Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression

Monitoring of motor vehicle exhaust emissions using Gaussian process regression frame interpolation optical flow algorithm

In fluid pollutant monitoring, the spatial continuity of pixel motion is disrupted by infrared cameras, primarily due to factors like low frame rate. This disruption impedes the accurate capture of pollutant distribution and evolution, resulting in substantial errors in monitoring outcomes. To address this challenge, we introduce the Gaussian Process Regression Frame Interpolation Optical Flow (GPR-FIOF), aimed at restoring the spatial continuity of pixel motion. Consequently, this facilitates a more precise estimation of fluid pollutant motion. Experimental results from fluid simulations demonstrate that, when compared to conventional algorithms, GPR-FIOF significantly enhances accuracy and stability, improving by 80.30% and 66.39%, respectively. Field experiments employing infrared gas correlation spectroscopy methods revealed improvements in accuracy and stability of emission rate inversion results, with enhancements of 18.24% and 61.77%, respectively. GPR-FIOF effectively mitigates the disruption in spatial continuity, enhancing the accuracy of pollutant gas emission monitoring and bolstering its feasibility for environmental monitoring applications.

On the evaluating membrane flux of forward osmosis systems: Data assessment and advanced intelligent modeling

As an emerging desalination technology, forward osmosis (FO) can potentially become a reliable method to help remedy the current water crisis. Introducing uncomplicated and precise models could help FO systems' optimization. This paper presents the prediction and evaluation of FO systems' membrane flux using various artificial intelligence-based models. Detailed data gathering and cleaning were emphasized because appropriate modeling requires precise inputs. Accumulating data from the original sources, followed by duplicate removal, outlier detection, and feature selection, paved the way to begin modeling. Six models were executed for the prediction task, among which two are tree-based models, two are deep learning models, and two are miscellaneous models. The calculated coefficient of determination (R2 ) of our best model (XGBoost) was 0.992. In conclusion, tree-based models (XGBoost and CatBoost) show more accurate performance than neural networks. Furthermore, in the sensitivity analysis, feed solution (FS) and draw solution (DS) concentrations showed a strong correlation with membrane flux. PRACTITIONER POINTS: The FO membrane flux was predicted using a variety of machine-learning models. Thorough data preprocessing was executed. The XGBoost model showed the best performance, with an R2 of 0.992. Tree-based models outperformed neural networks and other models.

ABTCN: an efficient hybrid deep learning approach for atmospheric temperature prediction

Temperature prediction is an important and significant step for monitoring global warming and the environment to save and protect human lives. The climatology parameters such as temperature, pressure, and wind speed are time-series data and are well predicted with data driven models. However, data-driven models have certain constraints, due to which these models are unable to predict the missing values and erroneous data caused by factors like sensor failure and natural disasters. In order to solve this issue, an efficient hybrid model, i.e., attention-based bidirectional long short term memory temporal convolution network (ABTCN) architecture is proposed. ABTCN uses k-nearest neighbor (KNN) imputation method for handling the missing data. A bidirectional long short term memory (Bi-LSTM) network with self-attention mechanism and temporal convolutional network (TCN) model that aids in the extraction of features from complex data and prediction of long data sequence. The performance of the proposed model is evaluated in comparison to various state-of-the-art deep learning models using error metrics such as MAE, MSE, RMSE, and R2 score. It is observed that our proposed model is superior over other models with high accuracy.

An Exploratory Study of Laser Scribing Quality through Cross-Section Scribing Profiles

This article presents a novel approach for evaluating laser scribing quality through cross-section profiles generated from a three-dimensional optical profiler. Existing methods for assessing scribing quality only consider the width and depth of a scribe profile. The proposed method uses a cubic spline model for cross-section profiles. Two quality characteristics are proposed to assess scribing accuracy and consistency. Accuracy is measured by the ratio of the actual laser-scribed area to the target area (RA), which reflects the deviation from the desired profile. The mean square error (MSE) is a measure of how close each scribed cross-section under the same scribing conditions is to the fitted cubic spline model. Over 1370 cross-section profiles were generated under 171 scribing conditions. Two response surface polynomial models for RA and MSE were built with 18 scribing conditions with acceptable scribing depth and RA values. Both RA and MSE were considered simultaneously via contour plots. A scatter plot of RA and MSE was then used for Pareto optimization. It was found that the cross-sectional profile of a laser scribe could be accurately represented by a cubic spline model. A multivariate nonlinear regression model for RA and MSE identified pulse energy and repetition rate as the two dominant laser parameters. A Pareto optimization analysis further established a Pareto front, where the best compromised solution could be found.

A different method of fault feature extraction under noise disturbance and degradation trend estimation with system resilience for rolling bearings

Due to the effects of noise disturbances and system resilience, the current methods for rolling bearing fault feature extraction and degradation trend estimation can hardly achieve more satisfactory results. To address the above issues, we propose a different method for fault feature extraction and degradation trend estimation. Firstly, we preset the Bayesian inference criterion to evaluate the complexity of the denoised vibration signal. When its complexity reaches a minimum, the noise disturbances are exactly removed. Secondly, we define the system resilience obtained by the Bayesian network as the intrinsic index of the system, which is used to correct the equipment degradation trend obtained by the multivariate status estimation technique. Finally, the effectiveness of the proposed method is verified by the completeness of the extracted fault features and the accuracy of the degradation trend estimation over the whole life cycle of the bearing degradation data.

Hazardous elements in urban cemeteries and possible architectural design solutions for a more sustainable environment

The general objective of this study is to identify the presence of hazardous elements in the soils of five urban cemeteries in the city of Passo Fundo, in southern Brazil, and to design solutions (architecturally) for future cemeteries to be more sustainable by mitigating toxicological risks to the population residing in the area. A total of 250 soil samples were obtained from points within the cemeteries and in areas surrounding the two oldest cemeteries at a distance of up to 400 m. Twelve architects who design cemeteries primarily focused on sustainability were interviewed, and presented their suggestions for sustainable urban cemetery design. The Building Information Modeling (BIM) computer modeling system was utilized to present a visual representation of suggested architectural features by these architects. The concentration of Pb in the vicinity of cemeteries deserves special attention, as concentrations of this neurotoxin exceed the federal limits set by Brazil. Soil Pb values were found to exceed the limit of 72 mg kg-1 up to a distance of 400 m from the walls of cemeteries A and B, indicating the presence of a danger to human health even at greater distances. This manuscript highlights construction features that enable future burial structures to adequately mitigate the very real problem of contaminants entering the environment from current cemetery design. Two-thirds of the technicians interviewed for this manuscript, each of whom specialize in Brazilian cemetery design, highlighted the importance of revitalizing urban vegetation both when constructing and revitalizing urban vertical cemeteries.

Deep neural networks for the quantile estimation of regional renewable energy production

Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage.

Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN

The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production.

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified.