• Journal of the Korean Geotechnical Society
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• v.22 no.6
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• pp.27-40
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• 2006

Damage of cut-and-cover tunnel lining can be attributed to physical and mechanical factors. Physical factors include material property, reinforcement corrosion, etc. while mechanical factors include underground water pressure, vehicle loads, etc. This study is limited to the modeling of rigid circular cut and cover tunnel constructed at a depth of $1.0{\sim}1.5D$ in loose sandy ground and subjected to a vibration frequency of 100 Hz. In this study, only damages due to mechanical factors in the form of additional loads were considered. Among the different types of additional, excessive earth pressure acting on the cut-and-cover tunnel lining is considered as one of the major factors that induce deformation and damage of tunnels after the construction is completed. Excessive earth pressure may be attributed to insufficient compaction, consolidation due to self-weight of backfill soil, precipitation and vibration caused by traffic. Laboratory tunnel model tests were performed in order to determine the earth pressure acting on the tunnel lining and to investigate the applicability of existing earth pressure formulas. Based on the difference in the monitored and computed earth pressure, a factor of safety was recommended. Soil deformation mechanism around the tunnel was also presented using the picture analysis method.

• 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 The Korean Association For Science Education
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• v.29 no.8
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• pp.990-1010
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• 2009

To derive brain-based evolutionary educational principles, this study examined the studies on the structural and functional characteristics of human brain, the biological evolution occurring between- and within-organism, and the evolutionary attributes embedded in science itself and individual scientist's scientific activities. On the basis of the core characteristics of human brain and the framework of universal Darwinism or universal selectionism consisted of generation-test-retention (g-t-r) processes, a Model of Brain-based Evolutionary Scientific Teaching for Learning (BEST-L) was developed. The model consists of three components, three steps, and assessment part. The three components are the affective (A), behavioral (B), and cognitive (C) components. Each component consists of three steps of Diversifying $\rightarrow$ Emulating (Executing, Estimating, Evaluating) $\rightarrow$ Furthering (ABC-DEF). The model is 'brain-based' in the aspect of consecutive incorporation of the affective component which is based on limbic system of human brain associated with emotions, the behavioral component which is associated with the occipital lobes performing visual processing, temporal lobes performing functions of language generation and understanding, and parietal lobes, which receive and process sensory information and execute motor activities of the body, and the cognitive component which is based on the prefrontal lobes involved in thinking, planning, judging, and problem solving. On the other hand, the model is 'evolutionary' in the aspect of proceeding according to the processes of the diversifying step to generate variants in each component, the emulating step to test and select useful or valuable things among the variants, and the furthering step to extend or apply the selected things. For three components of ABC, to reflect the importance of emotional factors as a starting point in scientific activity as well as the dominant role of limbic system relative to cortex of brain, the model emphasizes the DARWIN (Driving Affective Realm for Whole Intellectual Network) approach.

• Korean Journal of Environmental Agriculture
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• v.27 no.2
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• pp.120-132
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• 2008

Remotely sensed images from a satellite can be applied for detecting and quantifying spatial and temporal variations in terms of landuse & landcover, crop growth, and disaster for agricultural applications. The purposes of this study were to analyze topography using DEM(digital elevation model) and classify landuse & landcover into 10 classes-paddy field, dry field, forest, bare land, grass & bush, water body, reclaimed land, salt farm, residence & building, and others-using Landsat TM images in North Korea. Elevation was greater than 1,000 meters in the eastern part of North Korea around Ranggang-do where Kaemagowon was located. Pyeongnam and Hwangnam in the western part of North Korea were low in elevation. Topography of North Korea showed typical 'east-high and west-low' landform characteristics. Landcover classification of North Korea using spectral reflectance of multi-temporal Landsat TM images was performed and the statistics of each landcover by administrative district, slope, and agroclimatic zone were calculated in terms of area. Forest areas accounted for 69.6 percent of the whole area while the areas of dry fields and paddy fields were 15.7 percent and 4.2 percent, respectively. Bare land and water body occupied 6.6 percent and 1.6 percent, respectively. Residence & building reached less than 1 percent of the country. Paddy field areas concentrated in the A slope ranged from 0 to 2 percent(greater than 80 percent). The dry field areas were shown in the A slope the most, followed by D, E, C, B, and F slopes. According to the statistics by agroclimatic zone, paddy and dry fields were mainly distributed in the North plain region(N-6) and North western coastal region(N-7). Forest areas were evenly distributed all over the agroclimatic regions. Periodic landcover analysis of North Korea based on remote sensing technique using satellite imagery can produce spatial and temporal statistics information for future landuse management and planning of North Korea.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.7
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• pp.935-945
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• 2009

This study was performed to find the optimum ratio of ingredients in the Chinese quince jam. The experiment was designed according to the D-optimal design of mixture design, which included 14 experimental points with 4 replicates for three independent variables (Chinese quince paste $45{\sim}60%$, pectin $1.5{\sim}4.5%$, sugar $45.5{\sim}63.5%$). A mathematical analytical tool was employed for the optimization of typical ingredients. The canonical form and trace plot showed the influence of each ingredient in the mixture against final product. By use of F-test, sweetness, pH, L, b, ${\Delta}E$, and firmness were expressed by a linear model, while the spreadmeter value, a, and sensory characteristics (appearance, color, smell, taste, and overall acceptability) were by a quadratic model. The optimum formulations by numerical and graphical method were similar: Chinese quince paste 54.48%, pectin 2.45%, and sugar 53.07%. Optimum ingredient formulation is expected to improve use of Chinese quince and contribute to commercialization of high quality Chinese quince jam.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.6
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• pp.129-144
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• 2014

This study is to examine the effect of mutualistic benevolence, altruistic benevolence, and communication on affective commitment, calculative commitment, and switching intentions and investigate how two commitment dimensions play mediating roles between two benevolence constructs and communication, and switching intentions. For these purposes the author developed a structural model which consists of several constructs. In this model, benevolence factor that consists of mutualistic benevolence and altruistic benevolence, and communication were proposed to affect two commitment constructs and result in, increase switching intentions. Thus, two commitment constructs(e.g., affective and calculative commitment) were proposed as core mediating variables between mutualistic benevolence, altruistic benevolence, and communication, and switching intentions. The data were collected from 210 automobile parts buyers and were analyzed using frequency, reliability, and confirmatory factor analysis and SEM (structural equation model) with SPSS/WIN 20.0 and AMOS 20.0. The data were analyzed with structural equation modeling with AMOS 20.0 and SPSS Win/PC 20.0. The result of the overall model analysis appeared as follows: ${\chi}2=224.885$, d.f=123(${\chi}2/df=1.828$), p=0.000, GFI=.898, AGFI=.859, IFI=.967, NFI=.930, TLI=.958, RMSEA=.063, CFI=.966. Since the result of the overall model analysis demonstrated a good fit, we could further analyze our data. The findings can be summarized as follows: According to structural equation modeling analysis, firstly, mutualistic benevolence has direct effects on calculate commitment and affective commitment. Secondly, altruistic benevolence has a positively direct effect on calculate commitment. Thirdly, communication has a statistically direct effect on affective commitment. Fourthly, calculative commitment has direct effects on affective commitment and switching intentions. Lastly, affective commitment has a direct effect on switching intentions.

• Journal of the Korean Chemical Society
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• v.47 no.4
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• pp.391-400
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• 2003

This study was performed the analysis of seven kinds of the hight school chemistry II textbooks based on the 6th curriculum. Particularly, inquiry activity part was analyzed by the three dimension framework which consists of inquiry content dimension, inquiry process dimension and inquiry context dimension. In the analysis of the inquiry content dimension of inquiry activities, the total number of themes in seven kinds of textbook was 212. And the number of inquiry activities in seven kinds of textbook was diverse: A textbook had 28, B textbook 25, C textbook 31, D textbook 35, E textbook 31, F textbook 29 and G textbook 33. As for the avaerage number of inquiry activities of each chapter, chapter I "Material Science" is 3.00(9.91${\%}$), chapter II "Atomic Structure and Periodic Table" 4.57(15.1${\%}$), chapter III "Chemical Bonding and Compound" 6.86(22.6${\%}$), chapter IV "State of Matter and Solution" 7.00(23.1${\%}$), chapter V "Chemical Reaction" 8.86(29.2${\%}$). For the analysis of inquiry process dimension, it follows in the order of 'observation and measuring (66.7${\%}$)', 'Interpreting data and formulating generalizations (26.5${\%}$)', 'seeing a problem and seeking ways to solve it (4.1%)', and 'building, testing and revising the theoretical model (2.7${\%}$)'. As for the analysis of the inquiry context dimension, the scientific context occupied 90.5${\%}$, the individual context 4.3${\%}$, the social context 0.9${\%}$, and the technical context 4.3${\%}$. It shows that the proportion of STS(Science-Technology-Society) related contents in inquiry activities was only 9.5${\%}$.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.15 no.1
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• pp.31-36
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• 2004

Background and Objectives : It has been shown that the epilaryngeal tube in the human airway is responsible for vocal ring, or the singer's formant. In previous study, authors showed that in trained tenors, besides the conventional singer's formant in the region of ,5500Hz, another energy peak was observed in the region of 8,000Hz. This peak was interpreted as the second resonance of the epilarynx tube. Singers in other voice categories who produce vocal ring are assumed to have the same peak, but no measurements have as yet been made. Materials and Methods : Fifteen tenors, fourteen baritones, seven sopranos and five mezzo sopranos attending the music college, department of vocal music who could reliably produce the head and chest registers were chosen for this study. Each subject was asked to produce an/ah/sound for at least three seconds for the head register sound(tenors ; G4, barions ; E4 sopranos ; F5 and mezzosopranos ; C5) and for the chest register sound (tenors ; C3, baritones ; D3, sopranos ; D4 and Mezzosoprano ; A3). The sound data was analyzed using the Fast Fourier Transform (FFT)-based power spectrum, Long term average(LTA) power spectrum using the FFT algorithm of the Computerized Speech Lab (CSL, Kay elemetrics, Model 4300B, USA). Statistical analysis was performed using the Mann-Whitney test of the Statistical Package for Social sciences(SPSS). Results : For head register sounds, a significant increase was seen in the 2,200-3,400Hz region(p<0.05) and the Similar to the head register sounds, there was a significant increase in energy in the four trained singer group compared with the untrained group in the 2,200-3,100Hz region(p<0.05), the 7,800-8,400Hz region(p<0.05) for the chest register sounds. Conclusions : When good vocal production was made for the head and chest registers, an energy peak was observed near 2,500Hz, a frequency already known as the "singer's formant', in all subjects in the study group. Another region of increased energy was observed around 8,000Hz that had not been noticed previously. The authors believe this region to be the second singer's formant.

• Information Systems Review
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• v.11 no.1
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• pp.107-132
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• 2009

The purpose of this research is to define the consulting competence of IT consultants and empirically analyze the perceptual differences between the IT consultant group and the client group. Based on the previous researches and the opinion of the actual IT consultants, the consulting capability model has been established, which consists of six categories and eighteen factors. Six categories are (1) IT domain expertise, (2) problem solving ability, (3) project management capability, (4) communication skills, (5) human relations skills, and (6) professional ethics and attitude. Two field surveys have been performed and the responses of 174 IT consultants 116 clients have been acquired. It is shown that the level of possessed proficiency of IT consulting capability is far lower than the level of the required proficiency. And there exist the perceptual difference between two responding groups with respect to the level required proficiency but no difference exists in terms of the level of possessed proficiency. The findings of this research can provide some useful information in order to fully understand the differences between the IT consultant group and the client group.