Browsing by Author "Wang, Shih-Yuan"

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Spatial Glass Bonds Computation and fabrication system of complex glass structure
(2019-01-01)
Sheng, Yu-Ting
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Wang, Shih-Yuan
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Li, Mofei
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Chiu, Yu-Hung
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Lu, Yi-Heng
This paper introduces an adaptive robotic spatial aggregation system for the development of an intricate self-supporting glass structure. Rather than using discrete and standardized building elements in the design and fabrication process, this research focuses on utilizing a non-arbitrary shape as an aggregated material for autonomous robotic assembly. More specifically, this paper presents an adaptive robotic fabrication pipeline that measures the size of hollow glass balls (inaccurate materials) as fabrication units to aggregate the entire glass structure. Ultraviolet (UV) curing adhesive is used as the bond between each glass element. Thus, through the live robotic programming as well as various combinations of spherical glass objects and UV curing adhesives/devices, the entire glass structure is self-supported. The project is aimed not only at the development of algorithms and a robotic fabrication system, but also the exploration of the aesthetics of glass materials. In other words, this project investigates a flexible and adaptable framework in response to live sensor data for the design and fabrication of nonstandard spatial structures aggregated out of discrete spherical glass elements, and it further explores glass material aesthetic and perception of architecture.
Who Is the Designer? ARC-100 Database and Benchmark on Architecture Classification
(2020-01-01)
Huang, Yen-Chang
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Wang, Shih-Yuan
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Liong, Sze-Teng
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Huang, Chieh-En
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Hsieh, Yi-Chen
Architecture is about evolution, there exist many types of architectural styles that depend on the geography, traditions, and culture of the particular regions. An architectural designer may have a similar preference in creating the new architectural building, which can be easily recognized from the physical attributes and characteristics. This paper performs an architect classification based on the outward appearance of the building. An architecture database with 100 images (ARC-100) that have balanced class distribution is constructed. Among the architectural buildings, the best performance is 71% for 5-class classification. Convolutional neural networks (CNNs) have demonstrated breakthrough performance on various classification tasks in recent studies, and even outperform human experts in specific tasks. Thus, for the baseline evaluation, multiple pretrained CNN models are employed with slight modifications. Prior to the feature extraction and classification processes, the removal of background noise is performed using two approaches: manually and automatically. The former approach requires high human intervention, while the latter utilizes the cutting-edge object segmentation technology, namely mask regional convolutional neural network (R-CNN). The illustration of the experiment training progress and the confusion matrix are reported, to allow further interpretation and analysis for the model trained. Notably, this is the first work that performs automatic classification based on architectural styles. This framework can be used to improve the cultural understanding and practices in providing education for holistic development and enhance the learning experience and progressions from an aesthetic perspective. (C) 2020 The Authors. Published by Atlantis Press B.V.