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Power characteristics are important indicators of fuel cell performance. In the actual operation of fuel cells, changes in operating conditions lead to variations in their power characteristics. Therefore, it is imperative to explore the impact of operating conditions on power characteristics. This paper analyzes the factors influencing fuel cell power and uses sensitivity analysis to investigate how different factors affect fuel cell performance. The operating parameters are optimized using a Bayesian-optimized Gaussian process regression model. The research results indicate that temperature has the greatest impact on fuel cell power, followed by stoichiometry and backpressure. The Bayesian-optimized Gaussian process regression model performs the best, reducing its RSME from 0.1 to 0.0556. Residual analysis and regression characteristic analysis verify the optimized model's improved fitting and regression characteristics. Based on the Bayesian–Gaussian process regression model, the optimized operating parameters are obtained for maximum power: a temperature of 80 °C, stoichiometry of 4, and backpressure of 1.7 bar. This paper provides theoretical support for improving fuel cell performance. [ABSTRACT FROM AUTHOR]

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO's feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model's performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs' appropriate parameters with high precision. [ABSTRACT FROM AUTHOR]

Time series classification tasks play a crucial role in extracting relevant information from data equipped with a temporal structure. In various scientific domains, such as biology or finance, this kind of data comes from complex and hardly predictable phenomena. Therefore, classification algorithms for time series should be able to deal with the uncertainty contained in data and capture the relevant statistical properties of the underlying phenomenon. The main object of interest of this work is the development of a model for time series that tackles the classification task by interpreting time series as realisations of stochastic processes, the natural mathematical description of chaotic behaviour. The focus thus is on time series that can be thought as signals of some nature, and that convey some kind of statistical information. We propose a data-driven feature extraction model for time series built upon a Gaussian process based data augmentation and on the expected signature. The signature is a fundamental object that describes paths, much alike Fourier or wavelet expansion, but in a non-linear fashion. Likewise, the expected signature provides a statistical description of the law of stochastic processes. One of the main features is that an optimal feature extraction is learnt through the supervised task that uses the model. The model can be adapted to more complicated supervised tasks, as it integrates seamlessly in a neural network architecture and is fully compatible with back-propagation, and it can be easily accommodated to perform regressive tasks. The effectiveness of the model is demonstrated with numerical experiments on some benchmark time series.

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO’s feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model’s performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs’ appropriate parameters with high precision.

AbstractThe peak roof drift ratio is one of the most important engineering parameters to describe the expected seismic damage in a building. A predictive model of the drift ratio was developed using a machine learning approach (Gaussian process regression model) on a dataset of approximately 11,800 records from 34 monitored buildings in Japan. Four predictors for ground motion and three predictors for building vulnerability are used in the machine-learning modelling. The residual analysis shows a reduction of 50% compared to the state of the art. The Gaussian process regression model is applied in a second analysis on an original dataset of approximately 4,500 records for 127 monitored buildings in Italy. A satisfactory comparison emerges by comparing the drift ratio prediction map with the observed damage pattern produced by satellite imagery for a test site in central Italy after the 2009 earthquake. The drift ratio map plays an important role in the simulation of an earthquake scenario at regional scale, which is needed by Civil Protection for emergency planning and management activities.

Measuring weight during cattle growth is essential for determining their status and adjusting the feed amount. Cattle must be weighed on a scale, which is laborious and stressful and could hinder growth. Therefore, automatically predicting cattle weight could reduce stress on cattle and farm laborers. This study proposes a prediction system to measure the change in weight automatically during growth using three regression models, using environmental factors, feed intake, and weight during the period. The Bayesian inference and likelihood estimation principles estimate parameters that determine the models: the weighted regression model (WRM), Gaussian process regression model (GPRM), and Gaussian process panel model (GPPM). A posterior distribution was derived using these parameters, and a weight prediction system was implemented. An experiment was conducted using image data to evaluate model performance. The GPRM with the squared exponential kernel had the best predictive power. Next, GPRMs with polynomial and rational quadratic kernels, the linear model, and WRM had the next-best predictive power. Finally, the GPRM with the linear kernel, the linear model, and the latent growth curve model, and types of GPPM had the next-best predictive power. GPRM and WRM are statistical probability models that apply predictions to the entire cattle population. These models are expected to be useful for predicting cattle growth on farms at a population level. However, GPPM is a statistical probability model designed for measuring the weight of individual cattle. This model is anticipated to be more efficient when predicting the weight of individual cattle on farms. [ABSTRACT FROM AUTHOR]

In this paper, a new interval finite element model updating method is proposed for interval identification of structural parameters based on meta-model and response surface reconstruction. The lower and upper bounds of the uncertain structural parameters are determined by solving the optimization problem which minimizes the difference between the interval of the predicted and measured responses. The response surface models are reconstructed based on the resampling technique for mapping the relationship between a single input and a single output. Then the accurate interval of the responses during the iteration step can be efficiently estimated using the vertex method. Meanwhile, the Gaussian process regression model (GPRM) is constructed as the meta-model to replace the finite element model for calculating the responses of the system to improve computational efficiency. Several numerical and experimental examples are investigated to elucidate the feasibility of the proposed method in the interval identification of structural parameters. Obtained outcomes have demonstrated that the proposed method outperforms many existing approaches in the literature, especially for the nonlinear monotonously non-increasing problem. [ABSTRACT FROM AUTHOR]

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Measuring weight during cattle growth is essential for determining their status and adjusting the feed amount. Cattle must be weighed on a scale, which is laborious and stressful and could hinder growth. Therefore, automatically predicting cattle weight could reduce stress on cattle and farm laborers. This study proposes a prediction system to measure the change in weight automatically during growth using three regression models, using environmental factors, feed intake, and weight during the period. The Bayesian inference and likelihood estimation principles estimate parameters that determine the models: the weighted regression model (WRM), Gaussian process regression model (GPRM), and Gaussian process panel model (GPPM). A posterior distribution was derived using these parameters, and a weight prediction system was implemented. An experiment was conducted using image data to evaluate model performance. The GPRM with the squared exponential kernel had the best predictive power. Next, GPRMs with polynomial and rational quadratic kernels, the linear model, and WRM had the next-best predictive power. Finally, the GPRM with the linear kernel, the linear model, and the latent growth curve model, and types of GPPM had the next-best predictive power. GPRM and WRM are statistical probability models that apply predictions to the entire cattle population. These models are expected to be useful for predicting cattle growth on farms at a population level. However, GPPM is a statistical probability model designed for measuring the weight of individual cattle. This model is anticipated to be more efficient when predicting the weight of individual cattle on farms.

This study considers an adaptive cruise control problem of connected vehicles in the vehicular ad‐hoc network and proposes a Gaussian learning‐based fuzzy predictive cruise control approach to enhance the fuel efficiency and safety of the connected vehicles in a vehicle‐following scenario. First, a Gaussian process regression model is introduced and trained with real data to estimate the future acceleration of the preceding vehicle over the prediction horizon. Moreover, with assessing traffic scenarios, the weights characterising the importance of individual performance are adjusted by a fuzzy decision method in real time. Then a fuzzy predictive cruise controller is obtained by online solving a constrained receding horizon optimal control problem with a changing cost function and acceleration prediction of the preceding vehicle. Finally, through CarSim/Simulink co‐simulation, it is shown that the proposed approach has an improvement in fuel economy and safety compared with conventional predictive cruise control algorithms.

Wireless sensor nodes equipped with multiple sensors often have limited energy availability. To optimize the energy sustainability of such sensor hubs, in this paper a novel adaptive sensor selection framework is proposed. Multiple sensors monitoring different parameters in the same environment often possess cross-correlation, which makes the system predictive. To this end, a learning-based optimization strategy is developed using Upper Confidence Bound algorithm to select an optimum active sensor set in a measurement cycle based on the cross-correlations among the parameters, energy consumed by the sensors, and the energy available at the node. Further, a Gaussian process regressor-based prediction model is used to predict the parameter values of inactive sensors from the cross-correlated parameters of active sensors. To evaluate the performance of the proposed framework in real-life applications, an air pollution monitoring sensor node consisting of seven sensors is deployed in the campus that collects data at a default high sampling rate. Simulation results validate the efficiency and efficacy of the proposed framework. Compared to the current state-of-the-art the proposed algorithm is 54% more energy efficient, with complexity O(2P) for P sensors in the node, while maintaining an acceptable range of sensing error. [ABSTRACT FROM AUTHOR]

• A lag time Gaussian process regression model for groundwater chemicals was proposed. • Timing of 5.7 million chemical sites contaminating shallow groundwater were analyzed. • Global maps of lag time of different chemicals were estimated. • The mean ln(K d) is the primary control factor to natural logarithm of the lag time. Dissolved chemical transport in soils can delay groundwater contamination for years. These lag times, τ, are controlled by location-dependent hydrologic, soil, and chemical properties. Despite thousands of sites with known groundwater contamination globally, numerous sites with unidentified chemical leaks could take decades to reach and degrade groundwater. Here, we quantify the potential for contamination (n = 621 dissolved chemicals) to have reached water tables below 2.7 million chemical sites since their construction and when in the future groundwater contamination could occur. For the most mobile contaminants, groundwater contamination could exceed the maximum contaminant level for about half of these sites (48.1 %, −3.7 %/+18.6 % with source and environmental uncertainties) as of 2021. However, most groundwater contaminants are less mobile and delayed in the vadose zone, where substantially fewer sites could have already contaminated groundwater by 2021 for moderately mobile chemicals (K d = 1 L/kg, 23.3 % −8.1 %/+21.1 %) and even less mobile chemicals (K d = 100 L/kg, 0.3 % −0.1/+1.9 %). The modeled urgent (τ ≤ 10 yr) and cross-generational (10 < τ ≤ 100 yr) contamination potential patterns provide the first global sensitivity analysis of groundwater contaminants. These results highlight the need for chemical site monitoring, management, and planning, especially in the developing world. [ABSTRACT FROM AUTHOR]

The hydropower units have a complex structure, complicated and changing working conditions, complexity and a diversity of faults. Effectively evaluating the healthy operation status and accurately predicting the failure for the hydropower units using the real-time monitoring data is still a difficult problem. To this end, this paper proposes a prediction method for the early failure of hydropower units based on Gaussian process regression (GPR). Firstly, by studying the correlation between different monitoring data, nine state parameters closely related to the operation of hydropower units are mined from the massive data. Secondly, a health evaluation model is established based on GPR using the historical multi-dimensional monitoring information and fault-free monitoring data at the initial stage of unit operation. Finally, a condition monitoring directive based on the Mahalanobis distance (MD) is designed. The effectiveness of the proposed method is verified by several typical examples of monitoring data of a hydropower station in Guangxi, China. The results show that, in three cases, the abnormal conditions of the unit are found 2 days, 4 days and 43 days earlier than those of regular maintenances respectively. Therefore, the method can effectively track the change process of the operation state of hydropower units, and detect the abnormal operation state of hydropower units in advance. [ABSTRACT FROM AUTHOR]

Global Navigation satellite systems (GNSS) are predominantly affected by Ionospheric space weather. The GNSS signal delays due to ionospheric Total Electron Content (TEC) can be forecasted using advances in the emerging mathematics tools and algorithms. Machine learning algorithms such as Gaussian Process Regression (GPR) is considered in the present paper to implement in the forecasting of low-latitude ionospheric conditions. The GPS receiver data is obtained for 8 years (2009–2016) during 24th solar cycle from International GNSS Services (IGS) station located in Bengaluru (Geographical latitude: 12.97° N, Geographical longitude: 77.59° E), India. The performance of GPR model is validated using statistical parameters such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and correlation coefficient. The results of the proposed GPR model were compared with the Auto Regressive Moving Average (ARMA) model and Artificial Neural Networks (ANN) model during solar maximum period and descending phase of 24th solar cycle. The experiment results are evident that GPR model is significantly providing the promising results in forecasting the ionospheric time delays for GNSS signals. The outcome of the work can be useful to develop web based Ionospheric TEC forecasting system to alert the GNSS users. • The GPS-TEC variability is investigated during solar maximum period and descending phase of 24th solar cycle. • The performance of ARMA, ANN and GPR models are evaluated in forecasting the ionospheric delays for GPS signals. • GPR model has better predicted TEC than the existing ARMA and ANN models during geomagnetic quiet and storm periods. • Kernel-based approach of GPR model is well suitable to forecast the ionospheric TEC variations over low latitudes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a large-scale multiple testing procedure to find the significant sub-areas between two samples of curves automatically. The procedure is optimal in that it controls the directional false discovery rate at any specified level on a continuum asymptotically. By introducing a nonparametric Gaussian process regression model for the two-sided multiple test, the procedure is computationally inexpensive. It can cope with problems with multidimensional covariates and accommodate different sampling designs across the samples. We further propose the significant curve/surface, giving an insight on dynamic significant differences between two curves. Simulation studies demonstrate that the proposed procedure enjoys superior performance with strong power and good directional error control. The procedure is also illustrated with the application to two executive function studies in hemiplegia. [ABSTRACT FROM AUTHOR]

The hydropower units have a complex structure, complicated and changing working conditions, complexity and a diversity of faults. Effectively evaluating the healthy operation status and accurately predicting the failure for the hydropower units using the real-time monitoring data is still a difficult problem. To this end, this paper proposes a prediction method for the early failure of hydropower units based on Gaussian process regression (GPR). Firstly, by studying the correlation between different monitoring data, nine state parameters closely related to the operation of hydropower units are mined from the massive data. Secondly, a health evaluation model is established based on GPR using the historical multi-dimensional monitoring information and fault-free monitoring data at the initial stage of unit operation. Finally, a condition monitoring directive based on the Mahalanobis distance (MD) is designed. The effectiveness of the proposed method is verified by several typical examples of monitoring data of a hydropower station in Guangxi, China. The results show that, in three cases, the abnormal conditions of the unit are found 2 days, 4 days and 43 days earlier than those of regular maintenances respectively. Therefore, the method can effectively track the change process of the operation state of hydropower units, and detect the abnormal operation state of hydropower units in advance.

Identification of the factors influencing energy consumption in buildings is crucial for energy efficient control in the operation stage. By using a multi-variate approach in energy performance prediction, we can characterize the building energy usage with a few available variables. However, very few studies have applied the variables related to building operation for energy consumption in buildings. Especially, the importance of the use factor, which affects the energy consumption, varies depending on the usage of tenants. However, there is a lack of sufficient research on the energy consumption based on energy use factors of each tenant in a building. Therefore, in this study we propose an energy consumption model using data on the energy use factors, such as occupant schedule, operation, and equipment, especially with a focus on the tenants in buildings. In this study, we analyzed the ranking of variable importance using the Random Forest algorithm and verified the energy consumption results of individual, office, and retail tenants in commercial buildings using a Gaussian process regression model. The main contribution of this study is the identification of the influence of energy use factors on the energy consumption of each tenant, both office and retail, thereby developing an energy model. This study established a method to identify the combination of variables that could estimate the energy consumption. Moreover, it can be seen that the significant variables to consider for developing an energy model differ depending on the tenant use class, i.e., office or retail. [ABSTRACT FROM AUTHOR]

Uncertainties associated with estimates of model parameters are inevitable when simulating and modeling chemical processes and significantly affect safety, consistency, and decision making. Quantifying those uncertainties is essential for emulating the actual system behaviors because they can change the management recommendations that are drawn from the model. The use of conventional approaches for uncertainty quantification ( e.g ., Monte-Carlo and standard polynomial chaos methods) is computationally expensive for complex systems with a large/moderate number of uncertainties. This paper develops a two-stage approach to quantify the uncertainty of complex chemical processes with a moderate/large number of uncertainties (greater than 5). The first stage applies a multiplicative dimensional reduction method to approximate the variance-based global sensitivity measures (Sobol's method), and to simplify the model for the uncertainty quantification stage. The second stage uses the generalized polynomial chaos approach to quantify uncertainty of the simplified model from the first stage. A rigorous simulation illustrates the proposed approach using an interface between MATLAB and HYSYS for three complex chemical processes. The proposed method was compared with conventional approaches, such as the Quasi Monte-Carlo sampling-based method and standard polynomial chaos-based method. The results revealed the clear advantage of the proposed approach in terms of the computational efforts. [ABSTRACT FROM AUTHOR]

Reliable prediction of the transmembrane pressure (TMP) in membrane filtration systems often encounters different challenges in practice, including process nonlinearity, cycle-to-cycle change, and multiple operating conditions. In this work, an active learning TMP model with the hybrid structure is developed to predict the short-term fouling formation. The advantages of the physical and empirical models are integrated into this hybrid model. For construction of the empirical model, the Gaussian process regression model (GPRM) is adopted to approximate the complex fouling mechanism with fouling propensity in the short-term period. It can be a useful complement to the physical model with inaccuracy. Moreover, GPRM can simultaneously provide the prediction variance. Using this appealing property, the hybrid TMP model can be updated in an active and efficient manner without introducing additional unnecessary data samples. Consequently, accurate hybrid TMP models for the repetitive operations of membrane filtration processes are established. The superiority of the proposed hybrid TMP models is demonstrated through simulation and short-term experiments. The experimental results show the hybrid TMP models have better prediction performance (with R 2 values larger than 0.9) than the physical model (with R 2 about 0.5). [ABSTRACT FROM AUTHOR]

Short-term wind speed forecasting plays a major role in wind energy plant operations and the integration of wind power into traditional grid systems. This paper proposes a hybrid model, which is composed of the EWT (empirical wavelet transform), PACE (partial auto-correlation function) and GPR (Gaussian process regression) method, for short-term wind speed prediction. In this proposed approach, the EWT is employed to extract meaningful information from the wind speed series by designing an appropriate wavelet filter bank, and the GPR simulates the internal uncertainties and dynamic features of the wind speed time-series using inputs identified by the PACF. The hybrid GPR model can offer point predictions and interval estimations of future wind speed. Additionally, this study adopts a moving window approach in the prediction process to deal adequately with the training data set, thereby adapting to the time-varying characteristic of the wind speed. The proposed hybrid model was validated with real mean half-hour wind speed data and hourly wind speed data. The computational results show that the suggested hybrid model favorably improves point wind speed forecasts in comparison with other models and provides satisfactory interval wind speed prediction. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a Gaussian process regression (GPR) model which could adapt to both linear and nonlinear systems automatically without prior introduction of kernel functions. The applications of GPR model for two industrial examples are presented. The first example addresses a biological anaerobic system in a wastewater treatment plant and the second models a nonlinear dynamic process of propylene polymerization. Special emphasis is placed on signal preprocessing methods including the Savitzky-Golay and Kalman filters. Applications of these filters are shown to enhance the performance of the GPR model, and facilitate bias update leading to reduction of the offset between the predicted and measured values. [Copyright &y& Elsevier]

A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modeled by a Gaussian process regression model and the mean structure modeled by a functional regression model. The model allows the inclusion of covariates in both the covariance structure and the mean structure. It models the nonlinear relationship between a functional output variable and a set of functional and nonfunctional covariates. Several applications and simulation studies are reported and show that the method provides very good results for curve fitting and prediction. [ABSTRACT FROM AUTHOR]

• A new interval model updating method is developed based on universal grey mathematics. Especially, the method has a good performance when facing interval model updating problems with the nonlinear monotonic relationship between the input and the output. • As an interval model updating method, the method has the advantage of a low data requirement. The method only needs to know the interval of output. • Gaussian process regression model (GPRM) was used as the meta-model in this paper. Therefore, the present method is highly computational efficiency. In this paper, a new method is proposed to deal with the interval model updating problem using universal grey mathematics and Gaussian process regression model (GPRM). The nonlinear relationship between the input and the output generally exists in the interval model updating problem, which has not been specially discussed in the current papers. In order to handle this issue better, the method concerned about the nonlinear monotonic relationship was proposed. It treats the interval model updating problem in a deterministic framework by universal grey mathematics. What's more, GPRMs were used as the meta-model to improve computational efficiency. Two numerical examples and one experimental example were made in this paper: in the first numerical example, the method was validated using a three-degree-of-freedom spring-mass system; in the second numerical example, the ability of the method to deal with nonlinear problems was verified with a set of aluminum alloy plates; in the last experimental example, the experiment of a set of aluminum alloy plates was used to illustrate the method, where the thickness of the aluminum alloy plate was used as the updating parameter. [ABSTRACT FROM AUTHOR]