Search Results (128)

Accurate prediction of Air Quality Index (AQI) concentrations remains a critical challenge in environmental monitoring and public health management due to the complex nonlinear relationships among multiple atmospheric factors. To address this challenge, we propose a novel prediction model that integrates an adaptive-weight particle swarm optimization (AWPSO) algorithm with a back propagation neural network (BPNN). First, the random forest (RF) algorithm is used to scree the influencing factors of AQI concentration. Second, the inertia weights and learning factors of the standard PSO are improved to ensure the global search ability exhibited by the algorithm in the early stage and the ability to rapidly obtain the optimal solution in the later stage; we also introduce an adaptive variation algorithm in the particle search process to prevent the particles from being caught in local optima. Finally, the BPNN is optimized using the AWPSO algorithm, and the final values of the optimized particle iterations serve as the connection weights and thresholds of the BPNN. The experimental results show that the RFAWPSO-BP model reduces the root mean square error and mean absolute error by 9.17 μg/m³, 5.7 μg/m³, 2.66 μg/m³; and 9.12 μg/m³, 5.7 μg/m³, 2.68 μg/m³ compared with the BP, PSO-BP, and AWPSO-BP models, respectively; furthermore, the goodness of fit of the proposed model was 14.8%, 6.1%, and 2.3% higher than that of the aforementioned models, respectively, demonstrating good prediction accuracy. Full article

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article

In the field of ultra-wideband (UWB) indoor localization, traditional backpropagation neural networks (BPNNs) are limited by their susceptibility to local minima, which restricts their ability to achieve global optimization. To overcome this challenge, this paper proposes a novel hybrid algorithm, termed ARO-BP, which integrates the Artificial Rabbit Optimization (ARO) algorithm with a BPNN. The ARO algorithm optimizes the initial weights and thresholds of the BPNN, enabling the model to escape local optima and converge to a global solution. Experiments were conducted in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments using a four-base-station configuration. The results demonstrate that the ARO-BP algorithm significantly outperforms traditional BPNNs. In LOS conditions, the ARO-BP model achieves a localization error of 6.29 cm, representing a 49.48% reduction compared to the 12.45 cm error of the standard BPNN. In NLOS scenarios, the error is further reduced to 9.86 cm (a 46.96% improvement over the 18.59 cm error of the baseline model). Additionally, in dynamic motion scenarios, the trajectory predicted by ARO-BP closely aligns with the ground truth, demonstrating superior stability. These findings validate the robustness and precision of the proposed algorithm, highlighting its potential for real-world applications in complex indoor environments. Full article

As the distribution of indoor contaminants is often heterogeneous, the traditional Wells–Riley equation is inadequate for accurately assessing the infection risk to indoor personnel. In this study, contaminant concentration data from hospital wards were obtained through experimentally validated computational fluid dynamics (CFD) simulations. Four common machine learning models—multiple linear regression (MLR), support vector regression (SVR), backpropagation (BP) neural network, and convolutional neural network (CNN)—were employed to predict the distribution of contaminants within the wards. The results demonstrate that the CNN achieves the best predictive performance, followed by the BP neural network. Specifically, the CNN exhibited a root mean square error, mean absolute error, and mean absolute percentage error of 6.31 ppm, 3.18 ppm, and 8.33%, respectively. Comparative analysis revealed that the CNN reduced the mean absolute percentage error (MAPE) by 58.18%, 33.47%, and 25.15% compared to MLR, SVR, and BPNN, respectively. Full article

Springback control is a critical factor in profile stretch-bending-torsion forming. A new stretch-bending-torsion automatic forming method based on the mixture of finite element and BP neural network (FE-BPNN) is proposed. The method enhances the shape accuracy of profiles after single-step forming. Initially, the study introduces the 3D multi-point stretch-bending and torsion (3D MPSBT) forming machine and its forming principles. Subsequently, it details the springback prediction method and automatic forming control approach based on BPNN. A springback control model is established through numerical simulation and experiments. The proposed springback control method is compared with a springback factor-based approach from other researchers using hollow rectangular profiles undergoing combined bending and torsion deformation as the research object. The results validate the effectiveness and advantages of the proposed method. Full article

As the core process of the thermal treatment of municipal solid waste (MSW), incineration process optimization has become a frontier topic in the field of environmental engineering. This study took a 500 t/d incinerator for engineering application as the research object. Based on a two-fluid model, a three-dimensional transient model of a proportional incinerator was established. The effects of primary air proportion and moisture content on the combustion state in the incinerator were verified and discussed using field test data, and the dynamic changes in flue gas temperature were predicted by a BPNN (Backpropagation Neural Network). The results show that the increase in air volume in the drying section promotes water evaporation but inhibits the devolatilization and combustion of fixed carbon. The position where complete devolatilization and fixed carbon combustion begins was delayed by 1.5 m~3 m. The moisture content (M) is negatively correlated with the devolatilization and combustion of fixed carbon. From M = 25% to M = 40%, the flue gas outlet temperature decreased by 140 K. In addition, a dynamic combustion BP neural network model with the movement of the grate under rated conditions was constructed, with the MSE (Mean Squared Error) being 1.629%. The model can learn data characteristics well and has a good prediction effect. This study provides a scientific basis for optimizing the operating parameters of municipal solid waste incinerators, helps to optimize the incineration process, and is of great significance to the thermal treatment of MSW. Full article

To predict the forming behaviour of aluminium alloy variable diameter tubes during hydroforming, a genetic algorithm-enhanced particle swarm optimisation (GA-PSO) is used to optimise a backpropagation neural network (BP-NN). A fast prediction model based on the GA-PSO-BP neural network for the hydroforming of aluminium alloy variable diameter tubes was established. The loading paths (internal pressure, axial feeds, and coefficient of friction) were randomly sampled using the Latin hypercube random sampling method. The minimum wall thickness, maximum wall thickness, and maximum expansion height of the formed tubes are included in the main evaluation factors of the forming results. A variety of machine learning algorithms are used to predict, and the prediction results are compared with the finite element model in terms of error. The maximum average absolute value error and mean square error of the proposed model are less than 0.2, which improves the accuracy by 20.4% compared to the unoptimised PSO-BP neural network algorithm. The maximum error between simulated and predicted results is within 4%. The model allows effective prediction of the hydroforming effect of aluminium alloy variable diameter tubes and improves the computational rate and model accuracy of the model. The same process parameters are experimentally verified, the minimum wall thickness of the formed part is 1.27 mm, the maximum wall thickness is 1.53 mm, and the maximum expansion height is 5.11 mm. The maximum thinning and the maximum thickening rate comply with the standard of hydroforming, and the tube has good formability without obvious defects. Full article

Predicting the river discharge is essential for preparing effective measures against flood hazards or managing hydrological droughts. Despite mathematical modeling advancements, most algorithms have failed to capture the extreme values (especially the highest ones). In this article, we proposed a quantum neural networks (QNNs) approach for forecasting the river discharge in three scenarios. The algorithm was applied to the raw data series and the series without aberrant values. Comparisons with the results obtained on the same series by other neural networks (LSTM, BPNN, ELM, CNN-LSTM, SSA-BP, and PSO-ELM) emphasized the best performance of the present approach. The lower error between the recorded values and the predicted ones in the evaluation of maxima compared to the case of the competitors mentioned shows that the algorithm best fits the extremes. The most significant mean standard errors (MSEs) and mean absolute errors (MAEs) were 26.9424 and 4.8914, respectively, and the lowest R² was 84.36%, indicating the good performances of the algorithm. Full article

Accurately monitoring total nitrogen (TN) content in field soils is crucial for precise fertilization management. TN content is one of the core indicators in soil fertility evaluation systems. Rapid and accurate determination of TN in the tillage layer is essential for agricultural production. Although UAV-based multispectral remote sensing technology has shown potential in agricultural monitoring, research on its quantitative assessment of soil TN content remains limited. This study utilized UAV (unmanned aerial vehicle) multispectral imagery and field-measured TN data from four key growth stages of silage corn in 2022 at Huari Ranch, Minle County, Hexi region. The support vector machine–recursive feature elimination (SVM-RFE) algorithm was applied to select vegetation indices as model inputs. A total of 18 models based on machine learning algorithms, including BP neural networks (BPNNs), random forest (RF), and partial least squares regression (PLSR), were constructed to compare the most suitable inversion model for TN in the rhizosphere soil (0–30 cm) of silage corn at different growth stages. The optimal period for TN inversion was determined. The SVM-RFE algorithm outperformed the models built without feature selection in terms of accuracy. Among the nitrogen inversion models based on different machine learning algorithms, the PLSR model showed the best performance, followed by the RF model, while the BPNN model performed the worst. The PLSR model established for the mature growth stage at soil depths demonstrated the highest inversion accuracy, with R and RMSE values of 0.663 and 0.281, respectively. The next best period was the tasseling stage, while the worst inversion accuracy was observed during the seedling stage, indicating that the mature stage is the optimal period for TN inversion in the study area. Full article

For a laser diode (LD) with high output power, it is difficult to precisely and quickly control its temperature because of the large thermal power involved. In this paper, a machine learning-based temperature controller for high-power LDs is reported. It is implemented by developing a back-propagation neural network (BPNN) with an adaptive dynamic adjustment strategy (ADAS) temperature controller which integrates a constant-current-source circuit into the conventional proportional-integral-derivative (PID) temperature-controlling circuit. Compared to the conventional PID controller, the speed of temperature control had been shortened from 1300 s to 350 s, the long-term temperature fluctuation was decreased from ±0.148% to ±0.082%, and the step response time could be decreased from 960 s to 210 s. Full article

With the gradual depletion of global oil and gas resources, accurate prediction of anomaly formation pressure caused by pressure relief from other sources has become increasingly crucial in oil and gas exploration and development. The anomaly formation pressure caused by pressure relief affects the well’s stability and significantly impacts the safety and economy of drilling operations. However, traditional methods for predicting anomaly formation pressure, such as Bowers’ method, may not accurately identify the complex relationship between parameters and pore pressure. In contrast, the BP neural network (BPNN) can learn the complex relationship between input and output from data, which has a significant advantage in accurately identifying anomaly formation pressures caused by pressure relief from other sources. This study proposes a neural network-based method for accurately predicting anomaly formation pressure caused by pressure relief from other sources. The high quality of input data is ensured through meticulous preprocessing related to anomaly formation pressure caused by pressure relief from other sources, including data cleaning, standardization, and correlation analysis. Subsequently, model training was conducted to fully utilize its powerful nonlinear fitting ability and capture the complex changes in formation pressure caused by anomaly pressure relief from other sources. This method collects and organizes the parameters of the formation, including Gamma-ray (Gr), Delta-T (Dt), wave velocity (V_p), and Resistivity (R10), to train a BPNN model for predicting pressure relief type anomaly formations. The trained model has a Bayesian regularized backpropagation function, and the average absolute percentage error (AAPE) and correlation coefficient (R) of predicting pore pressure in well A are 4.22% and 0.875, respectively. To verify the proposed model’s effectiveness, it was applied to a blind dataset of adjacent B wells and successfully predicted pore pressure with AAPE of 5.44% and R of 0.864. We compare and analyze the formation pore pressure predicted by the traditional Bowers model and support vector machine (SVM) model. The prediction results of the BPNN model have more minor errors and are closer to the actual pressure coefficient. This study demonstrates the accuracy of the proposed model in predicting pressure relief type anomaly formation pressure using drilling data. Full article

In the Gobi region, concrete structures frequently suffer erosion from wind gravel flow. This erosion notably impairs their longevity. Therefore, creating a predictive model for wind gravel flow-related concrete damage is crucial to proactively address and manage this problem. Traditional theoretical models often fail to predict the erosion rate of concrete (CER) structures accurately. This issue arises from oversimplified assumptions and the failure to account for environmental variations and complex nonlinear relationships between parameters. Consequently, a single traditional model is inadequate for predicting the CER under wind gravel flow conditions in this region. To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. The support vector machine (SVM) model demonstrates superior predictive performance, evidenced by its R² value nearing one and a notable reduction in RMSE 1.123 and 1.573 less than the long short-term memory network (LSTM) and BP neural network (BPNN) models, respectively. Ensuring that the training set comprises at least 80% of the total data volume is crucial for the SVM model’s prediction accuracy. Moreover, erosion time is identified as the most significant factor affecting the CER. An enhanced theoretical erosion model, derived from the Bitter and Oka framework and integrating concrete strength and erosion parameters, was formulated. It showed average relative errors of 22% and 31.6% for the Bitter and Oka models, respectively. The SVM model, however, recorded a minimal average relative error of just −0.5%, markedly surpassing these improved theoretical models in terms of prediction accuracy. Theoretical models often rely on simplifying assumptions, such as linear relationships and homogeneous material properties. In practice, however, factors like concrete materials, wind gravel flow, and climate change are nonlinear and non-homogeneous. This significantly limits the applicability of these models in real-world environments. Ultimately, the SVM algorithm is highly effective in developing a reliable prediction model for CER. This model is crucial for safeguarding concrete structures in wind gravel flow environments. Full article

Small-batch workpieces in smart manufacturing demand process parameter modeling, but existing models lack analysis across varying sample sizes and runtime conditions. This study proposes a novel surface-roughness prediction method, Response Surface Methodology-BP Neural Network (RSM-BPNN), designed for experimental data from single small-batch workpieces with varying sample sizes. First, polynomial feature transformation and selection are performed based on the proposed process parameters to improve the feature quality of input data. Second, a Dynamic Central Composite Design-Response Surface Methodology (DCCD-RSM) determines the optimal experimental region and fits surface roughness, while a BPNN trains a deep learning model for prediction. The BPNN fusion method combines both approaches to create a general, adaptive predictive model for surface roughness. Finally, the accuracy and practicality of the BPNN model were verified through reverse calculation and parameter optimization in actual robot grinding experiments. The model demonstrated good predictive performance for surface roughness in aluminum alloy grinding, providing reliable guidance for surface quality prediction and process parameter optimization in small-batch workpieces within the context of smart manufacturing. Full article

In this study, an accurate suspension force modeling method for the magnetic bearings of flywheel batteries considering degree-of-freedom (DOF) interactions and their control system is proposed to solve the problem that the traditional flywheel battery suspension force model does not consider DOF interactions, which makes the control system control effect poor. Firstly, according to the structural characteristics of the flywheel battery used, a suspension force model is established for the radial and axial magnetic bearings, which are most seriously interfered with by the torsional degrees of freedom of the flywheel battery. Next, by proposing DOF interaction factors, the complex changes due to DOF interactions are cleverly summarized into several interaction factors applied to the fundamental model to achieve accurate suspension force modeling considering DOF interactions. To better adapt the established accurate model and ensure precise control of the flywheel battery system under various working conditions, the firefly algorithm is employed to optimize the BP neural network (FA-BPNN). This optimization regulates the control system’s parameters, enabling the achievement of optimal control parameters in different scenarios and enhancing control efficiency. Compared to the flywheel battery controlled using the fundamental model, the radial and axial displacements are reduced by more than 30 percent and 20 percent, respectively, in the uphill condition using the accurate model with FA-BPNN. Full article

Mine gas emission is one of the main causes of gas disasters. In order to achieve the accurate prediction of gas emission, a gas emission prediction model based on the random forest (RF) method was proposed in combination with the analysis of its influencing factors. The prediction results were compared with the support vector regression (SVR) and BP neural network (BPNN) methods, and then they were verified and analyzed through the Dongqu coal mine. The results show that the gas emission prediction model based on random forest has strong generalization and robustness, and RF has a wide range of parameter adaptation during the modeling process. When the number of trees (n_tree) exceeds 100, its training error tends to stabilize, and changes in n_tree have no substantial impact on the prediction performance. The SVR prediction model has significant bias in both the training and testing stages. Meanwhile, the BPNN model has excellent prediction results in the training phase, but there is a large error in the testing stage, which indicates that there is an “overfitting” phenomenon in the training stage, resulting in weak generalization. The evaluation of variable importance shows that the extraction rate, coal seam depth, daily production, gas content in adjacent layers, and coal seam thickness have a significant impact on gas emission. Meanwhile, through application analysis, it is further demonstrated that the random forest method has high accuracy, strong stability, and universality, and it can achieve good predictive performance without the need for complex parameter settings and optimization, making it is very suitable for predicting gas emission. Full article