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RISS 인기검색어
- 검색키워드
- 저자 : Yabuki Nobuyoshi (검색결과 4 건)
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An indoor thermal environment design system for renovation using augmented reality
Tomohiro Fukuda,Kazuki Yokoi,Nobuyoshi Yabuki,Ali Motamedi 한국CDE학회 2019 Journal of computational design and engineering Vol.6 No.2
The renovation projects of buildings and living spaces, which aim to improve the thermal environment, are gaining importance because of energy saving effects and occupants’ health considerations. However, the indoor thermal design is not usually performed in a very efficient manner by stakeholders, due to the limitations of a sequential waterfall design process model, and due to the difficulty in comprehending the CFD simulation results for stakeholders. On the other hand, indoor greenery has been introduced to build-ings as a method for adjusting the thermal condition. Creating a VR environment, which can realistically and intuitively visualize a thermal simulation model is very time consuming and the resulting VR envi-ronment created by 3D computer graphics objects is disconnected from the reality and does not allow design stakeholders to experience the feelings of the real world. Therefore, the objective of this research is to develop a new AR-based methodology for intuitively visualizing indoor thermal environment for building renovation projects. In our proposed system, easy-to-comprehend visualization of CFD results augment the real scenes to provide users with information about thermal effects of their renovation design alternatives interactively. Case studies to assess the effect of indoor greenery alternatives on the thermal environment are performed. In conclusion, integrating CFD and AR provides users with a more natural feeling of the future thermal environment. The proposed method was evaluated feasible and effective.
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Zhang Jiaxin,Fukuda Tomohiro,Yabuki Nobuyoshi 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.5
The extraction and integration of building facade data are necessary for the development of information infrastructure for urban environments. However, existing methods for parsing building facades based on semantic segmentation have difficulties in distinguishing individual instances of connected buildings. Manually collecting and annotating instances of building facades in large datasets is time-consuming and labor-intensive. With the recent development and use of city digital twins (CDTs), massive high-quality digital assets of buildings have been created. These assets make it possible to generate high-quality and cost-effective synthetic datasets that can replace real-world ones as training sets for the supervised learning-based instance segmentation of building facades. In this study, we developed a novel framework that can automatically produce synthetic datasets from a CDT. An auto-generation system for synthetic street views was built by rendering city digital assets in a game engine, while the system auto-generated the instance annotations for building facades. The hybrid dataset HSRBFIA, along with various subsets containing different proportions of synthetic and real data, were used to train deep learning models for facade instance segmentation. In our experiments, two types of synthetic data (CDT-based and virtual-based) were compared, and the results showed that the CDT synthetic data were more effective in boosting deep learning training with real-world images compared with the virtual synthetic data (no real-world counterparts). By swapping a certain portion of the real data with the proposed CDT synthetic images, the performance could almost match what is achievable when using the real-world training set.
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Kikuchi Takuya,Fukuda Tomohiro,Yabuki Nobuyoshi 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.5
The objective of this research is to develop a method to detect and virtually remove representations of existing buildings from a video stream in real-time for the purpose of visualizing a future scenario without these buildings. This is done by using semantic segmentation, which eliminates the need to create three-dimensional models of the buildings and the surrounding scenery, and a generative adversarial network (GAN), a deep learning method for generating images. Real-time communication between devices enables users to utilize only portable devices equipped with a camera to visualize the future landscape onsite. As verification of the proposed method’s usefulness, we evaluated the complementation accuracy of the GAN and real-time performance of the entire method. The results indicated that the process is completed accurately when the area to be complemented is less than 15% of the view and that the process runs at 5.71 fps. The proposed method enables users to understand intuitively the future landscape and contributes to reducing the time and cost for building consensus.
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Kikuchi Naoki,Fukuda Tomohiro,Yabuki Nobuyoshi 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.2
The integration of augmented reality and drones allows past and future landscapes to be visualized from an aerial perspective. However, these visualizations still suffer from the occlusion problem, where the three-dimensional (3D) virtual model displayed in the real world is in front of a real-world object. Currently, city digital twins are essential for the sustainable development of cities and the development of detailed 3D models of cities. By visualizing the city digital twin, augmented reality can facilitate the participation of nonexpert citizens in the decision-making process of urban design, but research examples are limited. Here, using detailed city 3D models, we develop a digital-twin approach to outdoor augmented reality with occlusion handling for both first-person and bird’s-eye views. In a verification experiment, the occlusion handling accuracy of the prototype system was measured to be about 0.8 using intersection over union. The frame rate of the entire prototype system was about 30 fps, and the delay between the controller and the augmented reality device was about 3 s. The internet-based system architecture was developed to integrate augmented reality and drone systems. Our system allows multiple stakeholders involved in building construction projects to observe aerial perspectives of those projects, both on-site and off-site via an internet browser, using augmented reality with occlusion handling.
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