In this paper we present a mobile augmented reality (MAR) application supporting teaching activities in interior design. The application supports students in learning interior layout design, interior design symbols, and the effects of different design layout decisions. Utilizing the latest AR technology, users can place 3D models of virtual objects as e.g., chairs or tables on top of a design layout plan and interact with these on their mobile devices. Students can experience alternative design decision in real-time and increases the special perception of interior designs. Our system fully supports the import of interior deployment layouts and the generation of 3D models based on design artefacts based on typical design layout plan design symbols and allows the user to investigate different design alternatives. We applied John Keller’s Attention, Relevance, Confidence, and Satisfaction (ARCS) learning motivation model to validate our solution to examine the students’ willingness and verify the ability of students to improve learning through MAR technology. We compared a sample experimental group of N = 52 test-subjects with a sample of N = 48 candidates in a control group. Learning indicators as learning interest, confidence, satisfaction and effective have been utilized to assess the students’ learning motivation through the use of MAR technology. The learning results have been determined by the independent sample t testing. The significance of the post-test had a p-value < 0.05 difference. The result of the study clearly shows that the reference group utilizing MAR technology as a learning aid show a higher learning effectiveness as the control group. Thus, we conclude that MAR technology does enhance students’ learning ability for interior design and making appropriate design decisions.

Capturing clear images in underwater environments is a major challenge in marine engineering. There are many issues to consider in obtaining clear underwater images such as climate, environment, and human factors. The most important reasons are the atomization effect caused by dispersion and the color cast caused by inconsistent energy attenuation of each wavelength when light propagates in water. Recently, deep learning technology has shown impressive performance on underwater image enhancement. The deep learning-based methods apply to the underwater image enhancement tasks. We propose a deep learning model for inferring a degradation model to further improve image dynamic range through a network-guided underwater image enhancement network architecture with multicolor space embedding and convolutional media transfer, fixed an issue with limited dynamic range and brightness in underwater images. Quantitative and qualitative results show that our network performs relatively well in the Underwater Image Enhancement Benchmark (UIEB) [7] dataset compared to other recent methods, and is expected to be applied to different types of underwater work and environments in the future and reduce the degradation problems that often occur with underwater images. The acquisition of high-fidelity imagery in subaqueous environments presents significant technical challenges in marine engineering, encompassing a complex interplay of climatological variables, environmental parameters, and anthropogenic factors. Primary impediments to image clarity comprise the atomization phenomenon induced by optical scattering and chromatic distortion resulting from wavelength-dependent energy attenuation in aqueous media. The procurement of high-resolution underwater imagery is fundamental to numerous scientific applications, including marine biological research, autonomous underwater robotics, and environmental surveillance systems, where precise visual data acquisition substantially augments analytical efficacy. Contemporary developments in deep learning architectures have exhibited remarkable potential for enhancing underwater image quality. In response to these challenges, we present a novel deep learning framework that derives an empirical degradation model, utilizing a network-guided enhancement architecture incorporating multicolor space embedding and convolutional media transfer methodologies to optimize image dynamic range. This methodological approach specifically addresses the limitations in luminance distribution and dynamic range characteristics inherent in subsea imagery. Empirical evaluation of our architectural framework on the standardized Underwater Image Enhancement Benchmark (UIEB) [7] dataset demonstrates statistically significant performance improvements over contemporary methodologies, suggesting broad applicability across diverse submarine environments for mitigating common degradation phenomena.

This purpose of this article is to explore the case for the integration of art, aesthetics, and the atmosphere of local life into hotel interior designs. We used statistical empirical methods to verify whether the transformation of cultural elements into tourism products can really bring tourists to feel cultural meaning. In this study, we use the “Movement of meaning” model proposed by McCracken as a validation of how consumers feel the effects of cultural learning through the consumption of hotel stays. This research focuses on the integration of local culture and hotel consumption in the part of cultural learning, and particularly on the second stage of the meaning trajectory: hotel tourists feel the cultural meaning transfer when they consume and stay. In order to objectively investigate the influence of cultural products on guest consumption experience, we asked more than 187 hotel guests for their data to understand their perceptions of artistic experience and cultural meaning through questionnaires. The contribution of our research is to provide a framework for testing the validity of cultural meanings transformed into commodity consumption for tourism. The value of this research lies in our empirical research on how people perceive the beauty of local culture, and how the combination of cultural elements and hotel design allows customers to experience the cultural meaning benefits. The quantitative verification method of this research for the “meaning movement” model can be used as an operational procedure for tourism relative research, especially for verifying the effectiveness of cultural meaning transmission by integrating culture into tourism products.

In this research, we propose innovative ideas for digital art combined with educational applications for the library. It also presents the library’s information services to be more humane and interesting. By taking interactive devices as the basis, this research integrates college library borrowing data and an interactive artwork in order to perform an information visualization service for a user in the library by using their own smart phone. This research uses an instant data operation to carry out the information inquiry applications to present three years of library borrowing data, from 2013 to 2015, obtained from the Hsih Shin University library Data Access Protocol. Finally, a usability evaluation for the interactive art is conducted. To testify that this interactive art design meets the principle of user acceptance, a questionnaire survey is used for the statistical verification. The research findings show that a positive explanation value was achieved for user acceptance. The research model proposed by this research acts as a usability evaluation of the integrated application of an interactive artwork and library information service. The research model also corresponded to the user’s emotional feedback in the HCI research field. The value of this research is that we provide usability-verified models for cross-domain applications.

Capturing clear images in underwater environments is a major challenge in marine engineering. There are many issues to consider in obtaining clear underwater images. Such as climate, environment and human factors. The most important reasons are the atomization effect caused by dispersion and the color cast caused by inconsistent energy attenuation of each wavelength when light propagates in water. We propose a deep learning model for inferring a degradation model to further improve image dynamic range through a network-guided underwater image enhancement network architecture with multicolor space embedding and convolutional media transfer. Fixed an issue with limited dynamic range and brightness in underwater images. Quantitative and qualitative results show that our network performs relatively well in the Underwater Image Enhancement Benchmark (UIEB) dataset compared to other recent methods, and is expected to be applied to different types of underwater work and environments in the future. Image enhancement. And reduce the degradation problems that often occur with underwater images.