• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Journal of Korean Port Research
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• v.10 no.2
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• pp.91-101
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• 1996

Today transportation problems are severer with the increase of the vehicles and travel demand in urban areas, but could not be completely solved with only the expansion of the new transportation facilities. Because the expansion of the new transportation facilities are limited in urban areas. As one of the Transportation System Management(TSM) techniques in this study, the simulation results of the existing signal systems which were operated based upon the peak time periods for increasing the efficiency on the pretimed signalized intersections(PSI) during the different time periods : the AM on-Peak, the AM off-Peak, the PM off-Peak, and the PM on-Peak, were as follows : i) There was no distinct difference in the total traffic volumes concentrated on the signalized intersections during the different time periods, but a considerably big difference in the directional traffic volumes for those time periods. ii) There were about 53% reduction of the average delay and 51% reduction of the fuel consumption when applying the different signal systems to the different time periods regardless of the CBD and Non-CBD. iii) There were about 36% increase of the average delay and 33% increase of the fuel consumption when applying the same signal systems during the peak time periods to the different time periods regardless of the CBD and Non-CBD. Based on the above results, it was concluded that constructing the different signal systems for the different time periods would be better than construction the same ones for those periods on the pretimed signalized intersections in urban areas.

• 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.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.110-126
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• 2024

This study conducted a classification of small-scale biological habitats created in Seoul to analyze and synthesize location characteristics, habitat structure, biological habitat functions, and threat factors of representative sites, as well as derive creation and management problems according to the ecological characteristics. The aim was to suggest improvement measures and management items. Data collected through a field survey was used to categorize 39 locations, and 8 representative sites were selected by dividing them into location, water system, and size as classification criteria for typification. Due to the characteristics of each type, the site was created in an area where amphibian movement was disadvantageous due to low or disconnected connectivity with the hinterland forest, and the water supply was unstable in securing a constant flow and maintaining a constant water depth. The habitat structure has a small area, an artificial habitat structure that is unfavorable for amphibians, having the possibility of sediment inflow, and damage to the revetment area. The biological habitat function is a lack of wetland plants and the distribution of naturalized grasses, and threats include the establishment of hiking trails and decks in the surrounding area. Artificial disturbances occur adjacent to facilities. When creating habitats according to the characteristics of each type, it was necessary to review the possibility of an artificial water supply and introduce a water system with a continuous flow in order to connect the hinterland forest for amphibian movement and locate it in a place where water supply is possible. The habitat structure should be as large as possible, or several small-scale habitats should be connected to create a natural waterfront structure. In addition, additional wetland plants should be introduced to provide shelter for amphibians, and facilities such as walking paths should be installed in areas other than migration routes to prevent artificial disturbances. After construction, the management plan is to maintain various water depths for amphibians to inhabit and spawn, stabilize slopes due to sediment inflow, repair damage to revetments, and remove organic matter deposits to secure natural grasses and open water. Artificial management should be minimized. This study proposed improvement measures to improve the function of biological habitats through the analysis of problems with previously applied techniques, and based on this, in the future, small-scale biological habitat spaces suitable for the urban environment can be created for local governments that want to create small-scale biological habitat spaces, including Seoul City. It is significant in that it can provide management plans.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• 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.

• Korean Journal of Heritage: History & Science
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• v.46 no.2
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• pp.46-69
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• 2013

The results of study on the meaning landscape and environmental design techniques of the Byulup, Yoohoedang garden(Hageowon) based on the story in the collection of Kwon Yi-jin (Yoohoedangjip, 有懷堂集), are as below. First, Yoohoedang Kwon Yi-jin (有懷堂 權以鎭 : 1668~1734) constructed a Byulup garden consisting of ancestor grave, Byulup, garden, and a school, through 3 steps for 20 years in the back hill area of Moosoo-dong village, south of Mountain Bomun in Daejeon. In other words, he built the Byulup(別業, Yoohoedang) by placing his father's grave in the back hill of the village, and then constructed Yoegeongam(餘慶菴) and Geoupjae(居業齋) for protection of the pond(Napoji, 納汚池), garden(Banhwanwon, 盤桓園), and ancestor graves, and descendants' studying in the middle stage. He built an extension in Yoohoedang and finally completed the large-size garden (Hageowon) by extending the east area. Second, in terms of geomancy sense, Yoohoedang Byulup located in Moosoo-dong village area is the representative example including all space elements such as main living house (the head family house of Andong Kwon family), Byulup (Yoohoedang), ancestor graves, Hagoewon (garden) and Yoegeongam (cemetery management and school) which byulup type Byulseo should be equipped with. Thirdly, there are various meaning landscape elements combining the value system of Confucianism, Buddhism and Taoism value, including; (1) remembering parents, (2) harmonious family, (3) integrity, (4) virtue, (5) noble personality, (6) good luck, (7) hermit life, (8) family prosperity and learning development, (9) grace from ancestors, (10) fairyland, (11) guarding ancestor graves, and (12) living ever-young. Fourth, after he arranged ancestor graveyard in the back of the village, he used surrounding natural landscapes to construct Hagoewon garden with water garden consisting of 4 mountain streams and 3 ponds for 13 years, and finally completed a beautiful fairyland with 5 platforms, 3 bamboo forests, as well as the Seokgasan(石假山, artificial hill). Fifth, he adopted landscape plantation (28 kinds; pine, maple, royal azalea, azalea, persimmon tree, bamboo, willow, pomegranate tree, rose, chinensis, chaenomeles speciosa, Japanese azalea, peach tree, lotus, chrysanthemum, peony, and Paeonia suffruticosa, etc.) to apply romance from poetic affection, symbol and ideal from personification, as well as plantation plan considering seasonal landscapes. Landscape rocks were used by intact use of natural rocks, connecting with water elements, garden ornament method using Seokyeonji and flower steps, and mountain Seokga method showing the essence of landscape meanings. In addition, waterscape are characterized by active use of water considering natural streams and physio-graphic condition (eastern valley), ecological corridor role that rhythmically connects each space of the garden and waterways following routes, landscape meaning introduction connecting 'gaining knowledge by the study of things' values including Hwalsoodam(活水潭, pond), Mongjeong(蒙井, spring), Hosoo(濠水, stream), and Boksoo(?水, stream), and sensuous experience space construction with auditory and visualization using properties of landscape matters.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.11-22
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• 2013

EAREF(East-Asia Reference Frame) is based on the Eurasian Plate which is considered relatively stable. It is managing the coordinate reference system by a specific epoch through the networking of GNSS CORS of the East-Asia region covering North-east and South-east Asia. Also it'll be the goal to assist integrating the geospatial information management. This study aims to estimate the precise coordinates of EAREF in the East-Asia region at the epoch of January 1st of 2012 (2012.0) after the Great East Japan Earthquake. It is related to 1st stage study for construction of data sets and made up the data processing techniques through the various experiments to upgrade the accuracy. Based on the results of the study, we calculated the initial precise coordinates of the EAREF network from the 2012.0 epoch covering the East-Asia region. The accuracy of the estimated coordinates was compared with the weekly solution provided by the IGS analysis centre. The differences were 0.004m, 0.007m and 0.009m at the directions of X, Y and Z respectively. In addition, this study reviews the next procedure how to implement and upgrade the EAREF network.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.121-128
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• 2012

High resolution intertidal DEM is a basic material for science research like sedimentation/erosion by ocean current, and is invaluable in a monitoring of environmental changes and practical management of coastal wetland. Since the intertidal zone changes rapidly by the inflow of fluvial debris and tide condition, remote sensing is an effective tool for observing large areas in short time. Although radar interferometry is one of the well-known techniques for generating high resolution DEM, conventional repeat-pass interferometry has difficulty on acquiring enough coherence over tidal flat due to the limited exposure time and the rapid changes in surface condition. In order to overcome these constraints, we tested the feasibility of radar interferometry using Cosmo-SkyMed tandem-like one-day data and ERS-ENVISAT cross tandem data with very short revisit period compared to the conventional repeat pass data. Small temporal baseline combined with long perpendicular baseline allowed high coherence over most of the exposed tidal flat surface in both observations. However the interferometric phases acquired from Cosmo-SkyMed data suffer from atmospheric delay and changes in soil moisture contents. The ERS-ENVISAT pair, on the other hand, provides nice phase which agree well with the real topography, because the atmospheric effect in 30-minute gap is almost same to both images so that they are cancelled out in the interferometric process. Thus, the cross interferometry with very small temporal baseline and large perpendicular baseline is one of the most reliable solutions for the intertidal DEM construction which requires very accurate mapping of the elevation.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.