Calibrating Dual-Directional Couplers and Evaluating Uncertainties in Net Power Delivery

2013 ◽  
Vol 718-720 ◽  
pp. 1446-1450
Author(s):  
Jin Yuan Li ◽  
Ming Xie
Keyword(s):  
Monte Carlo ◽  
Measurement Uncertainty ◽  
Monte Carlo Analysis ◽  
Measured Data ◽  
Power Delivery ◽  
Complete Model ◽  
Directional Couplers ◽  
Measurement Uncertainties ◽  
Field Systems

Dual-directional couplers are used to monitor net power fed into transmitting antennas in standard-field systems at NIM, China. It is essential to calibrate the coupler and evaluate the measurement uncertainty in the net power in such applications. The method we use at NIM to calibrate couplers are presented in this paper, and a complete model that takes into account the measurement uncertainties of couplers and horns to evaluate uncertainties in net power delivery is proposed. A case study is also provided of power delivery by a coupler with the nominal directivity of 40 dB using the Monte Carlo analysis based on the complete model and actual measured data.

Monte Carlo Analysis of Measurement Uncertainties for On-Wafer Multiline TRL Calibration Including Dynamic Accuracy

2020 ◽  
Vol 69 (11) ◽  
pp. 8874-8880
Author(s):  
Peng Luan ◽  
Yibang Wang ◽  
Wei Zhao ◽  
Chen Liu ◽  
Faguo Liang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis ◽  
Dynamic Accuracy ◽  
Measurement Uncertainties

scholarly journals RESECTION AND MONTE CARLO COVARIANCE FROM VANISHING POINTS FOR IMAGES OF UNKNOWN ORIGIN

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 487-494
Author(s):  
R. Settergren
Keyword(s):  
Monte Carlo ◽  
3D Reconstruction ◽  
Measurement Uncertainty ◽  
Unknown Origin ◽  
Monte Carlo Analysis ◽  
Vanishing Points ◽  
Uncertainty Of Measurements ◽  
Close Range ◽  
Camera Parameters ◽  
3D Scene

Abstract. Photogrammetric analysis requires camera metadata (position, attitude, interior orientation, etc.), which is not available for all images. Modern commercial solutions for 3D reconstruction from images typically assume large amounts of purposefully-collected, highly-overlapping imagery. In this article, a system is demonstrated for recovering a 3D scene from images of unknown origin, by marking ground space axes, resecting a camera consistent with the markings, and then using the solved camera to collect 3D measurements. The capability works with close-range (vanishing points) and long-range (parallel axes) imagery. Monte Carlo analysis is used to propagate measurement uncertainty of the vanishing lines to uncertainty of exterior and interior camera parameters, which can then be used to quantify uncertainty of measurements in the 3D scene.

scholarly journals Uncertainty in flood damage estimates and its potential effect on investment decisions

2016 ◽  
Vol 16 (1) ◽  
pp. 1-14 ◽  
Author(s):  
D. J. Wagenaar ◽  
K. M. de Bruijn ◽  
L. M. Bouwer ◽  
H. de Moel
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis ◽  
Investment Strategy ◽  
Potential Effect ◽  
Flood Damage ◽  
Flood Events ◽  
Damage Functions ◽  
Damage Models ◽  
Damage Estimates

Abstract. This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage functions and maximum damages can have large effects on flood damage estimates. This explanation is then used to quantify the uncertainty in the damage estimates with a Monte Carlo analysis. The Monte Carlo analysis uses a damage function library with 272 functions from seven different flood damage models. The paper shows that the resulting uncertainties in estimated damages are in the order of magnitude of a factor of 2 to 5. The uncertainty is typically larger for flood events with small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

scholarly journals Uncertainty in flood damage estimates and its potential effect on investment decisions

2015 ◽  
Vol 3 (1) ◽  
pp. 607-640 ◽  
Author(s):  
D. J. Wagenaar ◽  
K. M. de Bruijn ◽  
L. M. Bouwer ◽  
H. De Moel
Keyword(s):  
Monte Carlo ◽  
Optimal Investment ◽  
Monte Carlo Analysis ◽  
Investment Strategy ◽  
Potential Effect ◽  
Flood Damage ◽  
Damage Function ◽  
Damage Models ◽  
Damage Estimates

Abstract. This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. As input the Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. The resulting uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

How to Measure Diffusion Coefficients in Biofilms: A Critical Analysis

2020 ◽  
Author(s):  
Lenno van den Berg ◽  
Mark van Loosdrecht ◽  
Merle de Kreuk
Keyword(s):  
Monte Carlo ◽  
Diffusion Coefficient ◽  
Diffusion Coefficients ◽  
Effective Diffusion ◽  
Granular Sludge ◽  
Monte Carlo Analysis ◽  
Propagation Of Uncertainty ◽  
Relative Standard ◽  
Wide Range

<p>Effective diffusion coefficients are often required for kinetic descriptions of biofilms. Many previous studies have measured diffusion coefficients for specific molecule-biofilm combinations. As a result, many biofilm researchers today rely on literature values of diffusion coefficients for their own biofilm system. However, the reported diffusion coefficients in literature fall within a wide range, even for the same molecule. One potential cause of this range is the accuracy of the methods used to measure diffusion coefficients. The objective of this study was to determine the precision (similarity between repeated experiments) and bias (difference between measured and true diffusion coefficient) of six common methods. The six selected methods were based on determining mass balances and on microelectrode measurements. The precision and bias were quantified based on mathematical models of the six methods, with oxygen diffusion in granular sludge as a case study. The precision was assessed by a Monte Carlo uncertainty analysis, which considers the propagation of uncertainty in the input experimental parameters. The bias was determined for six potential sources of error: solute sorption, biomass deactivation, a concentration boundary layer, granule roughness, granule shape, and granule size distribution. From the Monte Carlo analysis, it followed that the precision of the methods ranged from 4-77% relative standard deviation. The microelectrode methods were more accurate than the mass balance methods. The bias due to the combined effect of the six errors was an underestimation of the diffusion coefficient by 74%. This shows that current methods are unable to accurately determine diffusion coefficients. We do not propose improvements to the current methods, but instead discuss why inaccurate diffusion coefficients are sufficient for accurate engineering of biofilm processes. </p>

Sign in / Sign up

Export Citation Format

Share Document