scholarly journals Gaussian Process Modeling of Specular Multipath Components

2020 ◽  
Vol 10 (15) ◽  
pp. 5216
Author(s):  
Anh Hong Nguyen ◽  
Michael Rath ◽  
Erik Leitinger ◽  
Khang Van Nguyen ◽  
Klaus Witrisal
Keyword(s):  
Gaussian Process ◽  
Incident Angle ◽  
Gaussian Process Regression ◽  
Training Data ◽  
Ultra Wideband ◽  
Model Parameters ◽  
Angle Of Arrival ◽  
Data Set ◽  
Arrival Angle ◽  
Arbitrary Position

The consideration of ultra-wideband (UWB) and mm-wave signals allows for a channel description decomposed into specular multipath components (SMCs) and dense/diffuse multipath. In this paper, the amplitude and phase of SMCs are studied. Gaussian Process regression (GPR) is used as a tool to analyze and predict the SMC amplitudes and phases based on a measured training data set. In this regard, the dependency of the amplitude (and phase) on the angle-of-arrival/angle-of-departure of a multipath component is analyzed, which accounts for the incident angle and incident position of the signal at a reflecting surface—and thus for the reflection characteristics of the building material—and for the antenna gain patterns. The GPR model describes the similarities between different data points. Based on its model parameters and the training data, the amplitudes of SMCs are predicted at receiver positions that have not been measured in the experiment. The method can be used to predict a UWB channel impulse response at an arbitrary position in the environment.

scholarly journals Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations

2021 ◽  
Author(s):  
Ryan Volz ◽  
Jorge L. Chau ◽  
Philip J. Erickson ◽  
Juha P. Vierinen ◽  
J. Miguel Urco ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Wind Velocity ◽  
Wind Field ◽  
Gaussian Process Regression ◽  
Small Scale ◽  
Model Parameters ◽  
Meteor Radar ◽  
Wind Fields ◽  
Data Set ◽  
Uncertainty Estimates

Abstract. Mesoscale dynamics in the mesosphere and lower thermosphere (MLT) region have been difficult to study from either ground- or satellite-based observations. For understanding of atmospheric coupling processes, important spatial scales at these altitudes range between tens to hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, and so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesocale dynamics on these scales using an increased number of detections and a diversity of viewing angles inherent to multistatic networks. In this work, we introduce a four dimensional wind field inversion method that makes use of Gaussian process regression (GPR), a non-parametric and Bayesian approach. The method takes measured projected wind velocities and prior distributions of the wind velocity as a function of space and time, specified by the user or estimated from the data, and produces posterior distributions for the wind velocity. Computation of the predictive posterior distribution is performed on sampled points of interest and is not necessarily regularly sampled. The main benefits of the GPR method include this non-gridded sampling, the built-in statistical uncertainty estimates, and the ability to horizontally-resolve winds on relatively small scales. The performance of the GPR implementation has been evaluated on Monte Carlo simulations with known distributions using the same spatial and temporal sampling as one day of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased and with statistical variances that depend on the geometry and are proportional to the prior velocity variances. A conservative and fast approach can be straightforwardly implemented by employing overestimated prior variances and distances, while a more robust but computationally intensive approach can be implemented by employing training and fitting of model parameters. The latter GPR approach has been applied to a 24-hour data set and shown to compare well to previously used homogeneous and gradient methods. Small scale features have reasonably low statistical uncertainties, implying geophysical wind field horizontal structures as low as 20–50 km. We suggest that this GPR approach forms a suitable method for MLT regional and weather studies.

INCREMENTAL SPARSE PSEUDO-INPUT GAUSSIAN PROCESS REGRESSION

2012 ◽  
Vol 26 (08) ◽  
pp. 1250019 ◽  
Author(s):  
HEUNG-IL SUK ◽  
YUZHUO WANG ◽  
SEONG-WHAN LEE
Keyword(s):  
Gaussian Process ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Performance Degradation ◽  
Training Data ◽  
Data Set ◽  
Significant Performance ◽  
Representational Power ◽  
Pseudo Data

In this paper, we devise a novel method that incrementally learns pseudo-data, which represent the whole training data set for Gaussian Process (GP) regression. The method involves sparse approximation of the GP by extending the work of Snelson and Ghahramani. We call the proposed method Incremental Sparse Pseudo-input Gaussian Process (ISPGP) regression. Unlike the Snelson and Ghahramani's work, the proposed ISPGP algorithm allows for training from either a huge amount of training data by scanning through it only once or an online incremental training data set. We also design a likelihood weighting scheme to incrementally determine pseudo-data while maintaining the representational power. Due to the nature of the incremental learning algorithm, the proposed ISPGP algorithm can theoretically work with infinite data to which the conventional GP or Sparse Pseudo-input Gaussian Process (SPGP) algorithm is not applicable. From our experimental results on the KIN40K data set, we can see that the proposed ISPGP algorithm is comparable to the conventional GP algorithm using the same number of training data. It also significantly reduces the computational cost and memory requirement in regression and is scalable to a large training data set without significant performance degradation. Although the proposed ISPGP algorithm performs slightly worse than Snelson and Ghahramani's SPGP algorithm, the level of performance degradation is acceptable.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

scholarly journals Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Siyu Ji ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Combined Cycle ◽  
Training Data ◽  
New Method ◽  
Model Parameters ◽  
Evaluation Function ◽  
Data Set ◽  
Standard Data ◽  
True Value ◽  
The Mean

Neural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. However, the system modal update occurs from time to time. Prediction using the original model parameters will cause the output of the model to deviate greatly from the true value. Traditional methods such as gradient descent and least squares methods are all centralized, making it difficult to adaptively update model parameters according to system changes. Firstly, in order to adaptively update the network parameters, this paper introduces the evaluation function and gives a new method to evaluate the parameters of the function. The new method without changing other parameters of the model updates some parameters in the model in real time to ensure the accuracy of the model. Then, based on the evaluation function, the Mean Impact Value (MIV) algorithm is used to calculate the weight of the feature, and the weighted data is brought into the established fault diagnosis model for fault diagnosis. Finally, the validity of this algorithm is verified by the example of UCI-Combined Cycle Power Plant (UCI-ccpp) simulation of standard data set.

scholarly journals Lost in Space: Geolocation in Event Data

2018 ◽  
Vol 7 (04) ◽  
pp. 871-888 ◽  
Author(s):  
Sophie J. Lee ◽  
Howard Liu ◽  
Michael D. Ward
Keyword(s):  
Learning Algorithm ◽  
Text Processing ◽  
Contextual Information ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Event Data ◽  
Data Set ◽  
N Gram ◽  
Automated Text Processing

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

scholarly journals An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 669 ◽  
Author(s):  
Eunseo Oh ◽  
Hyunsoo Lee
Keyword(s):  
Big Data ◽  
Gaussian Process ◽  
Missing Values ◽  
Gaussian Process Regression ◽  
Estimation Methods ◽  
Data Handling ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Industrial Big Data

The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statistical methods and machine learning techniques. Then, the extracted knowledge can be used for predictive maintenance or prognostic health management. However, it is difficult to gather complete data due to several issues in IIoT, such as devices breaking down, running out of battery, or undergoing scheduled maintenance. Data with missing values are often ignored, as they may contain insufficient information from which to draw conclusions. In order to overcome these issues, we propose a novel, effective missing data handling mechanism for the concepts of symmetry principles. While other existing methods only attempt to estimate missing parts, the proposed method generates a whole set of data set using Gaussian process regression and a generative adversarial network. In order to prove the effectiveness of the proposed framework, we examine a real-world, industrial case involving an air pressure system (APS), where we use the proposed method to make quality predictions and compare the results with existing state-of-the-art estimation methods.

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 Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators

2019 ◽  
Vol 39 (4) ◽  
pp. 405-413 ◽  
Author(s):  
Tiago M. de Carvalho ◽  
Eveline A. M. Heijnsdijk ◽  
Luc Coffeng ◽  
Harry J. de Koning
Keyword(s):  
Prostate Cancer ◽  
Simulation Model ◽  
Gaussian Process ◽  
Prediction Error ◽  
Life Histories ◽  
Gaussian Process Regression ◽  
Computation Time ◽  
Computational Effort ◽  
Sensitivity Analyses ◽  
Model Parameters

Background. Microsimulation models have been extensively used in the field of cancer modeling. However, there is substantial uncertainty regarding estimates from these models, for example, overdiagnosis in prostate cancer. This is usually not thoroughly examined due to the high computational effort required. Objective. To quantify uncertainty in model outcomes due to uncertainty in model parameters, using a computationally efficient emulator (Gaussian process regression) instead of the model. Methods. We use a microsimulation model of prostate cancer (microsimulation screening analysis [MISCAN]) to simulate individual life histories. We analyze the effect of parametric uncertainty on overdiagnosis with probabilistic sensitivity analyses (ProbSAs). To minimize the number of MISCAN runs needed for ProbSAs, we emulate MISCAN, using data pairs of parameter values and outcomes to fit a Gaussian process regression model. We evaluate to what extent the emulator accurately reproduces MISCAN by computing its prediction error. Results. Using an emulator instead of MISCAN, we may reduce the computation time necessary to run a ProbSA by more than 85%. The average relative prediction error of the emulator for overdiagnosis equaled 1.7%. We predicted that 42% of screen-detected men are overdiagnosed, with an associated empirical confidence interval between 38% and 48%. Sensitivity analyses show that the accuracy of the emulator is sensitive to which model parameters are included in the training runs. Conclusions. For a computationally expensive simulation model with a large number of parameters, we show it is possible to conduct a ProbSA, within a reasonable computation time, by using a Gaussian process regression emulator instead of the original simulation model.

Dealing with Observation Outages within Navigation Data using Gaussian Process Regression

2014 ◽  
Vol 67 (4) ◽  
pp. 603-615 ◽  
Author(s):  
Hongmei Chen ◽  
Xianghong Cheng ◽  
Haipeng Wang ◽  
Xu Han
Keyword(s):  
Kalman Filter ◽  
Theoretical Analysis ◽  
Gaussian Process ◽  
Inertial Navigation System ◽  
Dynamic Models ◽  
Gaussian Process Regression ◽  
Parametric Model ◽  
Integrated Navigation ◽  
Data Set ◽  
Navigation Data

Gaussian process regression (GPR) is used in a Spare-grid Quadrature Kalman filter (SGQKF) for Strap-down Inertial Navigation System (SINS)/odometer integrated navigation to bridge uncertain observation outages and maintain an estimate of the evolving SINS biases. The SGQKF uses nonlinearized dynamic models with complex stochastic nonlinearities so the performance degrades significantly during observation outages owing to the uncertainties and noise. The GPR calculates the residual output after factoring in the contributions of the parametric model that is used as a nonlinear SINS error predictor integrated into the SGQKF. The sensor measurements and SINS output deviations from the odometer are collected in a data set during observation availability. The GPR is then applied to predict SINS deviations from the odometer and then the predicted SINS deviations are fed to the SGQKF as an actual update to estimate all SINS biases during observation outages. We demonstrate our method's effectiveness in bridging uncertain observation outages in simulations and in real road tests. The results agree with the theoretical analysis, which demonstrate that SGQKF using GPR can maintain an estimate of the evolving SINS biases during signal outages.

scholarly journals Parameterization of wind evolution using lidar

2021 ◽  
Vol 6 (1) ◽  
pp. 61-91
Author(s):  
Yiyin Chen ◽  
David Schlipf ◽  
Po Wen Cheng
Keyword(s):  
Gaussian Process ◽  
Wind Field ◽  
Regression Models ◽  
Gaussian Process Regression ◽  
Evolution Model ◽  
Model Parameters ◽  
Turbulence Structures ◽  
Wind Turbine Control ◽  
Automatic Relevance Determination ◽  
Control Concept

Abstract. Wind evolution, i.e., the evolution of turbulence structures over time, has become an increasingly interesting topic in recent years, mainly due to the development of lidar-assisted wind turbine control, which requires accurate prediction of wind evolution to avoid unnecessary or even harmful control actions. Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into standard 3D simulations to provide a more realistic simulation environment for this control concept. Motivated by these factors, this research aims to investigate the potential of Gaussian process regression in the parameterization of wind evolution. Wind evolution is commonly quantified using magnitude-squared coherence of wind speed and is estimated with lidar data measured by two nacelle-mounted lidars in this research. A two-parameter wind evolution model modified from a previous study is used to model the estimated coherence. A statistical analysis is done for the wind evolution model parameters determined from the estimated coherence to provide some insights into the characteristics of wind evolution. Gaussian process regression models are trained with the wind evolution model parameters and different combinations of wind-field-related variables acquired from the lidars and a meteorological mast. The automatic relevance determination squared exponential kernel function is applied to select suitable variables for the models. The performance of the Gaussian process regression models is analyzed with respect to different variable combinations, and the selected variables are discussed to shed light on the correlation between wind evolution and these variables.

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