Crack Growth Detection on Al/Sicp Using Acoustic Monitoring and Artificial Neural Network

Christopher, C; Sasikumar, T.; Page, Tom

doi:10.1016/j.matpr.2019.05.135

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neuron Model. Artificial neural network is composed of artificial neurons as many basic processing units [9]. To establish an artificial neural network, we must first decide the artificial neuron model.…”

Section: Adaptive Genetic Neural Network Algorithmmentioning

confidence: 99%

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Zhang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.

show abstract

Section: Adaptive Genetic Neural Network Algorithmmentioning

confidence: 99%

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Zhang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Meanwhile, Xu et al [16] and Chen et al [17] developed a prediction ANN model solely from finite element analysis of corroded pipelines data. Additionally, Mahil et al [18] took a different approach using ANN to determine and predict the crack growth in aluminium composite materials and Wen et al [19], proposed a model to evaluate the dependability of corroded pipes using two consecutive inline inspections to anticipate the corrosion rate based on the physical properties of the pipeline. He demonstrates the comparison of the trained ANN model to the Monte-Carlo simulation where the suggested ANN modeling is more beneficial in terms of time analysis and pipelines reliability prediction.…”

Section: Introductionmentioning

confidence: 99%

Burst pressure strength of corroded subsea pipelines repaired with composite fiber-reinforced polymer patches

Muda

Hashim

Kamarudin

et al. 2022

Engineering Failure Analysis

View full text Add to dashboard Cite

Crack Growth Detection on Al/Sicp Using Acoustic Monitoring and Artificial Neural Network

Cited by 2 publications

References 18 publications

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Burst pressure strength of corroded subsea pipelines repaired with composite fiber-reinforced polymer patches

Contact Info

Product

Resources

About