LQG and Gaussian process techniques: For fixed-structure wind turbine control

Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression

Far offshore wind conditions in scope of wind energy

To predict the available capacity and state of health of lithium ion batteries by using Gaussian process regression, one of the crucial tasks is to choosing the covariance function. This paper proposes a method which can fulfill the Gaussian process regression by using most proper covariance functions or the optimal combination. First, a variety of typical functions are tried to fit the battery experimental data points with least square method, which can give us a valuable interpretation of the properties of the data; next, the three functions with minimum root mean square errors are selected to guide the choosing of the patterns of the covariance functions; then the Gaussian process regression is applied on the training data to determine the ultra-parameters included; finally, we use the Gaussian process model to predict the latter cycle capacities within the test data. Experiments show that the combination of selected covariance functions is effective and can be applied on predictions with different batteries. Also, the method can reduce the time in applying Gaussian process regression by determination of the covariance function quickly.

Smart Wind Turbine: Analysis and Autonomous Flap

This paper proposes the application of Gaussian process regression (GPR) as an alternative regression model to resolve the hard overlapped electrochemical signals belonging to the 2,4,6trichlorophenol/2,6-dichlorophenol (TCP/DCP) system. Gaussian process derives from the perspective of Bayesian non-parametric regression methods, in terms of the parameterization of the covariance function, results in its good performance for the development of a calibration model for both linear and non-linear data sets. The multivariate regression model developed by GPR was compared with some traditional regression methods such as partial least squares regression (PLSR), and support vector regression (SVR). The comparative results were satisfied. The satisfactory results obtained through GPR method suggest that it can be used as a more effective and promising tool for multivariate regression tasks than the others.

<div class="section abstract"><div class="htmlview paragraph">The advancement of Machine-learning (ML) methods enables data-driven creation of Reduced Order Models (ROMs) for automotive components and systems. For example, Gaussian Process Regression (GPR) has emerged as a powerful tool in recent years for building a static ROM as an alternative to a conventional parametric model or a multi-dimensional look-up table. GPR provides a mathematical framework for probabilistically representing complex non-linear behavior. Today, GPR is available in various programing tools and commercial CAE packages. However, the application of GPR is system dependent and often requires careful design considerations such as selection of input features and specification of kernel functions. Hence there is a need for GPR design optimization driven by application requirements. For example, a moving window size for training must be tuned to balance performance and computational efficiency for tracking changing system behavior. In this paper, a detailed design evaluation of GPR is conducted for the characterization of an engine disconnect clutch in P2 hybrid electric vehicle. Specifically, a clutch transfer function is constructed using GPR that maps actuator pressure to clutch torque. The disconnect clutch exhibits highly non-linear behaviors with a distinct hysteresis loop. A casual application of GPR results in a misrepresentation of clutch behaviors with a risk of overfitting. This paper first describes a process to select input features based on statistical measures. Several pre-defined kernel functions and their combinations are evaluated for generalization capability and computational efficiency. A size of data from a moving window is also evaluated for its effect on training errors as well as efficiency. The optimized GPR is applied to a set of disconnect clutch engagement data obtained from a long drive sequence for enabling accurate tracking of clutch behaviors in vehicles. This paper concludes with a set of recommendations for successfully deploying powerful GPR tool for addressing real-world automotive problems.</div></div>

Detecting small planets via the radial velocity (RV) method remains challenged by signals induced by stellar variability, versus the effects of the planet(s). Here, we explore using Gaussian process (GP) regression with Transiting Exoplanet Survey Satellite (TESS) photometry in modeling RVs to help to mitigate stellar jitter from oscillations and granulation for exoplanet detection. We applied GP regression to simultaneous TESS photometric and RV data of HD 5562, a G-type subgiant ( M ⋆ = 1.09 M ⊙ , R ⋆ = 1.88 R ⊙ ) with a V magnitude of 7.17, using photometry to inform the priors for RV fitting. The RV data is obtained by the Magellan Planet Finder Spectrograph. The photometry-informed GP regression reduced the RV scatter of HD 5562 from 2.03 to 0.51 m s −1 . We performed injection and recovery tests to evaluate the potential of GPs for discovering small exoplanets around evolved stars, which demonstrate that the GP provides comparable noise reduction to the binning method. We also found that the necessity of photometric data depends on the quality of the RV dataset. For long baseline and high-cadence RV observations, GP regression can effectively mitigate stellar jitter without photometric data. However, for intermittent RV observations, incorporating photometric data improves GP fitting and enhances detection capabilities.

Daily evaporative loss (Ep) forecasting models are decisive tools with potential applications in hydrology, the design of water systems, urban water assessments, and irrigation management. This paper performs a case study for forecasting daily Ep for Brisbane city using least-square support-vector regression (LSSVR). A limited set of predictor data with solar radiation and exposure, maximum/minimum temperatures, wind speed, and precipitation (March 1, 2014 to March 31, 2015) is adopted to develop the predictive model. The results are evaluated with Gaussian process regression (GPR), minimax probability machine regression (MPMR), and genetic programming (GP) models. In the testing phase, a correlation coefficient of 0.895 is attained between the observed and forecasted Ep by LSSVR that contrasted 0.875 (GPR), 0.864 (MPMR), and 0.628 (GP). A sensitivity test of predictor variables shows that approximately 28.5% of features are extracted from solar radiation data with 18.1% (wind speed), 16.6% (precipitation), and 10–15% (minimum and maximum temperature). The root-mean square error for LSSVR is lower than the GPR, MPMR, and GP models by 16.2, 11.4, and 79.4%, and the cumulative frequency of forecasting error attained for LSSVR is the highest within the smallest error band. The results confirm the better utility of LSSVR in relation to GP, GPR, and MPMR models for forecasting daily evaporative loss.

This paper evaluates the potential of two machine learning approaches i.e. Support vector machine (SVR) and Gaussian processes (GP) regression to model the oblique load capacity of batter pile groups. Linear regression was used to compare the performance of both SVR and GP based regression approaches to model the oblique load. Data set used consists of 147 samples obtained from the laboratory experiments. Out of the total sample size, 105 randomly selected samples were used for training whereas remaining 42 were used for testing the models. Input data set consist of angle of oblique load, pile length, sand relative density, number of vertical piles, number of batter piles where as oblique load was considered as output. Two kernel functions i.e. Polynomial and radial based kernel function were used with both SVR and GP regression. A comparison of results suggest that radial basis function based SVR approach works well in comparison to GP and linear regression based approaches and it could successfully be employed in modelling the oblique load capacity of batter pile groups. Parametric analysis and sensitivity analysis suggest that loading angle, pile length and number of batter pile were important in prediction of oblique load.

The applicability of Gaussian process (GP) regression as modeling tool within a genetic algorithm (GA) antenna optimization scheme is demonstrated. GP regression has recently been shown to be an efficient alternative to artificial neural networks (ANNs) when many accurate and fast estimates of antenna performance parameters, usually only obtainable from computationally intensive full-wave analyses, are required. GP regression has the important advantage that far fewer free parameters need to be determined; it furthermore is not subject to the implementation issues associated with neural nets. By using genetic algorithm optimization, two kinds of CPW-fed slot antennas were designed to operate within pre-specified frequency bands: an ultrawide-band (UWB) slot antenna with U-shaped tuning stub, and a dual-band slot loop antenna with separate T-shaped and insert tuning stubs. In both cases, the associated modeling problems were challenging, involving highly nonlinear mappings of several tunable geometry variables and frequency to S 11. This was especially so for the dual-band antenna, which had to operate in precisely positioned, comparatively narrow (compared to the UWB case) frequency bands (GSM and DECT) delineated by rapid changes in |S 11|. Despite using relatively few training data points given the dimensionality of the input spaces (a highly desirable model feature), the respective Gaussian process models were sufficiently accurate for the GA optimizer to yield antenna geometries that met the bandwidth specifications with ease, as was confirmed by comparison to moment-method-based simulations.

Mechanical property prediction plays a crucial role in the steel industry, enabling better materials selection and enhancing production efficiency. In this study, we propose a novel framework that facilitates optimal materials selection for steel’s alloying elements, based on accurate predictions of the Jominy hardness curve. Leveraging Gaussian Process (GP) regression, we provide probabilistic predictions of steel hardenability characteristics from alloy element composition. Taking it a step further, our framework incorporates these accurate predictions into a constrained optimization process, yielding optimal compositions that reduce overall spending while meeting performance specifications. Through data obtained from 1080 steel samples, our GP regression model exhibits high accuracy, achieving an RMSE of 1.37 and showcasing significant improvements in the field. Moreover, our constrained optimization utilizing the GP model and historical market data reveals an average cost reduction of 18% on alloying element expenses, highlighting the tangible cost-saving potential of this approach. By leveraging Gaussian Process (GP) regression, we not only achieve accurate predictions of the Jominy hardness curve based on alloy element composition, but we also introduce a crucial element of uncertainty quantification. This empowers us to place trust in the results of our optimization process, ensuring robust and reliable materials selection. The integration of GP regression and optimization provides a powerful tool for achieving cost-effective materials selection and marks a significant advancement compared to existing studies. This research underscores the promise of machine learning in the steel industry, demonstrating its ability to yield substantial cost savings and enhance decision-making in materials selection.

The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25% and 96.71%, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively.

Earth observation from satellite sensory data pose challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression and other kernel methods have excelled in biophysical parameter estimation tasks from space. GP regression is based on solid Bayesian statistics, and generally yield efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations is available though. In this work, we review three GP models that respect and learn the physics of the underlying processes in the context of inverse modeling. First, we will introduce a Joint GP (JGP) model that combines in situ measurements and simulated data in a single GP model. Second, we present a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical models of the system. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Finally, we present an Automatic Gaussian Process Emulator (AGAPE) that approximates the forward physical model via interpolation, reducing the number of necessary nodes. Empirical evidence of the performance of these models will be presented through illustrative examples of vegetation monitoring and atmospheric modeling.

Gaussian process regression for seismic fragility assessment of building portfolios

This paper examines experimental design procedures used to develop surrogates of computational models, exploring the interplay between experimental designs and approximation algorithms. We focus on two widely used approximation approaches, Gaussian process (GP) regression and nonintrusive polynomial approximation. First, we introduce algorithms for minimizing a posterior integrated variance (IVAR) design criterion for GP regression. Our formulation treats design as a continuous optimization problem that can be solved with gradient-based methods on complex input domains without resorting to greedy approximations. We show that minimizing IVAR in this way yields point sets with good interpolation properties and that it enables more accurate GP regression than designs based on entropy minimization or mutual information maximization. Second, using a Mercer kernel/eigenfunction perspective on GP regression, we identify conditions under which GP regression coincides with pseudospectral polynomial approximation. ...