Intelligent computer technology and its application in environmental art design

被引：0

作者：

Zhang H. ^{[1
]}

机构：

[1] Wuchang Institute of Technology, Hubei Province, Wuhan City

来源：

International Journal of Information and Communication Technology | 2024年 / 24卷 / 02期

关键词：

computer technology; design; environmental art; simulation;

D O I：

10.1504/IJICT.2024.137222

中图分类号：

学科分类号：

摘要：

To improve the effect of artistic environment design, this paper applies intelligent computer technology to environmental art design, and combines intelligent computer technology to provide mathematical analysis tools required for pre-computing optical energy transmission technology. Moreover, this paper discusses the diffuse and specular reflections at low frequencies, and conducts a detailed study of the rendering algorithm of the pre-computed radiance transfer (PRT) technology. In order to reconstruct the approximate function, this paper considers the opposite process of projection, and the SH function with scaled coefficients can be accumulated to approximate the original function. In addition, this paper constructs a simulation system for environmental art design. It is not difficult to see from the design evaluation results that the environmental art design system proposed in this paper can effectively improve the effect of environmental art design. This unifies the functionality of the colouring system, enabling people to handle more types of elements and flexibly utilise graphic pipelines. Copyright © The Author(s) 2023. Published by Inderscience Publishers Ltd.

引用

页码：213 / 227

页数：14

共 16 条

[1]

Bibri S.E., On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review, Journal of Big Data, 6, 1, pp. 1-64, (2019)

[2]

Gamache R., Kharrazi H., Weiner J.P., Public and population health informatics: the bridging of big data to benefit communities, Yearbook of Medical Informatics, 27, 1, pp. 199-206, (2018)

[3]

Ghernaout D., Aichouni M., Alghamdi A., Applying big data in water treatment industry: a new era of advance, International Journal of Advanced and Applied Sciences, 5, 3, pp. 89-97, (2018)

[4]

Gupta S., Altay N., Luo Z., Big data in humanitarian supply chain management: a review and further research directions, Annals of Operations Research, 283, 1, pp. 1153-1173, (2019)

[5]

Kang G.K., Gao J.Z., Chiao S., Lu S., Xie G., Air quality prediction: big data and machine learning approaches, International Journal of Environmental Science and Development, 9, 1, pp. 8-16, (2018)

[6]

La Sorte F.A., Lepczyk C.A., Burnett J.L., Hurlbert A.H., Tingley M.W., Zuckerberg B., Opportunities and challenges for big data ornithology, The Condor: Ornithological Applications, 120, 2, pp. 414-426, (2018)

[7]

Lv Z., Song H., Basanta-Val P., Steed A., Jo M., Next-generation big data analytics: state of the art, challenges, and future research topics, IEEE Transactions on Industrial Informatics, 13, 4, pp. 1891-1899, (2017)

[8]

Manogaran G., Lopez D., A survey of big data architectures and machine learning algorithms in healthcare, International Journal of Biomedical Engineering and Technology, 25, 2–4, pp. 182-211, (2017)

[9]

Mavragani A., Tsagarakis K.P., Predicting referendum results in the big data era, Journal of Big Data, 6, 1, pp. 1-20, (2019)

[10]

Maya-Gopal P.S., Chintala B.R., Big data challenges and opportunities in agriculture, International Journal of Agricultural and Environmental Information Systems, 11, 1, pp. 48-66, (2020)

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