903 resultados para STM images
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1854/07/01 (N144).
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1851/10/24 (N4).
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1853/03/26 (N78).
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Référence bibliographique : Rol, 60687
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Despite China's rapid growth in inbound tourism, the nature of its Canadian tourist market has been insufficiently studied. In response to this need, the objectives of this study are to identify China's destination image in Canadian students' minds, their possible internal motivations for visiting China as well as examining demographic influences on people's destination image formation. The study reviews image formation process and travel motivation categorisation, discusses their relationship, and implements Baloglu and McCleary's (1999) perceptual and affective image formation model and "push and pull factors" theory as its framework. A self-administered survey was applied to 424 undergraduate students in a Canadian university in early 2004. Exploratory factor analyses were conducted to identify perceived images and travel motivation. Summated means were calculated to illustrate the affective attitudes. A series of f-test and ANOVA tests were employed to examine the influence of demographics. An open-ended question format was adopted to analyse other images, motivations and visitation barriers that students may have. Findings demonstrate that cultural and natural attractions are the predominant image which the Canadian students have of China'; some stereotypes and negative images still influence the students' perception; travel service quality is largely unknown; increasing knowledge and seeking excitement and fun are the significant motivators in the likelihood of the Canadian students choosing to visit China; and personal interests may be a factor that significantly influences an individual's destination image and travel motivation. Raising awareness and increasing familiarity through promotion are suggested as methods to create a positive destination image of China.
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This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.
