Acknowledgement
이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2019R1F1A105857413).
References
- Ahn, K. (2018). Model-free Optimal Control for Building Energy System using Deep Q-learning, Thesis, Sungkyunkwan University.
- Boden, M, A. (2009). Computer models of creativity. AI Magazine, 30(3), 23-23. https://doi.org/10.1609/aimag.v30i3.2254
- Combes, L. (1976). Packing rectangles into rectangular arrangements. Environment and Planning B: Planning and Design, 3(1), 3-32. https://doi.org/10.1068/b030003
- Choi, J. S. (2011). Voiced-Unvoiced-Silence Detection Algorithm using Perceptron Neural Network. The Journal of the Korea institute of electronic communication sciences, 6(2), 237-242. https://doi.org/10.13067/JKIECS.2011.6.2.237
- Flemming, U. (1978). Wall representations of rectangular dissections and their use in automated space allocation. Environment and Planning B: Planning and Design, 5(2), 215-232. https://doi.org/10.1068/b050215
- Huang, W., & Zheng, H. (2018). Architectural drawings recognition and generation through machine learning.
- Han, S. H., Kang, K. M., Kim, M. S., Oh, C. Y., & Lee, D. H. (2020). Weed Classification in Sweet potato Fields Based on Image-learning. Precision Agriculture, 2(1), 74.
- Jin, C., Xu, M., Lin, L., & Zhou, X. (2018). Exploring BIM Data by Graph-based Unsupervised Learning, International Conference on Pattern Recognition Applications and Methods, 582-589.
- Kim, Y., Heo, K., You, G., Lim, H., Choi, Jung., Ku, K., Eom, J., & Jeon, Y. (2018). A Study on the Improvement of Heat Energy Efficiency for Utilities of Heat Consumer Plants based on Reinforcement Learning, Korean Society for Energy, 27(2), 26-31.
- Kim, H., Ji, S., & Jun, H. (2019). A Study on Application of Artificial Intelligence Technology to BIM Architectural Planning - Focus on Structural BIM Model in early Design Phase -, Study for Formative Art Design of Character, 22(4), 229-242.
- Kwon, O., & Cho, J. (2019). Space Usage Knowledge Extraction from BIM Data by Decision Tree and Expert System, Korean Journal of Computational Design and Engineering, 24(2), 126-134. https://doi.org/10.7315/cde.2019.126
- Kwon, O., & Cho, J. (2020). Quantitative Comparison of BIM Architectural Space Designs by Decision Tree and Expert System, Korean Journal of Computational Design and Engineering, 25(1), 36-44. https://doi.org/10.7315/cde.2020.036
- Kim, Y. (2021). Optimal Control of Lighting System based on Illuminance Prediction Model with Deep Deterministic Policy Gradient (DDPG), Thesis, Seoul National University.
- Lee, H, J. (1988). Systematic Design Methodology of Architectural Planning, Journal of the Architectural Institute of Korea, 32(3), 4-6.
- Lee, S., & Lu, N. (2020). A Methodology of Enhancing the Accuracy of Image Classification with CNN, Journal of the Architectural Institute of Korea, 36(9), 15-22. https://doi.org/10.5659/JAIK.2020.36.9.15
- Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236
- Park, I., & Kim, J. (1990). the phases of development and the contents of paradigms in the architectural design methodology, Journal of the Architectural Institute of Korea, 10, 95-101.
- Park, H. (1991). Design Process in Architecture (2), Journal of the Architectural Institute of Korea, 35(5), 61-66.
- Schiller, E. (2018). Creating Novel Architectural Layouts With Generative Adversarial Networks. Master's thesis, Harvard University.
- Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine learning, 8(3-4), 279-292. https://doi.org/10.1007/BF00992698
- Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
- Yoshimura, Y, Cai, B., Wang, Z., & Ratti, C. (2019). Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence, International Conference on Computers in Urban Planning and Urban Management. Springer, Cham, 249-265.