Long-term hybrid prediction method based on multiscale decomposition and granular computing for oxygen supply network

2021 ◽  
pp. 107442
Author(s):  
Pengwei Zhou ◽  
Zuhua Xu ◽  
Jun Zhao ◽  
Chunyue Song ◽  
Zhijiang Shao
Keyword(s):  
Granular Computing ◽  
Oxygen Supply ◽  
Prediction Method ◽  
Supply Network ◽  
Multiscale Decomposition ◽  
Hybrid Prediction

Evaluation of Leakage Probability of Non-Asbestos Fiber Sheet Gasket at Elevated Temperature Based on Percolation Theory

2010 ◽  
Author(s):  
Masahiro Hagihara ◽  
Hirokazu Tsuji ◽  
Atsushi Yamaguchi
Keyword(s):  
High Temperature ◽  
Percolation Theory ◽  
Life Prediction ◽  
Prediction Method ◽  
Acoustic Tomography ◽  
Leakage Path ◽  
Temperature Environment ◽  
Fiber Sheet ◽  
Bolted Flange

A long-term life prediction method for a compressed fiber sheet gasket under a high-temperature environment is studied. Non-asbestos compressed fiber sheet gaskets are now being used as a substitute for asbestos in the bolted flange joint, for instance petrochemical factories. Consequently, there is a real need for a technology to predict the lifetime of non-asbestos compressed fiber sheet gaskets quantitatively. In this report, the facing surface of the gasket and flange is visualized with scanning acoustic tomography (SAT). Voids were observed on the facing surface of the gasket and increased with the increase in exposure time at high temperature. If a leakage path for inner fluids is created by the increasing number of voids, the leak occurs on the facing surface of the gasket. The probability of a leak due to voids and the lifetime of this gasket are predicted by applying the percolation theory, which describes the connectedness of clusters.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

scholarly journals Granular Computing Concept based long-term prediction of Gas Tank Levels in Steel Industry

2014 ◽  
Vol 47 (3) ◽  
pp. 6105-6110 ◽  
Author(s):  
Zhongyang Han ◽  
Jun Zhao ◽  
Wei Wang ◽  
Ying Liu ◽  
Quanli Liu
Keyword(s):  
Steel Industry ◽  
Granular Computing ◽  
Term Prediction ◽  
Long Term Prediction

A Super-Long-Term Prediction Method of Earth Polar Motion Based on Spectrum Analysis

2021 ◽  
pp. 619-628
Author(s):  
Weitao Lu ◽  
Lue Chen ◽  
Zhijin Zhou ◽  
Songtao Han ◽  
Tianpeng Ren
Keyword(s):  
Spectrum Analysis ◽  
Polar Motion ◽  
Prediction Method ◽  
Term Prediction ◽  
Long Term Prediction

scholarly journals System load trend prediction method based on IF-EMD-LSTM

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986765 ◽  
Author(s):  
Jing Yu ◽  
Feng Ding ◽  
Chenghao Guo ◽  
Yabin Wang
Keyword(s):  
Information System ◽  
Short Term Memory ◽  
Prediction Method ◽  
Short Term ◽  
Trend Prediction ◽  
Term Memory ◽  
System Load ◽  
Mode Function ◽  
Abnormal Points

Accurately predicting the load change of the information system during operation has important guiding significance for ensuring that the system operation is not interrupted and resource scheduling is carried out in advance. For the information system monitoring time series data, this article proposes a load trend prediction method based on isolated forests-empirical modal decomposition-long-term (IF-EMD-LSTM). First, considering the problem of noise and abnormal points in the original data, the isolated forest algorithm is used to eliminate the abnormal points in the data. Second, in order to further improve the prediction accuracy, the empirical modal decomposition algorithm is used to decompose the input data into intrinsic mode function (IMF) components of different frequencies. Each intrinsic mode function (IMF) and residual is predicted using a separate long-term and short-term memory neural network, and the predicted values are reconstructed from each long-term and short-term memory model. Finally, experimental verification was carried out on Amazon’s public data set and compared with autoregressive integrated moving average and Prophet models. The experimental results show the superior performance of the proposed IF-EMD-LSTM prediction model in information system load trend prediction.

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