• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.1
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• pp.1-11
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• 1998

The purpose of this paper is twofold : to identify loyalty applicable to segmentation of theme park users and to find characteristics of the segments. Thetheme park was regarded as a product and Lotte World was regarded as a brand. One hundred thirty five college students were selected by nonprobability sampling for two waves thirty of data collection. Both behavioral and attitudinal dimension of loyalty were measured in the first wave by the proportion of visit of the Lotte World to 3 major theme parks for one year, including the Lotte World, and by calculating the mean score of selected 7 attitudinal items, respectively. After 14 weeks, the same respondents were asked the number of actual visits of the Lotte World. Medians of two dimensions and cluster analyses were utilized to classify the respondents into 4 categories : high, spurious, latent, and low loyalty. Then ANOVA and $$\chi$^2$ test of independence were conducted to find the difference in intention to visit the Lotte World and actual visitation of it among groups. Only intention was significantly different by the group and the mean score of intention was highest in the high loyalty group. Although no statistical difference was found in actual visitation among groups, the theory of planned behavior provided a theoretical support to conclude that the loyalty is a useful variable for segmentation of theme park users because intention is an antecedent variable to the behavior. Discriminant analyses showed that characteristics of each loyalty group can be differentiated by motivations and constraints. When median was a group classification criterion, 73.2 percent of high loyalty group was correctly classified. A few comments were suggested on data collection, and inclusion of new discriminant variables was discussed for the future research.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.4
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• pp.354-360
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• 2002

The piezoelectric composites had many advantages in comparison with conventional piezoelectric ceramics and piezopolymers for ultrasonic transducers used in NDT and in medical ultrasionic imaging. The electromechanical coupling coefficient should be high and the acoustic impedance should be low in these applications. However, the cross-coupling with spurious oscillations caused by laterally running plate waves exhibited complex motions in the surface of piezoelectric composites with coarse lateral spatial scale. The thickness mode electromechanical coupling coefficient of 1-3type of piezoelectric compoistes were 0.36 to 0.64, and the acoustic impedance of them were 9.8 to 22.7 MRayl. The lateral resonance frequency of 1-3 type piezoelectric composites shifted to high frequency region with decreasing lateral spatial scale.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.16 no.1
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• pp.14-26
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• 2011

Surface currents were observed by high-frequency (HF) radars off the Keum River estuary from December 2008 to February 2009. The dataset of observed surface currents had data gaps due to the interference of electromagnetic waves and the deteriorating weather conditions. To fill the data gaps an optimal interpolation procedure was developed. The characteristics of spatial correlation in the surface currents off the Keum River estuary were investigated and the spatial data gaps were filled using the optimal interpolation. Then, the temporal and spatial distribution of the interpolated surface currents and the patterns of interpolation error were examined. The correlation coefficients between the surface currents in the coastal region were higher than 0.7 because tidal currents dominate the surface circulation. The sample data covariance matrix (C), spatially averaged covariance matrix with localization ($C^G_{sm}$) and covariance matrix fitted by an exponential function ($C_{ft}$) were used to interpolate the original dataset. The optimal interpolation filled the data gaps and suppressed the spurious data with spikes in the time series of surface current speed so that the variance of the interpolated time series was smaller than that of the original data. When the spatial data coverage was larger (smaller) than 70% of the region, the interpolation error produced by $C^G_{sm}$ ($C_{ft}$) was smaller compared with that by C.