• Journal of Global Scholars of Marketing Science
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• v.15 no.2
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• pp.161-182
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• 2005

This paper reports on a study testing a model that articulates factors affecting duration of the relationship between exporters and importers. The model posits that affective commitment and calculative commitment influence duration of the relationship. Affective commitment of an importer toward an exporter is hypothesized to be predicted by social satisfaction, which in turn may be predicted by cultural familiarity, perceived similarity, and credibility. In contrast, calculative commitment is hypothesized to be predicted by economic satisfaction, which in turn may be predicted by opportunism and relationship performance. A survey of importers was conducted to test the model. The study results provided support for most of the hypotheses. Theoretical and managerial implications of the study results are discussed too.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.64-73
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• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.767-776
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• 2009

A software birthmark is a set of characteristics that are extracted from a program itself to detect code theft. A dynamic API birthmark is extracted from the run-time API call sequences of a program. The dynamic Windows API birthmarks of Tamada et al. are extracted from API call sequences during the startup period of a program. Therefore. the dynamic birthmarks cannot reflect characteristics of main functions of the program. In this paper. we propose a functional unit birthmark(FDAPI) that is defined as API call sequences recorded during the execution of essential functions of a program. To find out that some functional units of a program are copied from an original program. two FDAPIs are extracted by executing the programs with the same input. The FDAPIs are compared using the semi-global alignment algorithm to compute a similarity between two programs. Programs with the same functionality are compared to show credibility of our birthmark. Binary executables that are compiled differently from the same source code are compared to prove resilience of our birthmark. The experimental result shows that our birthmark can detect module theft of software. to which the existing birthmarks of Tamada et al. cannot be applied.

• Journal of KIISE
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• v.42 no.10
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• pp.1222-1230
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• 2015

Segmentation of the anterior cruciate ligament (ACL) in knee MRI remains a challenging task due to its inhomogeneous signal intensity and low contrast with surrounding soft tissues. In this paper, we propose a multi-atlas-based segmentation of the ACL in knee MRI with locally-aligned probabilistic atlas (PA) in an iterative graph cuts framework. First, a novel PA generation method is proposed with global and local multi-atlas alignment by means of rigid registration. Second, with the generated PA, segmentation of the ACL is performed by maximum-aposteriori (MAP) estimation and then by graph cuts. Third, refinement of ACL segmentation is performed by improving shape prior through mask-based PA generation and iterative graph cuts. Experiments were performed with a Dice similarity coefficients of 75.0%, an average surface distance of 1.7 pixels, and a root mean squared distance of 2.7 pixels, which increased accuracy by 12.8%, 22.7%, and 22.9%, respectively, from the graph cuts with patient-specific shape constraints.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.141-150
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• 2011

Precise image-to-image registration is required to process multi-sensor data together. The purpose of this paper is to develop an algorithm that register between high-resolution optical and SAR images using linear features. As a pre-processing step, initial alignment was fulfilled using manually selected tie points to remove any dislocations caused by scale difference, rotation, and translation of images. Canny edge operator was applied to both images to extract linear features. These features were used to design a cost function that finds matching points based on their similarity. Outliers having larger geometric differences than general matching points were eliminated. The remaining points were used to construct a new transformation model, which was combined the piecewise linear function with the global affine transformation, and applied to increase the accuracy of geometric correction.

• Journal of the Korea Fashion and Costume Design Association
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• v.17 no.3
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• pp.135-148
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• 2015

The purpose of this study was to research into TV makeup types focusing on Korean aesthetics with recognizing importance of Korean thought amid what the global interest pays attention to South Korea thanks to the recent influence of Hanryu(Korean wave). The following are summary and result of this study. The non-dual beauty is an integrative concept of including ambivalence on the categorical difference in sex, culture and class by transcending the extreme binary division. As this is what reflected the open thought of pursuing balance of yin and yang in Korean people, it is being shown in the form of transcendence and far-outness through TV entertainment programs. Transcendence coexisted with maintaining relative relationship beyond separation in sex. Far-outness was pursuing free sensitivity immanent in the non-separated thought of transcending the past, the present and future. This trend is being indicated similarly to a change in its paradigm from separation to convergence these days. Accordingly, even makeup was showing similarity to the recent trend with being paid attention to the makeup of focusing on identity in sex and to the makeup of being coexisted the past, the present and future with the aspect of being mixed space time. The Korean aesthetics has been feeble in its influence compared to the Oriental image of focusing on China and Japan in the meantime. Hence, this study can be said to have significance in a sense of being the first consideration that compared TV makeup types of focusing on Korean aesthetics and suggested its developmental direction.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.61-69
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• 2011

Dynamic Time Warping based on Dynamic Programming is the one of the most widely been used to compare the similarity of two patterns. DTW algorithm has two known problems. The one is singularities. And the another problem is the accuracy of the warping path with patterns. Therefore, this paper suggest the solution for DTW algorithm to use a 2nd derivative operator. Laplacian of Gaussian is a kind of a 2nd derivative operator. Consequently, our suggestion method to apply to this operator, more efficient to solve the singularities problems and to secure a accuracy of the warping path. And the result shows a superior ability of this suggested method.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.101-109
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• 2011

In most of the urbanized cities, socio-economic attributes tend to cluster as patterns of similarity in space, namely spatial autocorrelation, by agglomeration forces. The classical linear regression model, the most frequently adopted in the trip generation step, cannot sufficiently represent this effect. In order to take into account the effect properly, we need a model which adequately deals with the spatial dependence patterns. In this study, the Geographically Weighted Regression (GWR) model is adopted as an alternative method for the local analysis of relationships in multivariate data sets; that is GWR extends this traditional regression framework by estimating local rather than global parameters. This study shows the existence of spatial effects in the production and attraction of home base/non-home based trips through the GWR model using travel data collected in Daegu metropolitan area. Furthermore, LISA is employed to verify the fact that the local spatial autocorrelation exists.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.158-166
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• 2002

In this paper, we proposed a contour shape description method which use the CFR(contour fluctuation ratio) feature. The CFR is the ratio of the line length to the curve length of a contour segment. The line length means the distance of two end points on a contour segment, and the curve length means the sum of distance of all adjacent two points on a contour segment. We should acquire rotation and scale invariant contour segments because each CFR is computed from contour segments. By using the interleaved contour segment of which length is proportion to the entire contour length and which is generated from all the points on contour, we could acquire rotation and scale invariant contour segments. The CFR can describes the local or global feature of contour shape according to the unit length of contour segment. Therefore we describe the shape of objects with the feature vector which represents the distribution of CFRs, and calculate the similarity by comparing the feature vector of corresponding unit length segments. We implemented the proposed method and experimented with rotated and scaled 165 fish images of fifteen types. The experimental result shows that the proposed method is not only invariant to rotation and scale but also superior to NCCH and TRP method in the clustering power.

• Weed & Turfgrass Science
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• v.3 no.1
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• pp.6-12
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• 2014

The survey of weed occurrence was conducted to identify problem weed species in the paddy field. The 320 sites of the 13 regions in Gyeongbuk Province were investigated from June to July, 2013. In the whole region, 51 weed species were identified including 34 annuals and 17 perennials. The highest importance value of annual weed and perennial was dominant 88.4 and 48.7%, in Gumi region, respectively. The most dominant weed species in paddy fields of Gyeongbuk Province were Monochoria vaginalis var. plantaginea (10.80%), followed by Lemna paucicostata (10.74%), Bidens tripartite (8.77%), Echinochloa oryzoides (7.17%), and Scirpus juncoides (6.20%). The similarity of paddy weeds in 13 regions observed through TWINSPAN analysis was distinguished by Ludwigia prostrata, Scirpus juncoides, Lindernia dubia, Polygonum aviculare, and Alopecurus aequalis var. amurensis. The weed species, Ludwigia prostrata, was divided into the four regions, Gyeongju, Gumi(direct seeding cultivation), Youngcheon and Euiseong and ten regions, Gumi, Bonghwa, Sanju, Goryung, Youngdeog, Pohang, Sanju, Andong, Yecheon, and Cheongdo.