scholarly journals Research on the Relationship between Business Cycle and Industrial Fluctuations in Northeast China Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yinan Zhou ◽  
Guofeng Gu ◽  
Qiushuang Ren
Keyword(s):  
Economic Development ◽  
Business Cycle ◽  
Northeast China ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Secondary Industry ◽  
Adaptive Noise ◽  
The Stability ◽  
The Relationship

The Chinese economy has developed rapidly since the reform and opening up, but economic growth in Northeast China has declined dramatically after the 21st century. In this context, exploring the characteristics of economic and industrial fluctuations in the northeast of China and their relationship is beneficial to alleviating economic fluctuations and promoting stable economic development from the perspective of industrial development. The relationship between economic and industrial fluctuations in the three provinces of Northeast China was reexamined from the angle of fluctuation components with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm. The results obtained are as follows: (1) In the three northeastern provinces of China, economic fluctuations were almost free from the influence of the primary industry, most affected by the secondary industry, and gradually influenced by the tertiary industry after the 21st century. (2) Regarding the short-term business cycle of each province, economic development was the most stable when the market and government participated in the development of the secondary industry simultaneously. (3) The midterm business cycle of Jilin Province was affected by the investment of equipment in secondary and tertiary industries, while that of Liaoning Province was affected by the investment of equipment in the secondary industry. (4) Investment in the equipment of the secondary industry and the construction of secondary and tertiary industries was the key to maintaining the stability of long-term business cycle in Heilongjiang Province, and that in the construction of secondary and tertiary industries was the key to maintaining the stability of long-term business cycles in Jilin and Liaoning Provinces.

scholarly journals Long-term Relationship between Electricity Consumption and Economic Growth

2020 ◽  
Vol 165 ◽  
pp. 06014
Author(s):  
Xia Liyu ◽  
He Wan
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Electricity Consumption ◽  
Quarterly Data ◽  
Material Basis ◽  
Equilibrium Relationship ◽  
Secondary Industry ◽  
The Relationship

Electricity is an indispensable material basis for economic development. It is necessary to study the relationship between different electricity consumption and economic growth. Based on the quarterly data of China’s electricity consumption and economic development from 2011 to 2018, the long-term equilibrium relationship between variables are analyzed from a causal perspective, and electricity consumption indicators for reflecting economic development are identified. The results show that there is a long-term equilibrium relationship between secondary industry electricity consumption, industrial electricity consumption and GDP. The demand for electricity consumption still needs to be met urgently.

scholarly journals Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 931
Author(s):  
Kecheng Peng ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Yanan Guo ◽  
Wenlong Tian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
South Asian ◽  
Recurrent Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
South Asian High ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Deep Learning Model

The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

The Influence of the GRP Structure on the Nature of the Relationship Labor Productivity in the Region with Scientific, Technical and Informational Factors of Development

2021 ◽  
pp. 17-20
Author(s):  
Leonid Basovskiy ◽  
Tatyana Averina
Keyword(s):  
Economic Development ◽  
Labor Productivity ◽  
Russian Federation ◽  
Correlation Relationship ◽  
Extractive Industry ◽  
Scientific Technical ◽  
The Russian Federation ◽  
The Relationship ◽  
A Share

The article is devoted to the search for a criterion for grouping the regions of the Russian Federation, which allows to obtain adequate estimates of the correlation relationship between labor productivity and production, investment, information and innovation factors of economic development. Estimates based on the aggregate of data for all regions do not reflect a reliable relationship between the main and factor indicators. Conducting analytical procedures separately for regions with a share of the extractive industry in GRP of less than and more than 10% improved the correlation indicators. The conclusions are related to the development of programs for the long-term development of regions.

scholarly journals Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

2019 ◽  
Vol 9 (24) ◽  
pp. 5421 ◽  
Author(s):  
Patricio Fuentealba ◽  
Alfredo Illanes ◽  
Frank Ortmeier
Keyword(s):  
Feature Extraction ◽  
Uterine Contraction ◽  
Fetal Monitoring ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Compensatory Mechanisms ◽  
Main Hypothesis ◽  
Mode Decomposition ◽  
Independent Analysis ◽  
Adaptive Noise

Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.

scholarly journals A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 597 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Technology ◽  
Signal Denoising ◽  
Frequency Noise ◽  
Denoising Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Adaptive Noise

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen’s chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.

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