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Peng L. Intelligent landscape design and land planning based on neural network and wireless sensor network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189207] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
From the point of view of urban landscape design objectives, techniques and evaluation, the continuous development of digital information and digital technology has a positive impact on urban landscape design. The core of landscape planning model is to explore various possibilities and determinants of landscape spatial system by changing experimental conditions or parameters.In this paper, the author analyzes the intelligent landscape design and land planning based on neural network and wireless sensor network. The digital generation and construction is through the use of relevant digital technology groups for landscape design and construction activities. Parametric design makes great changes in modern urban landscape planning and design, and has a significant impact on the concept of landscape design, the auxiliary means of design and the construction of landscape entities. It is an indispensable and important link in the process of digital landscape design. Reasonable planning and design of urban landscape can make better use of urban land resources, alleviate the waste of land resources, and optimize the use of resources.
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Affiliation(s)
- Li Peng
- College of Landscape Architecture, Huaihua University, Huaihua, Hunan, China
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Wang J. Application of wavelet transform image processing technology in financial stock analysis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189204] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Traditional mathematical models have problems in the analysis of financial stocks that are not intuitive enough. In order to improve the intuitiveness of the stock forecasting model, based on the image recognition technology, this study normalizes the image and performs feature recognition with grayscale images. At the same time, this paper ignores the small fluctuations and combines the characteristics of stock images to remove the drying process and proposes an algorithm model based on feature recognition. In addition, in order to improve the image accuracy, the model combines the edge extraction technology to extract features, which reflects the actual rise and fall of the stock. Finally, this paper designs experiments to conduct research and analysis. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
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Affiliation(s)
- Juan Wang
- School of Finance and Economics of Xi’an Jiaotong University, China
- School of Economics of Bohai University, China
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