Parsimonious design of pattern recognition systems for slope stability analysis

Ospina-Dávila, Y. M.; Orozco-Alzate, Mauricio

doi:10.1007/s12145-019-00429-5

Cited by 7 publications

(4 citation statements)

References 55 publications

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“…The two randomly selected individuals are weighted by their vector differences and added to the third individual. If the sliding surface is planar, a small safety factor will also be obtained, which is confused with finding dangerous sliding surfaces, which will affect the calculation results, so it must be eliminated [7,8]. The calculation results and time analysis of the three optimization algorithms are shown in Table 1.…”

Section: Intelligent Optimization Algorithmmentioning

confidence: 99%

Application of Intelligent Optimization Algorithm in Slope Engineering Stability

Yu,

2023

Advances in Transdisciplinary Engineering

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Slope stability analysis is a very important part of engineering design, which is related to the safety of the construction process. In this paper, the lag effect of intelligent algorithms in solving landslide thrust and the characteristics of slope deformation are studied in depth. The purpose of studying slope engineering stability in this paper is to apply theoretical research to practical operation, so as to reduce unnecessary hazards. This paper mainly applies qualitative analysis and comparative analysis to control the variable of single and double row spacing, so as to explore the spacing of piles in slope engineering to determine the stability. The experimental data shows that when the pile spacing is 2 m, the stability of slope engineering can be strengthened.

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Section: Intelligent Optimization Algorithmmentioning

confidence: 99%

Application of Intelligent Optimization Algorithm in Slope Engineering Stability

Yu,

2023

Advances in Transdisciplinary Engineering

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“…e reason why fractal pattern has a strong visual impact is that each part of the graph is infinitely subdivided according to self-similarity. When the viewer is far sighted, he sees an overall flowing beauty [9][10][11]. If viewers look closely, they can see the visual beauty with rich details.…”

Section: Infinite Ductilitymentioning

confidence: 99%

CAD Fractal Art Pattern Design and Evaluation Based on RBF Network Algorithm

Liu

Zhu

2022

Advances in Multimedia

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In order to effectively solve the problems of unreasonable design and low evaluation level in the process of fractal art pattern design and evaluation, a CAD fractal art pattern design and evaluation is proposed based on RBF network algorithm. Through the corresponding sequence sorting of RBF network algorithm, the image surface with the same CAD fractal art pattern design and evaluation attributes is divided into two parts. The main shape in the algorithm is mainly the node of feature tree. According to the later stage of image features, using high-level design basic knowledge and evaluation methods, RBF network algorithm is introduced into the process of original image design modification. Finally, the experimental results show that the algorithm used in this paper has certain advantages in CAD fractal art pattern design and evaluation.

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“…This method uses RBF networks to produce and analyse fractal art patterns, emphasising the culture-related function of the patterns through the application of connotation and quality in the patterns, in addition to the measures and processes that are employed in the production of culture. This strategy may be valuable to designers in certain situations [ 21 ]. This article investigates the process of creating fractal art patterns and then generates and evaluates fractal art patterns that embody the essence of cultural identity as a whole.…”

Section: Introductionmentioning

confidence: 99%

The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching

Xiao

Luo

2022

Computational Intelligence and Neuroscience

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The rapid growth of artificial intelligence technology has been deployed in art teaching and learning. Radial basis function (RBF) networks have a completely different design compared to most neural network architectures. Most neural networks consist of multiple layers that can introduce nonlinearity by repetitive application of nonlinear activation functions. In this research, people will study the application of the RBF neural network model based on deep learning in flower pattern design in art teaching. The image classification process is finding and labeling groups of pixels or vectors inside an image based on rules. Deep learning is a type of machine learning that uses artificial neural networks to replicate the structure and function of the human brain. The proposed model uses the RBF neural network-based deep learning model in flower pattern design in art teaching and provides efficient results.

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Parsimonious design of pattern recognition systems for slope stability analysis

Cited by 7 publications

References 55 publications

Application of Intelligent Optimization Algorithm in Slope Engineering Stability

Application of Intelligent Optimization Algorithm in Slope Engineering Stability

CAD Fractal Art Pattern Design and Evaluation Based on RBF Network Algorithm

The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching

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