Introduction In the contemporary business environment, fashion companies ought to cope with fundamental changes marketing communication has conventionally been performed. In response to shifting socio-demographic, environmental and market-related conditions, gradually new forms of fashion promotion have evolved (Fill, 2006). Nowadays, the global fashion industry experiences a reduced dependence on mass media advertising and an enlarged reliance on dialogic, relationship-oriented and digitally grounded communication methods (Chitty, Barker, Valos & Shimp, 2012). Against this backdrop, it is irrefutable that social media technologies have been remarkably transforming the ways in which modern-day fashion communication is practiced (Brennan & Schafer, 2010; Funk et al., 2016; Dillon, 2012; Saarinen, Tinnilä & Tseng, 2006). The competitive and widely saturated apparel market is facing an era of intensive proliferation of brands, an epoche of awe bombardment of advertisements, which makes a well-though-out communicational strategy ever more imperative, particularly in a cross-cultural context (Dillon, 2012). Yet, studies that analyze the importance of social media in relation to traditional means of fashion communication are scarce. Even though, empirical introductions start being made to this explicit issue, considerable research deficiency subsists in the realm of cross-cultural fashion communication and social media optimization. Therefore, the rationale of this paper at hand is to contribute to balance out this research gap by providing evidence from four countries.

본 연구에서는 Geant4 application for tomographic emission (GATE) 시뮬레이션 프로그램을 통해 설계 된 male adult mesh (MASH) 팬텀의 영상을 획득한 후 다양한 필터링 인자가 설정된 FNLM 노이즈 제거 알고리즘을 적용함으로써 그에 따른 영상 특성의 경향성을 알아보고자 한다. 이를 위해 GATE 시뮬레이션 프로그램을 통해 인체를 모사할 수 있는 MASH 팬텀을 설계하였다. 또한, 설계된 MASH 팬텀을 기반으로 MAT LAB 프로그램을 통해 복부영상을 획득한 후 0.005의 σ 값을 갖는 Gaussian noise를 추가하여 열화영상을 모델링하였다. 모델링 된 열화영상으로부터 제안하는 FNLM 노이즈 제거 알고리즘의 필터링 인자를 각각 0.005, 0.01, 0.05, 0.1, 0.5, 1.0 으로 설정하여 적용하였으며, 정량적 평가를 위해 FNLM 노이즈 제거 알고리즘이 적용된 영상들로부터 각각의 coefficient of variation (COV), signal to noise ratio (SNR) 그리고 contrast to noise ratio (CNR)을 측정하였다. 결과적으로, 0.05의 필터링 인자가 적용된 영상에서 가장 개선된 COV, SNR 그리고 CNR 값을 보였다. 특히, COV는 설정된 필터링 인자가 증가함에 따라 감소하였으며, 0.05 값 이후부터 거의 일정한 값을 나타내었다. 또한, SNR 및 CNR의 경우 필터링 인자가 증가함에 따라 증가하였으며, 0.05 값 이후부터 감소하는 경향을 보였다. 결론적으로, 열화 영상으로부터 FNLM 노이즈 제거 알고리즘 적용 시 적합한 필터링 인자를 설정해야 함이 증명되었다.

As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

In this paper, we codify the objective function that should be optimized by using Genetic Algorithm instead of Heuristic method to solve these problems. So, each bit that constitutes one structure can signify each commodity. Therefore, we can exchange customers without restriction if the traveling distance diminishes among the districts. Furthermore, even though the capacity of a customer's commodities exceeds that of a vehicle, the following vehicle can be allocated. Also, we obtained good result by testing with real data. To be brief, we can effectively allocate innumerable commodities, that have various magnitudes and weight, into restricted capacity of the vehicle by applying genetic algorithm that is useful in solving the problems of optimization.