• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.105-114
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• 2021

This study determines the differences between Korean female university students and their Chinese counterparts residing in Korea by evaluating the subjective sensation and tactile preference in fabrics for casual shirts. Seven types of white causal shirt fabrics were selected as specimens from those available in spring and autumn shirt collection. The fabrics were made from various fibers (cotton 100%, polyester 100%, flax 100%, polyester 80%/cotton 20%, polyester 65%/cotton 35%, polyester 50%/cotton 45%/spandex 5%, and polyester 50%/modal 50%). Forty Korean and Chinese female university students subjectively assessed the subjective sensation of fabrics used for casual shirts. Participants were asked to observe the seven types of fabrics and complete the questionnaire. The subjective sensation to be assessed for white casual shirt fabrics was classified into the following four factors: smoothness, lightness, softness, and stretchiness. Subjective sensation factors showed partially significant differences by nationality. Korean female students evaluated cotton fabric as being light, whereas their Chinese counterparts evaluated the polyester/cotton/spandex blended fabric as light. Korean female students evaluated polyester/modal blended fabric as being stretchable. The tactile preference for fiber composition of the fabrics showed partially significant differences by nationality. Korean female students preferred cotton 80%/polyester 20% blended fabric, polyester 50%/cotton 45%/spandex 5% blended fabric, and flax 100% fabric. The subjective sensation of the fabrics had different effects on preferences by nationality. Smoothness and softness had positive effects on preferences for the fabrics of Korean students. However, smoothness, softness, and stretchiness had positive effects on the preferences of Chinese students. The subjective sensation and tactile preference for casual shirt fabrics showed a difference between Korean female students and their Chinese counterparts. Therefore, when planning casual shirt fabrics for female university students, it is necessary to reflect on these differences in subjective sensation and tactile preference.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.