A nonlinear correction method for the error caused by the change of the integration time of the fiber spectrometer

2021 ◽  
pp. 103820
Author(s):  
Gang Li ◽  
Dan Wang ◽  
Mei Zhou ◽  
Kang Wang ◽  
Shaohua Wu ◽  
...  
2020 ◽  
Vol 49 (1) ◽  
pp. 110002-110002
Author(s):  
白乐 Le BAI ◽  
赖雪峰 Xue-feng LAI ◽  
韩维强 Wei-qiang HAN ◽  
王昊光 Hao-guang WANG ◽  
周金梅 Jin-mei ZHOU ◽  
...  

2011 ◽  
Vol 66-68 ◽  
pp. 2034-2040
Author(s):  
Qin He Gao ◽  
Xiang Yang Li

This paper employed the theories of multibody system dynamics to analyze the multi-rigid-body model of erection system and build the general dynamic models in absolute coordinates. The impact theory of contact mechanics and nonlinear spring-damper force function were used to model the impact problems between rods of multi-stage hydraulic cylinder of erection system and educe the dynamic models of multi-rigid-body erection system with impact. An automatic violation correction method according to the step of integration time was given to solve the violation which is an incident problem in numerical integration of dynamic models in absolute coordinates. Simulation results show that these dynamic models are effective.


2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Joel Kuusk

A dark signal temperature dependence correction method for miniature spectrometer modules is described in this paper. It is based on laboratory measurements of dark signal temperature dependence at few different integration times. A set of parameters are calculated which make it possible to estimate dark signal at any temperature and integration time within reasonable range. In field conditions, it is not always possible to take frequent dark signal readings during spectral measurements. If temperature is recorded during the measurement, this method can be used for estimating dark signal for every single spectral measurement. The method is validated on two different miniature spectrometers.


2014 ◽  
Vol 651-653 ◽  
pp. 400-404 ◽  
Author(s):  
Zhuo Jing Yang ◽  
Jian Wei Zhang ◽  
Wen Jie Hao ◽  
Jin Ping Yang

Because resistance of two-dimensional position sensitive detector's (PSD) photo surface is not absolute uniformity that its output is nonlinear. It is this feature enables the PSD difficult to measure small displacement. In order to solve this problem, BP neural network is proposed to solve the problem of PSD nonlinear correction after the study of traditional nonlinear correction method; BP neural network would have a strong ability of nonlinear mapping after training, and it can approach arbitrarily contact function by arbitrary precision, and MATLAB neural networking boxes can simulate BP neural network easily. Simulation and verification indicate that the method has a remarkable effect in solving nonlinear problems, and it can meet system requirements.


AIAA Journal ◽  
1967 ◽  
Vol 5 (6) ◽  
pp. 1183-1185 ◽  
Author(s):  
WILLIAM E. HUNT ◽  
ALI HASAN NAYFEH

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1156
Author(s):  
Eu-Tteum Baek ◽  
Hyung-Jeong Yang ◽  
Soo-Hyung Kim ◽  
Gueesang Lee ◽  
Hieyong Jeong

A distance map captured using a time-of-flight (ToF) depth sensor has fundamental problems, such as ambiguous depth information in shiny or dark surfaces, optical noise, and mismatched boundaries. Severe depth errors exist in shiny and dark surfaces owing to excess reflection and excess absorption of light, respectively. Dealing with this problem has been a challenge due to the inherent hardware limitations of ToF, which measures the distance using the number of reflected photons. This study proposes a distance error correction method using three ToF sensors, set to different integration times to address the ambiguity in depth information. First, the three ToF depth sensors are installed horizontally at different integration times to capture distance maps at different integration times. Given the amplitude maps and error regions are estimated based on the amount of light, the estimated error regions are refined by exploiting the accurate depth information from the neighboring depth sensors that use different integration times. Moreover, we propose a new optical noise reduction filter that considers the distribution of the depth information biased toward one side. Experimental results verified that the proposed method overcomes the drawbacks of ToF cameras and provides enhanced distance maps.


Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2833
Author(s):  
Münevver Nehir ◽  
Carsten Frank ◽  
Steffen Aßmann ◽  
Eric P. Achterberg

Charge-coupled device (CCD) spectrometers are widely used as detectors in analytical laboratory instruments and as sensors for in situ optical measurements. However, as the applications become more complex, the physical and electronic limits of the CCD spectrometers may restrict their applicability. The errors due to dark currents, temperature variations, and blooming can be readily corrected. However, a correction for uncertainty of integration time and wavelength calibration is typically lacking in most devices, and detector non-linearity may distort the signal by up to 5% for some measurements. Here, we propose a simple correction method to compensate for non-linearity errors in optical measurements where compact CCD spectrometers are used. The results indicate that the error due to the non-linearity of a spectrometer can be reduced from several hundred counts to about 40 counts if the proposed correction function is applied.


2017 ◽  
Vol 21 (1) ◽  
pp. 267-279 ◽  
Author(s):  
Erik Gregow ◽  
Antti Pessi ◽  
Antti Mäkelä ◽  
Elena Saltikoff

Abstract. The focus of this article is to improve the precipitation accumulation analysis, with special focus on the intense precipitation events. Two main objectives are addressed: (i) the assimilation of lightning observations together with radar and gauge measurements, and (ii) the analysis of the impact of different integration periods in the radar–gauge correction method. The article is a continuation of previous work by Gregow et al. (2013) in the same research field. A new lightning data assimilation method has been implemented and validated within the Finnish Meteorological Institute – Local Analysis and Prediction System. Lightning data do improve the analysis when no radars are available, and even with radar data, lightning data have a positive impact on the results. The radar–gauge assimilation method is highly dependent on statistical relationships between radar and gauges, when performing the correction to the precipitation accumulation field. Here, we investigate the usage of different time integration intervals: 1, 6, 12, 24 h and 7 days. This will change the amount of data used and affect the statistical calculation of the radar–gauge relations. Verification shows that the real-time analysis using the 1 h integration time length gives the best results.


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