system is the one of the most effective methods for obtaining undisturbed samples from sand deposits. The objective of this study is to estimate the shear strengths and the characteristics of elastic waves of frozen-thawed and unfrozen specimens through the undrained triaxial compression test. For the experiments, Jumunjin standard sands are used to prepare frozen and unfrozen specimens with similar relative densities (60% and 80%). The water pluviation method is used to simulate the fully saturated condition under the groundwater table. When thawing the frozen specimens, the temperature is measured every minute. After the specimens are completely thawed, undrained triaxial compression tests are conducted using the same procedures as for the unfrozen specimens. During the triaxial tests (saturation, consolidation, and shear phase), compressional and shear waves are measured. The results show that the freeze-thaw process has minor effects on the peak deviatoric stress and shear strength values, and that the process does not affect the internal friction angle. The compressional wave velocity increases with increasing B-value to 1800 m/s in the saturation phase, but tends to remain constant in the process of consolidation and shearing. The shear wave velocity decreases with increasing B-value in the process of saturation, but changes velocity in accordance with the change in effective stress in the processes of consolidation and shearing. The compressional wave velocity has similar values regardless of the freeze-thaw process, but values of shear wave velocity are slighly lower in frozen-thawed specimens than in unfrozen specimens. This study is a preliminary experiment for estimating the shear strength and characteristics of elastic wave velocity in undisturbed frozen specimens that have been obtained using the artificial ground-freezing method.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.1 no.1
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• pp.3-17
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• 1998

The purposes of this study is to investigate the possibility of planting trees at space land in the riverside. The space land is for the green space. Calculating the plantable space in the representation section and the flood flowing stability of the existing banks based on the hydrological and meteorological data of the Kap-Chon riverbasin located in Tae-jon, the following results are drawn. (1) The flood discharges in each flow section are $698.7m^3/s$ in section 1, $654.6m^3/s$ in section 2, and $1353.3m^3/s$ in section 3 during 100 years recurrence interval. Because the designed-flood discharges in those sections are $1719.9m^3/s$, $2119.7m^3/s$, and $1512.8m^3/s$ respectively, safety for flood flowing is sufficient in existing banks. (2) The possible clearance for planting trees is 1.80m in section 1, 3.90m in section 2, and 0.01m in section 3. Planting clearance is enough in section 1 and 2. However, planting should be planned after estimating a rise-height due to the bridge piers, because many piers under riverine-highway are now on the construction in section 2. The section 3 does not have sufficient clearance for planting trees, but the planting is possible after getting enough flow area with slope by cutting the terrace land on the river artificially heightened. (3) In case of planting a tree 70cm diameter in $1m^2$ in section 1, the water level increases by 0.60m. Planting a tree in a $48m^2$ area increases the water level by 0.90m. Considering that plantable clearance is 1.8m in section 1, it is sufficient to flow safely. But if the trees are planted so compactly from the upper stream, expected heavy resistance is expected due to caught materials on the trees. So, trees have to be planted widely in upper streams but compactedly in lower streams. (4) The river width without changing, Kap-Chon's flow channel can be snaked in accordance with the nature law the wide terrace land in the riverside. Decreased flow area due to planting trees will be compensated by the inclination of terrace land. And, it is theoretically proved that the flood discharge is safe even though the terrace land on the river is parked similar to the nature. Planting trees in the terrace land of the Kap-chon river to the extent that flood flowing is not adversely affected, we can get the enjoyable park to citizens not spending expensive cost. It also contributes to the recovery of ecosystem, which gives the natural beauty of river and shade to citizens and becomes good natural-educational places for children.

• Journal of Educational Research in Mathematics
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• v.24 no.3
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• pp.333-357
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• 2014

This study resulted from a study regarding creative STEAM System based upon an experiment with the center of gravity. The results of the study are constructed by a fusion of mathematics and physics, showing that they are the same as mathematical calculations. Also, students can find that center of gravity of an object is in equilibrium on a metal rod when the center of gravity exactly is placed on the rod. The fact that an experimental results are correspond to calculations can maximize the effectiveness of teaching. And also this study has the following effectiveness. First, the exact construction and calculations arouses good competition among students. Second, this experiment can give students a motivation for study and increase their thinking in classes because the theoretical background of center of gravity experiment is basically attributed to math and science classes in school. This study includes three different types of center-of-gravity experiments. One is a simple type of experiment in which center of gravity exists inside of an object. Another is a complicated one in which the center of gravity is also inside of an object. However, the third type is an experiment in where the center of gravity is outside of an object. Therefore, it gives students an opportunity to discuss how to confirm equilibrium on a metal rod when the object has its center of gravity outside. Having discussions in class will allow students to have a critical way of thinking. In addition, searching for a way to solve a problem will increase creativity of students as well. And the last type is finding the center of gravity of a big acrylic panel where multiple objects are on the panel. According to the survey and interview conducted by students who participated in this program, teaching based on creative STEAM system helps students to get a better understanding and more fast acquisition of knowledge. We can expect that a well-planned creative STEAM system through a continuous study will be both effective and efficient in educating critical and creative students.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.10
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• pp.942-948
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• 2010

To predict the potential reduction of $CH_4$ by recovering several types of wastes as of reusable energy sources like RDF, the $CH_4$ emission for each type of waste from Landfill Site 3 of SUDOKWON Landfill was estimated for the period of 2017 to 2024. Without any recovering effort on types of wastes being disposed of at the Landfill, there are producing a total of $526{\times}10^6\;Nm^3$ of $CH_4$; municipal waste of $337{\times}10^6\;Nm^3$, construction waste of $178{\times}10^6\;Nm^3$, and facility waste of $11{\times}10^6\;Nm^3$. It composed of 41.5% to that observed from 2002 to 2009. With properly retrieved by MT(Mechanical Treatment), it released a total of $158{\times}10^6\;Nm^3$ $CH_4$; $127{\times}10^6\;Nm^3$, $28{\times}10^6\;Nm^3$, and $4{\times}10^6\;Nm^3$, respectively. Additionally, when biologically degradable residues can be fully treated by MBT (Mechanical & Biological Treatment) system, the total amount of $CH_4$ emitted from the site will be lowered down as low as $115{\times}10^6\;Nm^3$, which is comparably lower showing only 21.8% to that for without any energy recovery practice. Futhermore, it is far less showing 9.1% to that obtained from 2002 to 2009. It can be decided that predictable amount of $CH_4$ emission reduced could be successfully accomplished and enhanced through ways of energy recovery efforts such as further scale adjustment of LFG treatment capacity in association with currently implemented practices in the landfill site.

• Magazine of the Korean Society of Agricultural Engineers
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• v.15 no.4
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• pp.3170-3180
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• 1973

This study of the subject will review past and present irrigation development in Korea. Particular attention will be given to water pricing structure and a case study on the purpose of rational operation and management of irrigation water and organizations, and the optimum irrigation water and organizations, and the optimum irrigation water fee inorder to reduce farmers burden and to rationalize the farmland associations management so as to achieve development of the rural environment. In 1971, the reservoir of the Farmland Improvement A sociation (FIA) produced only 775 millison $m^3$ of irrigation water or 77% of planned capacity of 1,015 million $m^3$. It was caused by inefficient maintenance of irrigation facilities; for instance, about 21% of reservoirs, pumping stations and weirs in Korea have been silted by soil erosion which hinder to water production according to an ADC survey. The first Irritation Association was established in 1906, whcih was renamed the Farmland Assoeiation by the Rural Development Enouragement Law in 1970. By the end of 1971, 411,000 ha of rice paddies were under the control of 267 associations nationwide. The average water price assessed by Associations nationwide rose from 790 won per 0.1 ha. in 1966 to 1,886 won in 1971. The annual growth rate was 20%. The highest water price in 1971 was 4,773 won her 0.1 ha. and the lowest was 437 won. This range was caused by differences in debt burden, geographic conditions and management efficiency among the Associations. In 1971, the number of Associations which exceeded the average water price of 1,886 won per 0.1 ha. was 144, or 55.1% of all Association. In determination of water price, there are two principles; one is determined by production cost such as installation cost of irrigation facilities, maintenance cost, management cost and depreciation ect. For instance, the Yong San River Development project was required 33.7 billion won for total construction and maintenance cost is 3.1 billion won for repayment, maintenance and management cost per year. The project produces 590 million $m^3$ of irrigation water annually. Accordingly, the water price per $m^3$ is 5.25 won. The other principle is determined by water value in the crop products and in compared with production of irrigated paddy and non-irrigated paddy. By using this method, water value in compared with paddy rice vs. upland rice(Average of 1967-1971) was 14.15 won per $m^3$ and irrigated paddy vs. non-irrigated paddy was 2.98 won per $m^3$. In contrast the irrigation fee in average association of 1967-1971 was 1.54 won per $m^3$. Accordingly, the current national average irrigation fee(water price) is resonable compared with its water value. In this study, it is found that the ceiling of water price in terms of water value is 2.98 won per $m^3$ or 2,530 won per 0.1 ha. However, in 1971 55% of the associations were above the average of nationwide irrigation fees. which shows the need for rationalization of the Association's management. In connection with rationalization of the Association's management, this study recommends the following matters. (1) Irrigation fee must be assessed according to the amount of water consumption taking intoaccount the farmer's ability. (2) Irrigation fee should be graded according to behefits and crop patterns. (3) Training personnel in the operation and procedures of water management to save O&M costs. (4) Insolvent farmland association should be integrated into larger, sound associations in the same GUN in order to reduce farmers' water cost. (5) The maintenance and repair of existing irrigation facilities is as important as expansion of facilities. (6) Establishment of a new Union of Farmland Association is required to promoted proper maintenance and to protect the huge investment in irrigation facilities by means of technical supervision and guidance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• 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.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.