GDP Prediction of Liaoning Province Based on Grey Correlation Analysis and GRU Neural Network

Incidence rate of mental illness is increasing year by year with the development of city. The amount of modern medical data is huge and complex. In many cases, it is difficult to realize the rational allocation of resources, which puts forward an urgent demand for the artificial intelligence of modern medicine and brings great pressure to the development of the medical industry. The purpose of this study is to develop and construct a grey correlation analysis and related drug evaluation system of mental diseases based on deep convolution neural network. The establishment of the system can effectively improve the automation and intelligence of modern psychiatric treatment process. In this article, the grey correlation analysis of patient data is carried out, and then, the optimized deep convolution neural network is constructed. Combined with the medical knowledge base, the analysis of disease results is realized, and on this basis, the efficacy of related drugs in the treatment of mental diseases is evaluated. The results show that the advantage of the deep convolution neural network system is to effectively improve the induction rate. What’s more, compared with other algorithms, this algorithm has higher accuracy and efficiency. It improves the comprehensiveness and informatization of disease screening methods, improves the accuracy of screening, reduces the consumption of doctors’ human resources, and provides a theoretical basis for the digitization of the medical industry in the future.

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Grey Correlation Analysis between Urbanization Rate and Household Consumption Structure within Liaoning Province

Proceedings of the 2nd Annual International Conference on Social Science and Contemporary Humanity Development ◽

10.2991/sschd-16.2016.102 ◽

2016 ◽

Author(s):

Xiao-Tong Liang ◽

Wei-Hua Yang

Keyword(s):

Correlation Analysis ◽

Household Consumption ◽

Liaoning Province ◽

Consumption Structure ◽

Grey Correlation ◽

Grey Correlation Analysis ◽

Urbanization Rate

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A Multi-Factor Comprehensive Evaluation Method for Pipeline Integrity

Volume 7: Operations, Applications, and Components ◽

10.1115/pvp2019-93257 ◽

2019 ◽

Author(s):

Jing Yang ◽

Xiaolin Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Correlation Analysis ◽

Evaluation Method ◽

Residual Life ◽

Comprehensive Evaluation ◽

Comprehensive Evaluation Method ◽

Grey Correlation ◽

Grey Correlation Analysis ◽

Artificial Neural

Abstract Pipeline integrity management is widely used as an effective means for pipeline safety management, in which integrity evaluation is an important part. To some extent, pipeline integrity can be interpreted as the safety condition of the pipeline, while safety is an eternal topic for pipeline operators. In numerous recent studies, the evaluation of pipeline integrity generally focuses on the evaluation of remaining strength and/or residual life, which is based on the defect size such as corrosion, dents, etc., obtained during inspection. However, pipeline integrity is not only related to the pipe body, all factors that may threaten the operation safety of the pipe should be considered, including the pipe body, ancillary facilities, the pipe security system, and the surrounding environment, etc.. Although some comprehensive models have been established recently to assess pipeline condition, there still exist limitations for practical application, such as quantification of integrity and complexity of analysis. Therefore this paper presents the development of a comprehensive integrity evaluation method based on multi-factor analysis. The method is developed by an integrated application of fuzzy mathematics, grey correlation analysis theory, and the artificial neural network technique. After establishing integrity evaluating indexes, fuzzy analysis is used to quantify and classify pipeline integrity, and grey correlation analysis to screen key influence indicators. Then a comprehensive predictive evaluation model can be generated using large amount of relevant sample data based on the artificial neural network technique. In the end of the paper, a simple case is applied to validate feasibility of this comprehensive integrity evaluation method. The comprehensive evaluation method is expected to be applied to determine the condition of pipeline integrity, and to grade and rank the integrity condition of pipes, so as to assist and optimize pipeline maintenance decision for pipeline operators.

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