Model parameter calibration method of SiC power MOSFETs behavioural model

Abstract Rotary-laser automatic theodolite (R-LAT) system is a distributed large-scale metrology system, which provides parallel measurement in scalable measurement room without obvious precision losing. Each of R-LAT emits two nonparallel laser planes to scan the measurement space via evenly rotation, while the photoelectric sensors receive these laser planes signals and performs the coordinate calculation based on triangulation. The accurate geometric parameters of the two laser planes plays a crucial role in maintaining the measurement precision of R-LAT system. Practically, the geometry of the two laser plane, which is termed as intrinsic parameters, is unknown after assembled. Therefore, how to figure out the accurate intrinsic parameters of each R-LAT is a fundamental question for the application of R-LAT system. This paper proposed an easily operated intrinsic parameter calibration method for R-LAT system with adopting coordinate measurement machine. The mathematic model of laser planes and the observing equation group of R-LAT are established. Then, the intrinsic calibration is formulated as a nonlinear least square problem that minimize the sum of deviations of target points and laser planes, and the ascertain of its initial guess is introduced. At last, experience is performed to verify the effectiveness of this method, and simulations are carried out to investigate the influence of the target point configuration on the accuracy of intrinsic parameters.

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Research on the kinematic parameter calibration method of industrial robot based on LM and PF algorithm

2020 Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc49329.2020.9164756 ◽

2020 ◽

Author(s):

Xin Deng ◽

Lianzheng Ge ◽

Ruifeng Li ◽

Zhiheng Liu

Keyword(s):

Industrial Robot ◽

Calibration Method ◽

Kinematic Parameter ◽

Parameter Calibration

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Machine learning approaches to parameter calibration and uncertainty propagation for seasonal flood risk prediction

10.5194/egusphere-egu21-13262 ◽

2021 ◽

Author(s):

Oliver Bent ◽

Julian Kuehnert ◽

Sekou Remy ◽

Anne Jones ◽

Blair Edwards

Keyword(s):

Climate Change ◽

Machine Learning ◽

Uncertainty Quantification ◽

Supervised Learning ◽

Risk Prediction ◽

Flood Risk ◽

Parameter Calibration ◽

Model Parameter ◽

Seasonal Flood ◽

Node Type

<div data-node-type="line"> <div data-node-type="line">The increase in extreme weather associated with acute climate change is leading to more frequent and severe flood events.&#160; In the window of months and years, climate change adaption is critical to mitigate risk on socio-economic systems. Mathematical and computational models have become widely used tools to quantify the impact of catastrophic flooding and to predict future flood risks. For decision makers to plan ahead and to select informed policies and interventions, it is vital that the uncertainties of these models are well estimated. Besides the inherent uncertainty of the mathematical model, uncertainties arise from parameter calibration and the driving observational climate data.</div> <div data-node-type="line">Here we focus on the uncertainty of seasonal flood risk prediction for which we treat uncertainty propagation as a two step process. Firstly through calibration of model parameter distributions based on observational data. In order to propagate parameter uncertainties, the posed calibration framework is required to infer model parameter posterior distributions, as opposed to a single best-fit estimate. While secondly uncertainty is propagated by the seasonal weather forecasts driving the flood risk prediction models, such model drivers have their own inherent uncertainty as predictions. Through handling both sources of uncertainty and its propagation we investigate the impacts of combined uncertainty quantification methods for flooding predictions. The first step focussing on the flooding models own characterisation of uncertainty and the second characterising how uncertain model drivers impact our future predictions.</div> <div data-node-type="line">In order to achieve the above features of a calibration framework for flood models we leverage concepts from machine learning. At the core we assume a minimisation of a loss function by the methods based on the supervised learning task in order to achieve calibration of the flood model. Uncertainty quantification is equally a growing field in machine learning or AI with regards the interpretability of parametric models. For this purpose we have adopted a Bayesian framework which contains natural descriptions of model expectation and variance. Through combining uncertainty quantification with the steps of supervised learning for parameter calibrations we propose a novel approach for seasonal flood risk prediction.</div> </div><div data-node-type="line"></div>

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A camera-IMU system extrinsic parameter calibration method

2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) ◽

10.1109/itnec.2017.8284902 ◽

2017 ◽

Cited By ~ 2

Author(s):

Liu Tao ◽

Du Hongwang

Keyword(s):

Calibration Method ◽

Parameter Calibration ◽

Extrinsic Parameter

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