scholarly journals PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1243
Author(s):  
Wenxin Yin ◽  
Wenhui Diao ◽  
Peijin Wang ◽  
Xin Gao ◽  
Ya Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Power Plants ◽  
State Of The Art ◽  
Thermal Power ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Thermal Power Plants ◽  
Average Precision ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

scholarly journals Deep Residual Squeeze and Excitation Network for Remote Sensing Image Super-Resolution

2019 ◽  
Vol 11 (15) ◽  
pp. 1817 ◽  
Author(s):  
Jun Gu ◽  
Xian Sun ◽  
Yue Zhang ◽  
Kun Fu ◽  
Lei Wang
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
Single Image ◽  
Remote Sensing Images ◽  
Deep Convolutional Neural Networks ◽  
Current Block ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Recently, deep convolutional neural networks (DCNN) have obtained promising results in single image super-resolution (SISR) of remote sensing images. Due to the high complexity of remote sensing image distribution, most of the existing methods are not good enough for remote sensing image super-resolution. Enhancing the representation ability of the network is one of the critical factors to improve remote sensing image super-resolution performance. To address this problem, we propose a new SISR algorithm called a Deep Residual Squeeze and Excitation Network (DRSEN). Specifically, we propose a residual squeeze and excitation block (RSEB) as a building block in DRSEN. The RSEB fuses the input and its internal features of current block, and models the interdependencies and relationships between channels to enhance the representation power. At the same time, we improve the up-sampling module and the global residual pathway in the network to reduce the parameters of the network. Experiments on two public remote sensing datasets (UC Merced and NWPU-RESISC45) show that our DRSEN achieves better accuracy and visual improvements against most state-of-the-art methods. The DRSEN is beneficial for the progress in the remote sensing images super-resolution field.

State-of-the-art of solar thermal power plants—A review

2013 ◽  
Vol 27 ◽  
pp. 258-273 ◽  
Author(s):  
V. Siva Reddy ◽  
S.C. Kaushik ◽  
K.R. Ranjan ◽  
S.K. Tyagi
Keyword(s):  
Power Plants ◽  
State Of The Art ◽  
Thermal Power ◽  
Solar Thermal ◽  
Thermal Power Plants ◽  
Solar Thermal Power

On the prospect of introduction of plasma ignition in power boilers

2019 ◽  
Vol 12 (1) ◽  
pp. 22-28
Author(s):  
V. Ye. Mikhailov ◽  
S. P. Kolpakov ◽  
L. A. Khomenok ◽  
N. S. Shestakov
Keyword(s):  
Russian Federation ◽  
Solid Fuel ◽  
Power Plants ◽  
Thermal Power ◽  
Fuel Oil ◽  
Solid Fuels ◽  
Thermal Power Plants ◽  
Plasma Ignition ◽  
The Russian Federation ◽  
Capital Construction

One of the most important issues for modern domestic power industry is the creation and further widespread introduction of solid propellant energy units for super-critical steam parameters with high efficiency (43–46%) and improved environmental parameters. This will significantly reduce the use of natural gas.At the same time, one of the major drawbacks of the operation of pulverized coal power units is the need to use a significant amount of fuel oil during start-up and shutdown of boilers to stabilize the burning of the coal torch in the variable boiler operating modes.In this regard, solid fuel TPPs need to be provided with fuel oil facilities, with all the associated problems to ensure the performance (heating of fuel oil in winter), reliability and safety. All of the above problems increase both the TPP capital construction costs, and the electricity generating cost.A practical solution to the above problems at present is the use of a plasma technology for coal torch ignition based on thermochemical preparation of fuel for combustion. The materials of the developments of JSC “NPO CKTI” on application of plasmatrons in boilers of thermal power plants at metallurgical complexes of the Russian Federation are also considered.Plasma ignition systems for solid fuels in boilers were developed by Russian specialists and were introduced at a number of coal-fi red power plants in the Russian Federation, Mongolia, North Korea, and Kazakhstan. Plasma ignition of solid fuels is widely used in China for almost 30% of power boilers.The introduction of plasma-energy technologies will improve the energy efficiency of domestic solid-fuel thermal power plants and can be widely implemented in the modernization of boilers.During the construction of new TPPs, the construction of fuel oil facilities can be abandoned altogether, which will reduce the capital costs of the construction of thermal power plants, reduce the construction footprint, and increase the TPP safety.

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