Research on vibration effect of tunnel blasting based on an improved Hilbert–Huang transform

Zhao, Yan; Shan, Ren Liang; Wang, Hai long

doi:10.1007/s12665-021-09506-9

Cited by 42 publications

(21 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to clearly obtain the information contained in the color signal, it is necessary to perform signal processing on the signal. At present, the analysis methods for such nonstationary signals mainly include Fourier transform, wavelet analysis, wavelet packet analysis [13,14], and Hilbert transform [15,16]. Among them, the Fourier transform is mainly suitable for linear steady-state signals.…”

Section: Color Feature Retrieval Of Movie Animationmentioning

confidence: 99%

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Gong

2022

Security and Communication Networks

View full text Add to dashboard Cite

Video artworks are closely linked with the development of contemporary technology. Therefore, it is widely used in various fields of social life. Video art has become one of the main media forms of contemporary art. In the practice of art teaching, how to combine the existing content of traditional art teaching with video technology and how to understand the inner connection between traditional aesthetics and technological aesthetics have become issues that workers in the new era must think about and pay attention to. As a typical case of influencing works of art, movie animation is loved by the majority of young people. In order to quantify the application effect of machine learning in video art and film animation text mining, this paper conducts prediction research and analysis on several main aspects of color features involved in film animation. By introducing three typical machine learning methods, this paper analyzes the distribution law of the color features of film animation from the perspective of machine learning and its influence on artistic texts. Specifically, the paper uses machine learning methods as a carrier to predict the performance of multiple main modules of color features in movie animation. The prediction results show that the square of the correlation coefficient corresponding to the extreme learning machine is the largest, and the root mean square error, the mean absolute percentage error, and the median absolute error are the smallest, which shows that the extreme learning machine has the best prediction effect. Therefore, it corresponds to the best prediction. In addition, the comparison between the predicted data and the measured data shows that the relationship between the two is approximately a linear function of y = x. At the same time, the fitting calculation shows that the predicted data corresponding to the two main modules of the main color and the color structure in the color feature exhibit a good functional relationship of polynomial functions.

show abstract

Section: Color Feature Retrieval Of Movie Animationmentioning

confidence: 99%

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Gong

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Particle swarm optimization algorithm [16,17] is used to simulate various biological social behaviors such as biological reproduction and upgrading. is artificial intelligence algorithm consists of a finite number of particles that are unrelated to each other.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Modeling and Analysis Method of National Fitness Big Data for Basketball Projects Based on a Multivariate Statistical Model

Zhao

2022

Security and Communication Networks

View full text Add to dashboard Cite

How to start from the fitness needs of people and effectively improve the precision of the supply of public fitness services everyone is an important issue that needs to be solved first at the current stage. This requires us to proceed from the reality, conduct accurate research, and find a method that can match the current problem. In this paper, taking basketball projects in national fitness as an example, by introducing a proposition about the development of small basketball events, the corresponding big data modeling and analysis methods are studied. The research methods and research objectives involved in this paper are based on the relevant parameters of the multivariate statistical model. First, the article introduces the calculation principle of the multiple linear regression model. We introduce the concept of variance inflation factor involved in this principle and carry out the modeling and analysis of big data based on this variable. In order to illustrate the application effect of big data in this kind of research, this paper introduces three different big data technologies, including immune selection optimization algorithm, particle swarm optimization algorithm, and Elman neural network, to predict and analyze the variance inflation factor (VIF) corresponding to the small basketball project. The analysis results show that the Elman network exhibits certain advantages in terms of computing convergence time. And, as the number of calculation steps increases, the superiority of the Elman network is more obvious. As far as the prediction performance is concerned, the square of the correlation coefficient corresponding to the immune selection optimization algorithm is the largest and the sum of the squares of the residuals is the smallest, showing superior prediction performance.

show abstract

“…With the rapid development of science and technology such as cloud computing, big data [22,23], "Internet of ings+" and artificial intelligence [24,25], artificial intelligence-based teaching optimization [26][27][28], teaching management, real-time tracking and evaluation of teaching process, educational resources, and technical services have become an important part of the development of modern education.…”

Section: Introductionmentioning

confidence: 99%

Smart Teaching Design Mode based on Machine Learning and its Effect Evaluation

Su¹

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the continuous progress of science and technology, the mode of smart teaching is more and more applied in the actual teaching process. Under the intelligent teaching system mode, teachers and students can jointly complete the construction of curriculum resources and classroom teaching tasks. This paper evaluates its application effect by introducing two examples of smart teaching. Among them, the evaluation effect of the academic teaching network system shows that the teaching network system based on machine learning is composed of three parts: model selection, data preparation, and modeling prediction. Moreover, it can greatly improve the prediction performance of the intelligent algorithm in the teaching mode by introducing the oversampling technology SMOTE algorithm (Synthetic Minority Oversampling Technique). To further verify the advantages of the smart teaching model, an example of social sciences teaching is introduced. The results show that the relevant predictive performance indicators in the computer engineering discipline are improved by using a combination of intelligent algorithms.

show abstract

Research on vibration effect of tunnel blasting based on an improved Hilbert–Huang transform

Cited by 42 publications

References 35 publications

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Modeling and Analysis Method of National Fitness Big Data for Basketball Projects Based on a Multivariate Statistical Model

Smart Teaching Design Mode based on Machine Learning and its Effect Evaluation

Contact Info

Product

Resources

About