Hilbert-Huang transform is widely used in signal analysis. However, due to its inadequacy in estimating both the maximum and the minimum values of the signals at both ends of the border, traditional HHT is easy to produce boundary error in empirical mode decomposition (EMD) process. To overcome this deficiency, this paper proposes an enhanced empirical mode decomposition algorithm for processing complex signal. Our work mainly focuses on two aspects. On one hand, we develop a technique to obtain the extreme points of observation interval boundary by introducing the linear extrapolation into EMD. This technique is simple but effective in suppressing the error-prone effects of decomposition. On the other hand, a novel envelope fitting method is proposed for processing complex signal, which employs a technique of nonuniform rational B-splines curve. This method can accurately measure the average value of instantaneous signal, which helps to achieve the accurate signal decomposition. Simulation experiments show that our proposed methods outperform their rivals in processing complex signals for time frequency analysis.
Feature selection is one of the hottest topics in the field of machine learning and data mining. In 2016, the feature selection using forest optimization algorithm (FSFOA) was proposed, which had a better classification performance and dimensionality reduction ability. However, there are some shortcomings in FSFOA. Feature Selection using Improved Forest Optimization Algorithm (FSIFOA) is proposed in this article, which aims at solving the problems of FSFOA during the stages of random initialization, forming the candidate population and updating the best tree. FSIFOA uses the Pearson correlation coefficient and the L1 regularization method to replace the random initialization strategy in the initialization stage, uses the method of separating good and bad trees and filling the quantity gap between them to solve the problem of category imbalance in the candidate population generation stage, adds trees of the same precision but different dimension compared with the best tree to the forest in the update stage. In experiment, the new algorithm uses the same data and parameters as the traditional algorithm to test the small, medium and large dimensional data respectively. The results of the experiments show that the new algorithm can improve the classification accuracy of classifiers and increase the dimension reduction ratio compared with the traditional algorithms in the medium and large dimension data set.
Financial time series is always one of the focus of financial market analysis and research. In recent years, with the rapid development of artificial intelligence, machine learning and financial market are more and more closely linked. Artificial neural network is usually used to analyze and predict financial time series. Based on deep learning, six layer long short-term memory neural networks were constructed. Eight long short-term memory neural networks were combined with Bagging method in ensemble learning and predicting model of neural networks ensemble learning was used in Chinese Stock Market. The experiment tested Shanghai Composite Index, Shenzhen Composite Index, Shanghai Stock Exchange 50 Index, Shanghai-Shenzhen 300 Index, Medium and Small Plate Index and Gem Index during the period from January 4, 2012 to December 29, 2017. For long short-term memory neural network ensemble learning model, its accuracy is 58.5%, precision is 58.33%, recall is 73.5%, F1 value is 64.5%, and AUC value is 57.67%, which are better than those of multilayer long short-term memory neural network model and reflect a good prediction outcome.
With the rapid development of wireless communication technology, fourth generation wireless system (4G) technology has undoubtedly become a hot topic. This paper first reviews the development history of wireless communication and from them the reason for the shift in turn into the fourth generation wireless communication system. The paper explains the 4G communication system and describes the feature, goal, advantage, reason of leap of 4G communication system. Using examples shows the application prospect of wireless communication in the future.
The browser/server mode (B/S model) has become the mainstream technology enterprise application development. It has the traits of the lower response and weaker interactivity for the both of client-side and server-side use the "request-waitresponse" method to communicate. As a technique that has realized with server asynchronous communication, Ajax has successfully solved this problem to make up for the deficiency of B/S model. But Ajax development has the certain difficulty. Based on analysis of the Ajax principle foundation, this article proposes an available framework applicated in the laboratory system developing. It reduces the difficulty of development, provides the development efficiency and obtains the very good actual effect.. Keywords-component; Ajax; B/S Model; Framework I.Identify applicable sponsor/s here. (sponsors)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.