Tomographic reconstruction of emissive profile in the divertor region for the visible light imaging diagnostic on Experimental Advanced Superconducting Tokamak

2021 ◽  
Vol 163 ◽  
pp. 112149
Author(s):  
Zhiyuan Lu ◽  
Shifeng Mao ◽  
Jianhua Yang ◽  
Tingfeng Ming ◽  
Junguang Xiang ◽  
...  
2018 ◽  
Vol 131 ◽  
pp. 166-174 ◽  
Author(s):  
Junguang Xiang ◽  
Ran Chen ◽  
Tingfeng Ming ◽  
Guosheng Xu ◽  
Jianhua Yang ◽  
...  

1998 ◽  
Vol 5 (3) ◽  
pp. 642-644 ◽  
Author(s):  
J. Y. Huang ◽  
I. S. Ko

A diagnostic beamline is being constructed in the PLS storage ring for measurement of electron- and photon-beam properties. It consists of two 1:1 imaging systems: a visible-light imaging system and a soft X-ray imaging system. In the visible-light imaging system, the transverse beam size and beam position are measured with various detectors: a CCD camera, two photodiode arrays and a photon-beam position monitor. Longitudinal bunch structure is also investigated with a fast photodiode detector and a picosecond streak camera. On the other hand, the soft X-ray imaging system is under construction to measure beam sizes with negligible diffraction-limited error. The X-ray image optics consist of a flat cooled mirror and two spherical focusing mirrors.


2003 ◽  
Vol 75 (4) ◽  
pp. 716-722 ◽  
Author(s):  
Jaap van der Weerd ◽  
Marieke K. van Veen ◽  
Ron M. A. Heeren ◽  
Jaap J. Boon

MRS Bulletin ◽  
1988 ◽  
Vol 13 (1) ◽  
pp. 13-18 ◽  
Author(s):  
J.H. Kinney ◽  
Q.C. Johnson ◽  
U. Bonse ◽  
M.C. Nichols ◽  
R.A. Saroyan ◽  
...  

Imaging is the cornerstone of materials characterization. Until the middle of the present century, visible light imaging provided much of the information about materials. Though visible light imaging still plays an extremely important role in characterization, relatively low spatial resolution and lack of chemical sensitivity and specificity limit its usefulness.The discovery of x-rays and electrons led to a major advance in imaging technology. X-ray diffraction and electron microscopy allowed us to characterize the atomic structure of materials. Many materials vital to our high technology economy and defense owe their existence to the understanding of materials structure brought about with these high-resolution methods.Electron microscopy is an essential tool for materials characterization. Unfortunately, electron imaging is always destructive due to the sample preparation that must be done prior to imaging. Furthermore, electron microscopy only provides information about the surface of a sample. Three dimensional information, of great interest in characterizing many new materials, can be obtained only by time consuming sectioning of an object.The development of intense synchrotron light sources in addition to the improvements in solid state imaging technology is revolutionizing materials characterization. High resolution x-ray imaging is a potentially valuable tool for materials characterization. The large depth of x-ray penetration, as well as the sensitivity of absorption crosssections to atomic chemistry, allows x-ray imaging to characterize the chemistry of internal structures in macroscopic objects with little sample preparation. X-ray imaging complements other imaging modalities, such as electron microscopy, in that it can be performed nondestructively on metals and insulators alike.


2010 ◽  
Vol 22 (9) ◽  
pp. 2023-2026 ◽  
Author(s):  
马金龙 Ma Jinlong ◽  
刘长安 Liu Chang’an ◽  
裘伟 Qiu Wei ◽  
常星璋 Chang Xingzhang ◽  
毛静锋 Mao Jingfeng ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5852
Author(s):  
Yuanzhi Wang ◽  
Tao Lu ◽  
Tao Zhang ◽  
Yuntao Wu

Pedestrian detection is an essential problem of computer vision, which has achieved tremendous success under controllable conditions using visible light imaging sensors in recent years. However, most of them do not consider low-light environments which are very common in real-world applications. In this paper, we propose a novel pedestrian detection algorithm using multi-task learning to address this challenge in low-light environments. Specifically, the proposed multi-task learning method is different from the most commonly used multi-task learning method—the parameter sharing mechanism—in deep learning. We design a novel multi-task learning method with feature-level fusion and a sharing mechanism. The proposed approach contains three parts: an image relighting subnetwork, a pedestrian detection subnetwork, and a feature-level multi-task fusion learning module. The image relighting subnetwork adjusts the low-light image quality for detection, the pedestrian detection subnetwork learns enhanced features for prediction, and the feature-level multi-task fusion learning module fuses and shares features among component networks for boosting image relighting and detection performance simultaneously. Experimental results show that the proposed approach consistently and significantly improves the performance of pedestrian detection on low-light images obtained by visible light imaging sensor.


2015 ◽  
Vol 4 (11) ◽  
pp. F61-F64 ◽  
Author(s):  
Y. Tachikawa ◽  
J. Sugimoto ◽  
M. Takada ◽  
T. Kawabata ◽  
S. M. Lyth ◽  
...  

Author(s):  
Xiu-Mei Shao ◽  
Bo Yang ◽  
Songlei Huang ◽  
Yang Wei ◽  
Xue Li ◽  
...  

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