High precision LED-based light pulser for electromagnetic calorimeter monitoring system

Author(s):  
A. Fedorov ◽  
A. Lopatik ◽  
Q. Missevitch ◽  
J.-P. Peigneux ◽  
A. Singovski
2011 ◽  
Vol 217-218 ◽  
pp. 1324-1329
Author(s):  
Yan Hua Mi ◽  
Li Xin Liu ◽  
Li Fang Lai

This paper introduced the characteristics of functions of the automatic monitoring system on surveys robot (TCA total station ). Has analyzed the application situation and the precision of the automatic monitoring system. Practical applications indicated that this system had high efficiency and precise data transmission. The achievement of surveys achieved very high precision in both horizon talplane and vertical direction. The results can provide technology references for similarprojects.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yu Zhang ◽  
Ruofei Zhong ◽  
Yongrong Li ◽  
Haili Sun

The development of information technology and computer science has put forward higher requirements on the intelligence of deformation monitoring. We study a method based on image deformation analysis, which uses Scale-Invariant Feature Transform (SIFT) to extract image feature points after preprocessing the acquired images, applies All-Pixels Matching (APM) method to the sequence images to do further high-precision matching to achieve the accuracy of subpixels, and finally solves the deformation variables according to the relationship of the real size of the reference target and its pixel. Wavelet analysis and least squares are used to improve the image quality and matching accuracy. Based on this method, we design and develop a new remotely automated deformation monitoring system. In this paper, we introduce the algorithm principle of deformation analysis, the integration of the system, and the engineering application example of the monitoring system. The monitoring accuracy of the system satisfying 0.1 mm within 10 m and 0.8 mm within 60 m is verified in the simultaneous comparison observation according to the high-precision total station, which illustrates the effectiveness of the present deformation analysis method and monitoring system and also has the characteristics of low monitoring cost and high degree of automation.


1998 ◽  
Author(s):  
G. David ◽  
E. Kistenev ◽  
S. Stoll ◽  
S. White ◽  
C. Woody ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Qiushuang Lin ◽  
Chunxiang Li ◽  
Chao Wu

Wind signal forecasting has become more and more crucial in the structural health monitoring system and wind engineering recently. It is a challenging subject owing to the complicated volatility of wind signals. The robustness and generalization of a predictor are significant as well as of high precision. In this paper, an adaptive residual convolutional neural network (CNN) is developed, aiming at achieving not only high precision but also high adaptivity for various wind signals with varying complexity. Afterwards, reinforced forecasting is adopted to enhance the robustness of the preliminary forecasting. The preliminary forecast results by adaptive residual CNN are integrated with historical observed signals as the new input to reconstruct a new forecasting mapping. Meanwhile, simplified-boost strategy is applied for more generalized results. The results of multistep forecasting for five kinds of nonstationary non-Gaussian wind signals prove the more excellent adaptivity and robustness of the developed two-stage model compared with single models.


2021 ◽  
pp. 187-196
Author(s):  
Chengli She ◽  
Haitao Liu ◽  
Jun Yu ◽  
Peiyuan Zhou ◽  
Hongzheng Cui

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