Feature constrained compressed sensing CT image reconstruction from incomplete data via robust principal component analysis of the database

2013 ◽  
Vol 58 (12) ◽  
pp. 4047-4070 ◽  
Author(s):  
Dufan Wu ◽  
Liang Li ◽  
Li Zhang
Keyword(s):  
Principal Component Analysis ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Incomplete Data ◽  
Principal Component ◽  
Component Analysis ◽  
Ct Image ◽  
Robust Principal Component Analysis ◽  
Ct Image Reconstruction

Compressed Sensing STORM Super-Resolution Image Reconstruction Based on Noise Correction-Principal Component Analysis Preprocessing Algorithm

2020 ◽  
Vol 47 (2) ◽  
pp. 0207024
Author(s):  
潘文慧 Pan Wenhui ◽  
陈秉灵 Chen Bingling ◽  
张建国 Zhang Jianguo ◽  
顾振宇 Gu Zhenyu ◽  
熊佳 Xiong Jia ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Super Resolution ◽  
Principal Component ◽  
Component Analysis ◽  
Resolution Image ◽  
Noise Correction

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

scholarly journals Robust principal component analysis for modal decomposition of corrupt fluid flows

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Isabel Scherl ◽  
Benjamin Strom ◽  
Jessica K. Shang ◽  
Owen Williams ◽  
Brian L. Polagye ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fluid Flows ◽  
Principal Component ◽  
Component Analysis ◽  
Robust Principal Component Analysis ◽  
Modal Decomposition
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