A Gaussian Process Regression Approach to Cooperative Sampling by Underwater Gliders

An energy performance certificate (EPC) provides information on the energy performance of an energy system. The objective of this research aimed at obtaining a predictive model for early detection of thermal power efficiency (TPE) for energy conversion and preservation in buildings. This article expounds a sound and solid nonparametric Bayesian technique known as Gaussian process regression (GPR) approach, based on a set of data collected from different dwellings in an oceanic climate. Firstly, this model introduces the relevance of each predictive variable on energy performance in residential buildings. The second result refers to the statement that we can predict successfully the TPE by using this model. A coefficient of determination equal to 0.9687 was thus established in order to predict the TPE from the observed data, using the GPR approach in combination with the differential evolution (DE) optimiser. The concordance between experimental observed data and the predicted data from the best-proposed novel hybrid DE/GPR-relied model demonstrated here the adequate efficiency of this innovative approach.

Experimental validation of data-driven reactive control strategy for wave energy converters: A Gaussian process regression approach

ABSTRACTIn this paper, a Gaussian process regression (GPR)-based novel method is proposed for non-linear aerodynamic modelling of the aircraft using flight data. This data-driven regression approach uses the kernel-based probabilistic model to predict the non-linearity. The efficacy of this method is examined and validated by estimating force and moment coefficients using research aircraft flight data. Estimated coefficients of aerodynamic force and moment using GPR method are compared with the estimated coefficients using maximum-likelihood estimation (MLE) method. Estimated coefficients from the GPR method are statistically analysed and found to be at par with estimated coefficients from MLE, which is popularly used as a conventional method. GPR approach does not require to solve the complex equations of motion. GPR further can be directed for the generalised applications in the area of aeroelasticity, load estimation, and optimisation.

Continuum robots have the potential to enable new applications in medicine, inspection, and countless other areas due to their unique shape, compliance, and size. Excellent progress has been made in the mechanical design and dynamic modeling of continuum robots, to the point that there are some canonical designs, although new concepts continue to be explored. In this paper, we turn to the problem of state estimation for continuum robots that can been modeled with the common Cosserat rod model. Sensing for continuum robots might comprise external camera observations, embedded tracking coils, or strain gauges. We repurpose a Gaussian process (GP) regression approach to state estimation, initially developed for continuous-time trajectory estimation in SE(3). In our case, the continuous variable is not time but arclength and we show how to estimate the continuous shape (and strain) of the robot (along with associated uncertainties) given discrete, noisy measurements of both pose and strain along the length. We demonstrate our approach quantitatively through simulations as well as through experiments. Our evaluations show that accurate and continuous estimates of a continuum robot’s shape can be achieved, resulting in average end-effector errors between the estimated and ground truth shape as low as 3.5 mm and 0.016° in simulation or 3.3 mm and 0.035° for unloaded configurations and 6.2 mm and 0.041° for loaded ones during experiments, when using discrete pose measurements.

A practical example of a theory-guided data science case study is presented to evaluate the potential of the Diyab formation, an Upper Jurassic interval, source rock of some of the largest reservoirs in the Arabian Peninsula. A workflow base on a three-step approach combining the physics of logging tool response and a probabilistic machine-learning algorithm was undertaken to evaluate four wells of the prospect. At first, a core-calibrated multi-mineral model was established on a concept well for which an extensive suite of logs and core measurements had been acquired. To transfer the knowledge gained from the latter physics-driven interpretation onto the other data-scarce wells, the relationship between the output rock and fluid volumes and their input log responses was then learned by means of a Gaussian Process Regression (GPR). Finally, once trained on the key well, the latter probabilistic algorithm was deployed on the three remaining wells to predict reservoir properties, quantify resource potential and estimate volumetric-related uncertainties. The physics-informed machine-learning approach introduced in this work was found to provide results which matches with the majority of the available core data, while discrepancies could generally be explained by the occurrence of laminations which thickness are under the resolution of nuclear logs. Overall, the GPR approach seems to enable an efficient transfer of knowledge from data-rich key wells to other data-scarce wells. As opposed to a more conventional formation evaluation process which is carried out more independently from the key well, the present approach ensures that the final petrophysical interpretation reflects and benefits from the insights and the physics-driven coherency achieved at key well location.

In this paper, we evaluate the possibility of using a machine learning Gaussian Process Regression (GPR) approach to monitor Chlorophyll-a content in Arctic waters by using the Sentinel 3 Ocean and Land Color Instrument. We develop the GPR model on a synthetic dataset, which represents both open ocean and coastal Arctic waters. This allows the model to be exposed to and trained on data from both kinds of aquatic environments. The chosen GPR model has previously been trained and tested in a different aquatic environment, representing a variety of complexity conditions, where it was demonstrated to have strong generalization capabilities. Our results suggest that this model can also be used for diverse Arctic water conditions as well.

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

We demonstrate how the Gaussian process regression approach can be used to efficiently reconstruct free energy surfaces from umbrella sampling simulations. By making a prior assumption of smoothness and taking account of the sampling noise in a consistent fashion, we achieve a significant improvement in accuracy over the state of the art in two or more dimensions or, equivalently, a significant cost reduction to obtain the free energy surface within a prescribed tolerance in both regimes of spatially sparse data and short sampling trajectories. Stemming from its Bayesian interpretation the method provides meaningful error bars without significant additional computation. A software implementation is made available on www.libatoms.org .

The process of di‐bittering of mosambi peel powder (MPP) using sodium chloride solution (0, 5, and 10%) was optimized. The optimization was based on the acceptable taste, maximum total polyphenol content, and maximum antioxidant properties of peel powder. Support vector machine (SVM)‐based classification model was developed for sensory evaluation. Artificial neural network (ANN) and Gaussian process regression (GPR) was used for development of regression models to predict total polyphenol and total antioxidant properties. The two combinatorial models of SVM–ANN and SVM–GPR were developed for optimization using genetic algorithm. The optimized powder had total polyphenol content and antioxidant properties of 0.65 ± 0.10% and 57.05 ± 0.08%, respectively, while taste was in acceptable limits and showed good, swelling power, water binding capacity, oil binding capacity, emulsion stability, and emulsion activity. The optimized dibittered powder also showed good results for in vitro glucose retardation index and showed antimicrobial activity against some pathogenic microorganisms.Practical ApplicationsMosambi peel powder is rich in dietary fiber and other bioactive compounds. However, its utilization is limited due to the presence of some bitter compounds. The present study deals with the optimization of di‐bittering process of mosambi peel. The optimized powder had convincing functional properties and hence opens the doors for its commercial application in food industry. Mosambi peel powder being rich in dietary finder can enhance the fiber content of same fiber deficient foods.

Reliable and comprehensive predictive tools for the frictional pressure drop (FPD) are of particular importance for systems involving two-phase flow condensation. However, the available models are only applicable to specific operating conditions and channel sizes. Thus, this study aims at developing universal models to estimate the FPD during condensation inside smooth mini/micro and conventional (macro) channels. An extensive databank, comprising 8037 experimental samples and 23 working fluids from 50 reliable sources, was prepared to achieve this target. A comprehensive investigation on the literature models reflected the fact that all of them are associated with high deviations, and their average absolute relative errors (AAREs) exceed 26%. Hence, after identifying the most effective input variables through the Spearman's correlation analysis, three soft-computing paradigms, i.e., multilayer perceptron (MLP), gaussian process regression (GPR) and radial basis function (RBF) were employed to establish intelligent and dimensionless predictive tools for the FPD based on the separated model suggested by Lockhart and Martinelli. Among them, the most accurate results were presented by the GPR approach with AARE and R2 values of 4.10%, 99.23% respectively, in the testing step. The truthfulness and applicability of the models were explored through an array of statistical and visual analyses, and the results affirmed the obvious superiority of the newly proposed approaches over the literature correlations. Furthermore, the novel predictive tools excellently described the physical variations of the condensation FPD versus the operating parameters. Ultimately, the order of importance of factors in controlling the condensation FPD was clarified by a sensitivity analysis.

Chlorophyll-a (hereafter referred to as Chl-a) is a recognized indicator for phytoplankton abundance and biomass –hence, an effective estimation of the trophic condition– of water bodies as lakes, reservoirs and oceans. Indeed, Chl-a is the primary molecule responsible for photosynthesis. A strong and robust Bayesian nonparametric technique, termed Gaussian process regression (GPR) approach, for foretelling the dependent variable Chl-a concentration in Tanes reservoir from a dataset concerning to 268 samples is shown in this paper. Ten years (2006–2015) of monitoring water quality variables (biological and physico-chemical independent variables) in the Tanes reservoir were used to build this mathematical GPR-relied model. As an optimizer, the method known as Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGSB) iterative algorithm was used; this allows the selection of kernel optimal parameters during the GPR training phase, which greatly determines the regression precision. The results of the current investigation can be summarized in two. Firstly, the relevance of each input variable on Chl-a concentration in Tanes reservoir is determined. Secondly, the Chl-a can be successfully predicted using this hybrid LBFGSB/GPR–relied model (R2 and r values were 0.8597 and 0.9306, respectively). The concordance between observed data and the model clearly proves the high efficiency of this innovative approach.

Heavy metals such as arsenic can be effectively removed through adsorption. Through material property evaluation and adsorption parameter optimization, machine learning (ML) modeling provides an alternative to lengthy laboratory experimentation. In this work, adsorption data from an earlier study employing a waste-material composite were used. To create prediction models, four non-neural network algorithms - support vector machines (SVM), Gaussian process regression (GPR), linear regression, and ensemble approaches - were used and contrasted with neural network algorithms. Nine predictors were utilized, ranging from adsorbent composition alterations to experimental circumstances. Using principal component analysis (PCA) and feature selection, together with the F-test and minimum redundancy maximum relevance (MRMR) algorithms for feature reduction, optimization was accomplished. With an R-squared of 0.939, mean absolute error (MAE) of 5.778, and root mean squared error (RMSE) of 7.119 for training and an R-squared of 0.942, MAE of 5.450, and RMSE of 6.870 for testing, the optimized GPR method offered the best predictive performance. The best R-squared values found for other algorithms were: SVM (0.922), linear regression (0.925), and ensemble (0.927). The most important variables influencing adsorption efficiency were initial arsenic concentration, time, and the iron salt content. Local interpretable model-agnostic explanations (LIME), partial dependence plot (PDP), and Shapley additive explanations (SHAP) plots were used to explain these results. This work shows that, based on model-derived parameters, non-neural network algorithms may efficiently simulate and optimize arsenic adsorption tests, providing a trustworthy substitute for neural network techniques and markedly increasing adsorption efficiency.

A Gaussian process regression (GPR) approach for directly constructing the canonical polyadic decomposition (CPD) of a multidimensional potential energy surface (PES) by discrete training energies is proposed and denoted by CPD-GPR. The present CPD-GPR method requires the kernel function in a product of a series of one-dimensional functions. To test CPD-GPR, the reactive probabilities of H + H2 as a function of kinetics energy are performed. Comparing the dynamics results computed by the CPD-GPR PES with those by the original PES, a good agreement between these results can be clearly found. Discussions on the previous algorithms for building the decomposed form are also given. We further show that the CPD-GPR method might be the general algorithm for building the decomposed form. However, further development is needed to reduce the CPD rank. Therefore, the present CPD-GPR method might be helpful to inspire ideas for developing new tools in building decomposed potential functions.

Formation control of discrete-time nonlinear multi-glider systems for both leader–follower and no-leader scenarios under switching topology: Cases for a fleet consisting of a wave glider and multiple underwater gliders

Users often struggle to enter text accurately on touchscreen keyboards. To address this, we present a flexible decoder for touchscreen text entry that combines probabilistic touch models with a language model. We investigate two different touch models. The first touch model is based on a Gaussian Process regression approach and implicitly models the inherent uncertainty of the touching process. The second touch model allows users to explicitly control the uncertainty via touch pressure. Using the first model we show that the character error rate can be reduced by up to 7% over a baseline method, and by up to 1.3% over a leading commercial keyboard. Using the second model we demonstrate that providing users with control over input certainty reduces the amount of text users have to correct manually and increases the text entry rate.