scholarly journals Improving the mean and uncertainty of ultraviolet multi-filter rotating shadowband radiometer in situ calibration factors: utilizing Gaussian process regression with a new method to estimate dynamic input uncertainty

2019 ◽  
Vol 12 (2) ◽  
pp. 935-953 ◽  
Author(s):  
Maosi Chen ◽  
Zhibin Sun ◽  
John M. Davis ◽  
Yan-An Liu ◽  
Chelsea A. Corr ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Average Method ◽  
Moving Average ◽  
Gaussian Process Regression ◽  
Input Uncertainty ◽  
In Situ Calibration ◽  
The Mean ◽  
Mean Function

Abstract. To recover the actual responsivity for the Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR), the complex (e.g., unstable, noisy, and with gaps) time series of its in situ calibration factors (V0) need to be smoothed. Many smoothing techniques require accurate input uncertainty of the time series. A new method is proposed to estimate the dynamic input uncertainty by examining overall variation and subgroup means within a moving time window. Using this calculated dynamic input uncertainty within Gaussian process (GP) regression provides the mean and uncertainty functions of the time series. This proposed GP solution was first applied to a synthetic signal and showed significantly smaller RMSEs than a Gaussian process regression performed with constant values of input uncertainty and the mean function. GP was then applied to three UV-MFRSR V0 time series at three ground sites. The method appropriately accounted for variation in slopes, noises, and gaps at all sites. The validation results at the three test sites (i.e., HI02 at Mauna Loa, Hawaii; IL02 at Bondville, Illinois; and OK02 at Billings, Oklahoma) demonstrated that the agreement among aerosol optical depths (AODs) at the 368 nm channel calculated using V0 determined by the GP mean function and the equivalent AERONET AODs were consistently better than those calculated using V0 from standard techniques (e.g., moving average). For example, the average AOD biases of the GP method (0.0036 and 0.0032) are much lower than those of the moving average method (0.0119 and 0.0119) at IL02 and OK02, respectively. The GP method's absolute differences between UV-MFRSR and AERONET AOD values are approximately 4.5 %, 21.6 %, and 16.0 % lower than those of the moving average method at HI02, IL02, and OK02, respectively. The improved accuracy of in situ UVMRP V0 values suggests the GP solution is a robust technique for accurate analysis of complex time series and may be applicable to other fields.

scholarly journals Improving the Mean and Uncertainty of Ultraviolet Multi-Filter Rotating Shadowband Radiometer In-Situ Calibration Factors: Utilizing Gaussian Process Regression with a New Method to Estimate Dynamic Input Uncertainty

2018 ◽  
Author(s):  
Maosi Chen ◽  
Zhibin Sun ◽  
John M. Davis ◽  
Yan-An Liu ◽  
Chelsea A. Corr ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Time Window ◽  
Gaussian Process Regression ◽  
New Method ◽  
Input Uncertainty ◽  
In Situ Calibration ◽  
The Mean ◽  
Mean Function

Abstract. To recover the actual responsivity for Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR), the complex (e.g. unstable, noisy, and with gaps) time series of its in-situ calibration factors (Vo) need to be smoothed. Many smoothing techniques require accurate input uncertainty of the time series. A new method is proposed to estimate the dynamic input uncertainty by examining overall variation and subgroup means within a moving time window. Using this calculated dynamic input uncertainty within Gaussian Process regression (GP) provides the mean and uncertainty functions of the time series. This proposed GP solution was first applied on a synthetic signal and showed significant smaller RMSEs than a Gaussian Process regression performed with constant values of input uncertainty and the mean function. GP was then applied to three UV-MFRSR Vo time series at three ground sites; The method appropriately accounted for variation in slopes, noises, and gaps at all sites. The validation results demonstrated that the agreement between aerosol optical depths (AODs) calculated using Vo determined by the GP mean function and AERONET AODs were consistently better than those calculated using Vo from standard techniques (e.g. moving average). The improved accuracy of in-situ UVMRP Vo values suggests the GP solution is a robust technique for accurate analysis of complex time series and may be applicable to other fields.

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

2021 ◽  
Author(s):  
Aidin Tamhidi ◽  
Nicolas Kuehn ◽  
S. Farid Ghahari ◽  
Arthur J. Rodgers ◽  
Monica D. Kohler ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Ground Motion ◽  
San Francisco ◽  
Earthquake Engineering ◽  
Seismic Analysis ◽  
Ground Motions ◽  
Free Field ◽  
Gaussian Process Regression ◽  
Motion Time

ABSTRACT Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

Battery Capacity Estimation From Partial-Charging Data Using Gaussian Process Regression

2017 ◽  
Author(s):  
Robert R. Richardson ◽  
Christoph R. Birkl ◽  
Michael A. Osborne ◽  
David A. Howey
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Diagnostic Technique ◽  
Lithium Ion ◽  
Constant Current ◽  
Time Data ◽  
Capacity Estimation ◽  
Voltage Range ◽  
Battery Capacity

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a novel diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which is capable of estimating the battery capacity using voltage vs. time measurements over short periods of galvanostatic operation. The approach uses Gaussian process regression to map from voltage values at a selection of uniformly distributed times, to cell capacity. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data through the lens of Incremental Capacity (IC) or Differential Voltage (DV) analysis. This overcomes both the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. Rather, GP-ICE gives insight into which portions of the voltage range are most informative about the capacity for a particular cell. We apply GP-ICE to a dataset of 8 cells, which were aged by repeated application of an ARTEMIS urban drive cycle. Within certain voltage ranges, as little as 10 seconds of charge data is sufficient to enable capacity estimates with ∼ 2% RMSE.

scholarly journals COUNT AND DURATION TIME SERIES WITH EQUAL CONDITIONAL STOCHASTIC AND MEAN ORDERS

2020 ◽  
pp. 1-33
Author(s):  
Abdelhakim Aknouche ◽  
Christian Francq
Keyword(s):  
Time Series ◽  
Conditional Distribution ◽  
Stochastic Orders ◽  
Conditional Mean ◽  
Contraction Condition ◽  
Mixing Coefficients ◽  
The Mean ◽  
Quasi Maximum Likelihood ◽  
Geometric Decay ◽  
Mean Function

We consider a positive-valued time series whose conditional distribution has a time-varying mean, which may depend on exogenous variables. The main applications concern count or duration data. Under a contraction condition on the mean function, it is shown that stationarity and ergodicity hold when the mean and stochastic orders of the conditional distribution are the same. The latter condition holds for the exponential family parametrized by the mean, but also for many other distributions. We also provide conditions for the existence of marginal moments and for the geometric decay of the beta-mixing coefficients. We give conditions for consistency and asymptotic normality of the Exponential Quasi-Maximum Likelihood Estimator of the conditional mean parameters. Simulation experiments and illustrations on series of stock market volumes and of greenhouse gas concentrations show that the multiplicative-error form of usual duration models deserves to be relaxed, as allowed in this paper.

scholarly journals Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Haopeng Zhang ◽  
Cong Zhang ◽  
Zhiguo Jiang ◽  
Yuan Yao ◽  
Gang Meng
Keyword(s):  
Gaussian Process ◽  
Pose Estimation ◽  
Gaussian Process Regression ◽  
Regression Function ◽  
Mean Value ◽  
Training Data ◽  
Simulated Image ◽  
Lighting Conditions ◽  
Mean Function ◽  
Imaging Sensors

In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (GPR). Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. These explicit formulations can not only offer the category or estimated pose by the mean value of the predicted output but also give its uncertainty by the variance which makes the predicted result convincing and applicable in practice. Besides, we also introduce a manifold constraint to the output of the GPR model to improve its performance for satellite pose estimation. Extensive experiments are performed on two simulated image datasets containing satellite images of 1D and 2D pose variations, as well as different noises and lighting conditions. Experimental results validate the effectiveness and robustness of our approach.

scholarly journals An Efficient Radio Map Updating Algorithm based on K-Means and Gaussian Process Regression

2018 ◽  
Vol 71 (5) ◽  
pp. 1055-1068 ◽  
Author(s):  
Jianli Zhao ◽  
Xiang Gao ◽  
Xin Wang ◽  
Chunxiu Li ◽  
Min Song ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Gaussian Process ◽  
Predictive Model ◽  
Gaussian Process Regression ◽  
Environment Changes ◽  
Model Based ◽  
Map Updating ◽  
Mean Function ◽  
Radio Map ◽  
Indoor Localisation

Fingerprint-based indoor localisation suffers from influences such as fingerprint pre-collection, environment changes and expending a lot of manpower and time to update the radio map. To solve the problem, we propose an efficient radio map updating algorithm based on K-Means and Gaussian Process Regression (KMGPR). The algorithm builds a Gaussian Process Regression (GPR) predictive model based on a Gaussian mean function and realises the update of the radio map using K-Means. We have conducted experiments to evaluate the performance of the proposed algorithm and results show that GPR using the Gaussian mean function improves localisation accuracy by about 13·76% compared with other functions and KMGPR can reduce the computational complexity by about 7% to 20% with no obvious effects on accuracy.

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