Finite element method for stress and strain analysis of FGM hollow cylinder under effect of temperature profiles and inhomogeneity parameter

This article aims at improving the design effect of garden landscape space environment and optimizes the structure of garden landscape space environment. An optimization design method of garden landscape space environment based on interactive genetic algorithm is proposed in this article by designing a landscape space environment design with image visual feature space distributed monitoring model and fuzzy pixel area feature fusion reconstruction model. The landscape space environment design of the image by multistage decomposition and pixel gray characteristics has been established landscape space environment design image visual feature reconstruction model. The method proposed in this article combines the block area template matching method with landscape space environment design of the image features visual reconstruction. The visual space distributed detection is done with information fusion using the similarity model reconstruction for landscape space environment design of image visual perception process of information fusion. In order to extract fuzzy characteristic landscape space environment design of the image, interactive genetic algorithm is used to realize the quality assessment of landscape art information fusion perception and visual reconstruction. The simulation results show that compared with the traditional method, the visual reconstruction quality of landscape spatial environment design image processed by this method is better along with higher image recognition accuracy, and the output signal-to-noise ratio is improved by 14.6%. The experimental results prove that the introduction of interactive genetic algorithm in landscape planning and design can effectively solve the problems of multi-level feature decomposition and pixel feature separation in the process of landscape design. The proposed method achieves better optimization of landscape spatial environment structure, and achieves good landscape spatial environment design effect.Povzetek: Za snovanje prostorskih krajinskih načrtov, npr. vrtov, je uporabljen interaktivni genetski algoritem.

Section: B Image Feature Fusion and Reconstruction Modelmentioning

confidence: 99%

Application of interactive Genetic Algorithm in landscape planning and design

Li¹,

Sharma²

2022

“…Therefore, the quality control in the design stage of the project is mainly responsible by the design unit and the construction unit. The work content of quality control in the design stage mainly includes two aspects: the control of quality standards adopted by the project and the control of design work quality itself [24,25].…”

Section: The Refined Construction Management Integrated With Bim Tech...mentioning

confidence: 99%

Intelligent engineering management of prefabricated building based on BIM Technology

Feng

Zhang

et al. 2022

This article solves the problem of China's construction industry adopted by the traditional extensive construction mode for a long time. The traditional methods was falling behind as they have the largest number of accidents among various types of safety accidents in the construction industry. This paper puts forward a new mode of fine construction management based on BIM. This article depicts the experimental analysis considering 277 accidents of falling from height, accounting for 54% of the total. There were 72 collapse accidents, ranking second among all types of safety accidents in the construction industry. It further discusses the application measures and benefits of BIM Technology in fine management from four aspects of quality management, schedule control, cost management and safety management. It is demonstrated from experimentation that BIM Technology has brought good economic and social effects to aid fine management.Povzetek: S strojnim učenjem in pomočjo BIM tehnologije so bile analizirane nesreče v kitajskem gradbeništvu.

“…Railway signal equipment safety is an important part of ensuring railway traffic safety, thus, it is very necessary to study a system that can diagnose the fault of railway signal equipment according to the actual situation. The literature suggests that there are many studies on using data synthesis method to solve the sample imbalance based on the deep learning of artificial intelligence approach [12][13][14][15]. This paper diagnoses the fault of high-speed railway signal equipment, improves the performance of equipment fault diagnosis, so as to improve the safety of railway.…”

Section: Related Workmentioning

confidence: 99%

“…According to formula (13) and formula ( 14), the lower the error proportion of neural network in category classification, the better classification performance it has in this category, the higher the category weight will be allocated. Then add the overall weight of the neural network and the category weight according to equation (15) to recalculate the predicted value of the neural network in each category. This combined weighted integration method can avoid the influence of few values and extreme values in the integration method.…”

Section: Deep Learning Integrated Fault Diagnosis Modelmentioning

confidence: 99%

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Lian

Wang

Sharma

et al. 2022

The rapid development of railway transportation towards high speed, high density and heavy load has led to even higher requirements for the safety of railway signal equipment. The safety of railway signal equipment is an important part of ensuring railway traffic safety, thus, it is very necessary to study a system that can diagnose the fault of railway signal equipment according to the actual situation. This article utilizes the deep learning algorithm of artificial intelligence for investigating the interlocking faults in the railway transportation. This paper uses ADASYN data synthesis method to synthesize few category samples, uses TF-IDF to extract features and transform vectors, and proposes a deep learning integration method based on combined weight. The results show that BiGRU has better overall classification performance when evaluated on the index of primary and secondary fault classification accuracy. The classification accuracy improvement of 5% is achieved for primary fault classification and the comprehensive evaluation index of secondary fault classification is improved by about 9%. It was revealed that when compared with ADASYN + BiLSTM neural network, the comprehensive evaluation index of primary fault classification accuracy is improved by about 6%, and the comprehensive evaluation index of secondary fault classification is improved by about 10%. It is demonstrated that deep learning integration is an effective method to improve the classification performance of turnout fault diagnosis model.Povzetek: Za železniški sistem je bila uporabljena metodologija globokih nevronskih mrež za iskanje napak v signalih.