• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.9-16
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• 2005

The purpose of this research is to acquire emotional data on luminous source by measuring and evaluating human emotional response to the change of the optical feature of luminous environment Luminous sources used in actual architectural space were selected with the optical feature of luminous soured then to measure and analysis human emotional response on Luminous Source. As a result of that 1) In the result of performance measurement by the item of the clear vision of an optic function the fluorescent lamp of daylight indicated the most excellent Performance. 2) In the item of fatigue and stress, the metal halide lamp and mercury lamp showed the most 3) In $\ulcorner$ suitable in light$\lrcorner$, $\ulcorner$a similar with daylight$\lrcorner$ adjective of the amenity item the fluorescent lamp of daylight which color temperature was high turned up to be high also, in $\ulcorner$brilliant$\lrcorner$, adjective, the metal halide lamp and mercury lamp turned up to be low. 4) In the result of factor analysis, three factors $\ulcorner$activity$\lrcorner$, $\ulcorner$potency$\lrcorner$, $\ulcorner$evaluation$\lrcorner$ were abstracted and $\ulcorner$activity$\lrcorner$ factor has the most influential on evaluating the mood of interior space. 5) For the affection in the mood evaluation by each luminous sources, $\ulcorner$activity$\lrcorner$ factor was the most influential by metal halide lamp and fluorescent lamp of daylight, $\ulcorner$potency$\lrcorner$ factor was most influential by kind of incandescent lamp, $\ulcorner$evaluation$\lrcorner$ factor was most influential by fluorescent lamp of low color temperature.

• 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 KIISE:Databases
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• v.28 no.4
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• pp.529-544
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• 2001

Prefetching is an effective method to minimize the number of roundtrips between the client and the server in database management systems. In this paper we propose new notions of the type-level access pattern and the type-level access locality and developed an efficient prefetchin policy based on the notions. The type-level access patterns is a sequence of attributes that are referenced in accessing the objects: the type-level access locality a phenomenon that regular and repetitive type-level access patterns exist. Existing prefetching methods are based on object-level or page-level access patterns, which consist of object0ids of page-ids of the objects accessed. However, the drawback of these methods is that they work only when exactly the same objects or pages are accessed repeatedly. In contrast, even though the same objects are not accessed repeatedly, our technique effectively prefetches objects if the same attributes are referenced repeatedly, i,e of there is type-level access locality. Many navigational applications in Object-Relational Database Management System(ORDBMs) have type-level access locality. Therefore our technique can be employed in ORDBMs to effectively reduce the number of roundtrips thereby significantly enhancing the performance. We have conducted extensive experiments in a prototype ORDBMS to show the effectiveness of our algorithm. Experimental results using the 007 benchmark and a real GIS application show that our technique provides orders of magnitude improvements in the roundtrips and several factors of improvements in overall performance over on-demand fetching and context-based prefetching, which a state-of the art prefetching method. These results indicate that our approach significantly and is a practical method that can be implemented in commercial ORDMSs.

• Journal of Distribution Science
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• v.10 no.6
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• pp.27-37
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• 2012

Many studies have focused on the importance of organizational integration when companies try to achieve growth through mergers and acquisitions (M&A). However, there has been little research that focuses on the new branding or customer base integration of the M&A process, despite the fact that this integration is very important for achieving M&A goals and business performance in industries such as retail. The purpose of this study is to provide an M&A case study of the retail industry, focused especially on the new branding and customer integration of two department stores. This study examined key integration processes in terms of brand name and membership systems of both companies by examining how the merged company achieved its new branding and the integration of its membership systems. The methodology of this research is the case study, which is used in both normative and empirical studies for distribution research in Korea. This research analyzes the case of both new branding and customer membership systems of the two companies. The new branding initiatives of this case centered on decision making including brand extension and brand naming. The customer membership integration of the two companies is analyzed on the basis of the customer reward programs that include both financial and service rewards. This study shows the success factors of new branding and customer integration in the M&A process in terms of achieving marketing goals and business performance as follows: First, companies should identify the integration areas by analyzing the brand and membership of both companies and make a balanced decision for both the customer and company. Second, the goals of new branding and membership integration in the M&A process should not emphasize business efficiency from a short-term perspective but rather should consider brand power and business synergy from a long-term perspective. Third, the post-merger integration process of the brand or customer areas requires not only the organized execution of integration tasks but also follow-up programs for changes in business strategy and marketing-related programs to realize the synergy effects of integrated organization. Although this study provides a detailed review and analysis of the new branding and customer integration processes in post-merger integration and in identifying the primary decision-making areas of these processes, there are some limitations requiring further research that may overcome or compensate for these limitations. The suggested future research areas are as follows: First, since this research is a case study of only one M&A, it makes few theoretical contributions such as new propositions or theories or possibilities for generalization. This limitation can be overcome through further research using multiple cases, which may lead to new propositions. Second, the methodology of this study lacks sufficient rigor in terms of its analytic approach because this case study was developed and analyzed descriptively. Further research is needed to compensate for these limitations, such as using a theory-based approach or comparative analysis approach that makes case analysis more systematic.

• Journal of Korean Society of Forest Science
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• v.90 no.1
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• pp.43-54
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• 2001

Genetic differentiation of populations is resulted from the environmental and the genetic effects, and the interactions between them. Whereas, the major factors influencing to the genetic differentiation within populations are the gene flow induced by seed or pollen dispersial, the microsite heterogeneity, and the density-dependent distribution of individuals. For the purpose of studying spatial genetic structure and the distribution pattern of Korean pines(Pinus koraiensis), we set up one $100{\times}100m$ plot at a Korean pine stand in Quercus mongolica community on Mt. Jumbong in Korea. To estimate the coefficient of spatial autocorrelation as Moran's index and an analogue, simple block distance, isozyme markers were analyzed in 325 Korean pines. For 11 polymorphic loci observed in 9 enzyme systems, the average percentage of polymorphic loci, the observed and expected heterozygocity were 72.2% 0.200, and 0.251, respectively. It was revealed the excess of homozygotes was observed in the plot, which suggests that here may be more number of consanguineous trees than expected. On the basis of isozyme genotypes observed in this study, 325 trees were classified into 147 groups in which the maximum number of trees for one group was 34. From the distance class of 24-32m, the genetic heterogeneity began to increase. The variation of simple block distance against the growth performance by tree height and diameter also showed the same trend at 24~32m class. According to high fixation index(F=0.204), the spatial genetic structure within a stand, the analysis of the growth performance, and the distribution patterns of identical genotypes, we inferred that the genetic structure of a Korean pine stand in Mt. Jumbong has been maintained rather density-dependent mechanism than the gene flow, such as the pollen dispersial or the heavy input of seeds following the forest gaps. The genetic patchy size was determined between 24~32m, which suggests that the selection of individuals for the ex situ conservation of Korean pine in Mt. Jumbong may be desirable to be made with the spatial distance over 37 meters between trees.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.665-679
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• 2019

Mesoscale soil moisture measurement from the promising Cosmic-Ray Neutron Probe (CRNP) is expected to bridge the gap between large scale microwave remote sensing and point-based in-situ soil moisture observations. Traditional calibration based on $N_0$ method is used to convert neutron intensity measured at the CRNP to field scale soil moisture. However, the static calibration parameter $N_0$ used in traditional technique is insufficient to quantify long term soil moisture variation and easily influenced by different time-variant factors, contributing to the high uncertainties in CRNP soil moisture product. Consequently, in this study, we proposed a modified traditional calibration method, so-called Dynamic-$N_0$ method, which take into account the temporal variation of $N_0$ to improve the CRNP based soil moisture estimation. In particular, a nonlinear regression method has been developed to directly estimate the time series of $N_0$ data from the corrected neutron intensity. The $N_0$ time series were then reapplied to generate the soil moisture. We evaluated the performance of Dynamic-$N_0$ method for soil moisture estimation compared with the traditional one by using a weighted in-situ soil moisture product. The results indicated that Dynamic-$N_0$ method outperformed the traditional calibration technique, where correlation coefficient increased from 0.70 to 0.72 and RMSE and bias reduced from 0.036 to 0.026 and -0.006 to $-0.001m^3m^{-3}$. Superior performance of the Dynamic-$N_0$ calibration method revealed that the temporal variability of $N_0$ was caused by hydrogen pools surrounding the CRNP. Although several uncertainty sources contributed to the variation of $N_0$ were not fully identified, this proposed calibration method gave a new insight to improve field scale soil moisture estimation from the CRNP.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.322-340
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• 2021

Due to climate change and its consequential spring temperature rise, flowering time of Robinia pseudoacacia has advanced and a simultaneous blooming phenomenon occurred in different regions in South Korea. These changes in flowering time became a major crisis in the domestic beekeeping industry and the demand for accurate prediction of flowering time for R. pseudoacacia is increasing. In this study, we developed and compared performance of four different models predicting flowering time of R. pseudoacacia for the entire country: a Single Model for the country (SM), Modified Single Model (MSM) using correction factors derived from SM, Group Model (GM) estimating parameters for each region, and Local Model (LM) estimating parameters for each site. To achieve this goal, the bloom date data observed at 26 points across the country for the past 12 years (2006-2017) and daily temperature data were used. As a result, bloom dates for the north central region, where spring temperature increase was more than two-fold higher than southern regions, have advanced and the differences compared with the southwest region decreased by 0.7098 days per year (p-value=0.0417). Model comparisons showed MSM and LM performed better than the other models, as shown by 24% and 15% lower RMSE than SM, respectively. Furthermore, validation with 16 additional sites for 4 years revealed co-krigging of LM showed better performance than expansion of MSM for the entire nation (RMSE: p-value=0.0118, Bias: p-value=0.0471). This study improved predictions of bloom dates for R. pseudoacacia and proposed methods for reliable expansion to the entire nation.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.1
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• pp.76-87
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• 2019

The purpose of this study was to evaluate the performance of and to derive future maintenance-management measures of the constructed alternative habitat for the Kaloula borealis at the University of Seoul, examining the period between 2015-2017. The research was constructed in 2014 and in a $191m^2$ area. The performance evaluation was divided into maintaining the habitat of the target species, maintaining the population and reproduction rates of the target species, maintaining the habitat of the wild species, the resilience of natural ecosystems, and the harmony with the surrounding environment. In terms of maintaining the habitat of the target species, soil collected from the existing habitat of the Kaloula borealis and was the depth was increased to 30cm in the alternative habitat. An artificial water supply was required every year during the supporting the spawning and hatching of other amphibians along with the Kaloula borealis. The sources of water of the alternative habitat were both rain and tap water, as it cannot be maintained naturally. Additionally, the Kaloula borealis thrived because it inhabited the research site and the average temperature was $26.2^{\circ}C$ from April-June, which is when the Kaloula borealis spawns. In terms of maintaining the population and reproduction rates of the Kaloula borealis, they were evaluated to have stable rates of reproduction. In terms of maintaining the habitat of the wild species, studies on vegetation and the structure of the characteristics of prey or predators will be needed. Also, alien species, such as Humulus japonicus and Bidens frondosa needed to be removed to maintain the wetland ecosystem of the wild species. In the assessment of the resilience of the natural ecosystems, the mud was monitored, noting the changes in the depth of water, with steps taken to reduce the leakage of water. The mud collected from the Haneul Pond wetland, which is located around the research site was piled up. Also, partial mowing management and the inducement of a natural vegetation colony was required for vegetation management. It was also necessary to create porous spaces, such as old trees and tree branches to create a habitat with hiding places and feeding and spawning places for small organisms. In terms of the harmony with the surrounding environment, the following threat factors needed to be managed: amphibian roadkill by vehicles and pedestrians and artificial draining due to nearby user access. Based on the monitoring results, alternative habitat management measures presented the promoting various waterside structures, in which amphibians can spawn and hide in, managing the water environment consistently, managing the vegetation, focused on the habitat of the wild species, and managing the surrounding environment for the habitat. The creation of an alternative habitat should be managed through monitoring, reflecting the characteristics of the changes in the site. Also continuing efforts are also needed to improve the habitat of the target species.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.235-247
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• 2019

In order to achieve the results that are appropriate for the purpose of the enterprise, it is important to comprehensively understand the behaviors within the roles of the members of the organization and actions outside the roles. However, there have been relatively few studies on corporate social responsibility (CSR) among the existing studies that have been conducted to date. In particular, organizational citizenship behavior, a voluntary commitment by organizational members, is perceived as a very effective way of enhancing corporate performance, but studies on organizational citizenship behavior based on corporate social responsibility have rarely been conducted. In recent years, domestic companies have recognized social responsibility as an activity rather than an additional activity. Therefore, it is very meaningful to look at the organizational performance by examining the factors that make up this social responsibility from the perspective of the company. It is considered a task. In order to demonstrate this, this study collected 303 data from a business incubator center operated by universities and public institutions nationwide and used a total of 303 samples. As a result of the verification, the wages received by the members of the organization did not affect the organizational commitment, and the working hours and the working environment affected the organizational commitment. In addition, corporate social responsibility has an effect on organizational trust in the marketing side. Organizational commitment and organizational trust were studied to positively affect organizational citizenship behavior. In addition, this study divides the degree of perception of social responsibility of the organizational members into the high recognition group and the low recognition group and analyzed whether there is a difference in the level of organizational citizenship behavior according to the employment type (regular and irregular workers) The results of this study are as follows.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.