• Journal of the Korea Concrete Institute
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• v.19 no.5
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• pp.613-621
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• 2007

Due to the increasing significance of durability, much researches on carbonation, one of the major deterioration phenomena are carried out. However, conventional researches based on fully hardened concrete are focused on prediction of carbonation depth and they sometimes cause errors. In contrast with steel members, behaviors in early-aged concrete such as porosity and hydrates (calcium hydroxide) are very important and may be changed under carbonation process. Because transportation of deteriorating factors is mainly dependent on porosity and saturation, it is desirable to consider these changes in behaviors in early-aged concrete under carbonation for reasonable analysis of durability in long term exposure or combined deterioration. As for porosity, unless the decrease in $CO_2$ diffusion due to change in porosity is considered, the results from the prediction is overestimated. The carbonation depth and characteristics of pore water are mainly determined by amount of calcium hydroxide, and bound chloride content in carbonated concrete is also affected. So Analysis based on test for hydration and porosity is recently carried out for evaluation of carbonation characteristics. In this study, changes in porosity and hydrate $(Ca(OH)_2)$ under carbonation process are performed through the tests. Mercury Intrusion Porosimetry (MIP) for changed porosity, Thermogravimetric Analysis (TGA) for amount of $(Ca(OH)_2)$ are carried out respectively and analysis technique for porosity and hydrates under carbonation is developed utilizing modeling for behavior in early-aged concrete such as multi component hydration heat model (MCHHM) and micro pore structure formation model (MPSFM). The results from developed technique is in reasonable agreement with experimental data, respectively and they are evaluated to be used for analysis of chloride behavior in carbonated concrete.

• Economic and Environmental Geology
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• v.49 no.2
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• pp.105-120
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• 2016

Most of riverside in Korea, in case of application of water curtain cultivation (WCC) technique, has been inveterately suffering from the gradual drawdown of groundwater level and related shortage of water resources during the WCC peak time. We believe that the water resources issue in these riverside areas can be effectively solved when the interaction between groundwater and nearby surface water is well understood. To investigate the connection between stream and ground water, and the influence of stream water on the nearby aquifer, this study examined the water temperature and oxygen and hydrogen stable isotopic compositions. The study area is well-known strawberry field applying the WCC technique in Sangdae-ri, Gadeok-myon, Cheongju City, and the sampling was done from February 2012 through June 2014 for stream and ground water. Some groundwater wells near stream showed big temporal variations in water temperature, and their oxygen and hydrogen stable isotopes showed similar compositions to those of adjacent stream water. This indicates that the influence of stream water is highly reflected in the stable isotopic composition of groundwater. Four cross-sectional lines from stream to hillside were established in the study area to determine the spatial differences in water quality of wells. At the late stage of WCC in February to March, groundwater of wells in line with short cross-sectional length showed the narrow range of isotopic compositions; however, those in the long cross-sectional line showed a wide compositional range. It was shown that the influence of the stream water at the late WCC stage have reached to the distance of 160 to 165 m from stream line, which is equivalent to the whole length and one-third point in each short and long cross-sectional line, respectively. Therefore, the wide compositional range in the long cross-sectional lines was not only due to the influence of stream water, but apparently resulted from the change of relative impact of each groundwater supplying from two or more aquifers. In view of stable isotopic compositions, there seems to be three different aquifers in this study area, which is competing for dominance of water quality in wells at each period of WCC.

• Journal of Conservation Science
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• v.32 no.4
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• pp.471-490
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• 2016

This study investigated provenance of raw materials and making technique of lime-based materials used in the tomb barriers of the Yesan Mokri tombs from Joseon dynasty on the basis of analysis to material characteristics and physical properties. In the barrier materials, dry density and porosity are the highest value ($1.82g/cm^3$) and the lowest value (25.20%) in the south wall of No. 1 tomb, respectively. Dry density and porosity are inversely proportional in all barrier materials, but unconfined compressive strength, which is the highest value of $182.36kg/cm^2$ in the No. 2 tomb, does not show an interrelation with porosity and density. Mineral components in the lime-soil mixtures of the tomb barrier are mainly quartz, feldspar, mica and calcite about 200 to $600{\mu}m$ size with yellowish brown matrix. Hydrotalcite and portlandite are detected in the lime mixture, and kaolinite in the soils. The lime materials of the tomb barrier occurred in large quantities weight loss and variable endothermic peaks caused by decarbonization reaction of $CaCO_3$ in the range from 600 to $800^{\circ}C$ in thermal analysis. Making temperature of lime for the tomb barrier is presumed approximately about $800^{\circ}C$ based on the occurrences, compositions and thermal analysis. The tomb barriers are revealed to very wide composition ranges of major elements and loss-on-ignition (22.5 to 33.6 wt.%) owing to mixture of the three materials (lime, sand and clay). It is interpreted that low quality construction technique was applied as the limes are very heterogeneous mixture with aggregates, and curing of the lime was poorly processed in the tomb barriers. Possible limestone sources are distributed in many areas around the Mokri site where limestone conformation and quarries for commercial production are found within Yesan and Hongseong areas. Therefore, we estimated that raw materials were possibly supplied from the local mines near the Mokri site.

• 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 Wetlands Research
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• v.23 no.4
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• pp.327-341
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• 2021

The riparian eco-belt is an efficient technique that can reduce non-point pollution sources in the basin and improve ecological connectivity and health. In Korea, a legal system for the construction and management of riparian eco-belts is in operation. However, it is currently excluded that rivers and floodplains in dam reservoir that are advantageous for buffer functions such as control of non-point pollutants and ecological habitats. Accordingly, this study presented and analyzed a plan to select a site for an integrated riparian ecol-belt that comprehensively evaluates the water quality and ecosystem characteristics of each dam floodplain and riparian zone for the Daecheong Dam basin in Geum River watershed. First, the Daecheong Dam basin was divided into 138 sub-basin with GIS, and the riparian zone adjacent to the dam floodplain was analyzed. Sixteen evaluation factors related to the ecosystem and water quality impact that affect the selection of integrated riparian eco-belt were decided, and weights for the importance of each factor were set through AHP analysis. The priority of site suitability was derived by conducting an integrated evaluation by applying weights to sub-basin by floodplains and riparian zone factors. In order to determine whether the sites derived through GIS site analysis are sutiable for actual implementation, five sites were inspected according to three factors: land use, pollution sources, and ecological connectivity. As a result, it was confirmed that all sites were appropriate to apply integrated riparian ecol-belt. It is judged that the riparian eco-belt site analysis technique proposed through this study can be applied as a useful tool when establishing an integrated riparian zone management policy in the future. However, it might be necessary to experiment various evaluation factors and weights for each item according to the characteristics and issues of each dam. Additional research need to be conducted on elaborated conservation and restoration strategies considering the Green-Blue Network aspect, evaluation of ecosystem services, and interconnection between related laws and policy and its improvements.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.4
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• pp.14-26
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• 2011

Imdaejeong Wonlim is located on the verge of Sangsa Village in Sapyeong-ri, Daepyeong-myeon, Hwasun-gun Gyeongsangnam-do toward Northwest. It was planned by Sa-ae, Minjuhyeon in 1862 on the basis of Gobanwon built by Nam Eongi in 16th century against the backdrop of Mt. Bongjeong and facing Sapyeong Stream. As water flows from west to east in the shape of crane, this area is a propitious site standing for prosperity and happiness. This area shows a distinct feature of Wonlim surrounding the Imdaejeong with multi layers as consisting of 5 districts - front yard where landmark stone with engraved letters of 'Janggujiso of Master Sa-ea' and junipers are harmoniously arranged, internal garden of upper pavilion ranging from a pavilion to square pond with a little island in the middle, Sugyeongwon of under pavilionu consisting of 2 ponds with a painting of three taoist hermits, forest of Mt. Bonggeong and external garden including Sapyeong Stream and farmland. According to documentation and the results of on-site investigation, it is certainly proved that Imdaejeong Wonlim was motivated by Byeoseo Wonlim which realized the idea of 'going back to hometown after resignation' following the motives of Janggujiso, a hideout aimed to accomplish the ideology, 'training mind and fostering innate nature,' on the peaceful site surrounded by water and mountain, as well as motives of Sesimcheo(洗心處) to be unified with morality of Mother Nature, etc. In addition, it implies various imaginary landscapes such as Pihangji, Eupcheongdang, square pond with an island and painting of three Taoist hermits based on a notion that 'the further scent flies away, the fresher it becomes,' which is originated from Aelyeonseol(愛蓮說). In terms of technique of natural landscape treatment, divers techniques are found in Imdaejeong Wonlim such as distant view of Mt. Bongjeong, pulling view with an intention of transparent beauty of moonlight, circle view of natural and cultural sceneries on every side, borrowed scenary of pastoral rural life adopted as an opposite view, looked view of Sulyundaero, over looked view of pond, static view in pavilion and paths, close view of water space such as stream and pond, mushroom-and-umbrella like view of Imdaejeong, vista of pond surrounded by willows, imaginary view of engraved letters meaning 'widen knowledge by studying objectives' and selected view to comprise sunrise and sunset at the same time. In the beginning of construction, various plants seemed to be planted, albeit different from now, such as Ginkgo biloba, Phyllostachys spp., Salix spp., Pinus densiflora, Abies holophylla, Morus bombycis, Juglans mandschurica, Paulownia coreana, Prunus mume, Nelumbo nucifera, etc. Generally, it reflected dignity of Confucianism or beared aspect of semantic landscape implying Taoist taste and idea of Phoenix wishing a prosperity in the future. Furthermore, a diversity of planting methods were pursued for such as liner planting for the periphery of pond, bosquet planting and circle planting adopted around the pavilion, spot planting using green trees, solitary planting of monumentally planted Paulownia coreana and opposite planting presenting the Abies holophylla into yin and yang.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Economic and Environmental Geology
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• v.38 no.6 s.175
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• pp.663-673
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• 2005

This study investigates the relationship between the geochemical maps and the gold-silver deposit locations. Geochemical maps of 21 elements, which are published by KIGAM, locations of gold-silver deposits, and 1:1,000,000 scale geological map of Korea are utilized far this investigation. Pixel size of the basic geochemical maps is 250m and these data are resampled in 1km spacing for the statistical analyses. Relationship between the mine location and the geochemical data are investigated using bayesian statistics and decision tree algorithms. For the bayesian statistics, each geochemical maps are reclassified by percentile divisions which divides the data by 5, 25, 50, 75, 95, and $100\%$ data groups. Number of mine locations in these divisions are counted and the probabilities are calculated. Posterior probabilities of each pixel are calculated using the probability of 21 geochemical maps and the geological map. A prediction map of the mining locations is made by plotting the posterior probability. The input parameters for the decision tree construction are 21 geochemical elements and lithology, and the output parameters are 5 types of mines (Ag/Au, Cu, Fe, Pb/Zn, W) and absence of the mine. The locations for the absence of the mine are selected by resampling the overall area by 1 km spacing and eliminating my resampled points, which is in 750m distance from mine locations. A prediction map of each mine area is produced by applying the decision tree to every pixels. The prediction by Bayesian method is slightly better than the decision tree. However both prediction maps show reasonable match with the input mine locations. We interpret that such match indicate the rules produced by both methods are reasonable and therefore the geochemical data has strong relations with the mine locations. This implies that the geochemical rules could be used as background values oi mine locations, therefore could be used for evaluation of mine contamination. Bayesian statistics indicated that the probability of Au/Ag deposit increases as CaO, Cu, MgO, MnO, Pb and Li increases, and Zr decreases.