BP Neural Network Research Articles

Screening and Analysis of Skin Cancer Treatment Using Biocomponents of Plants Using Backpropagation Neural Networks: A Comprehensive Review

: In recent years, the use of natural compounds derived from plants for the treatment of skin cancer has gained significant attention due to their potential therapeutic effects and minimal side effects. This review focuses on the innovative approach of utilizing biocomponents sourced from plants in combination with backpropagation neural networks (BPNN) for the screening and analysis of skin cancer treatments. The integration of plant-derived compounds and AI-driven algorithms holds promise for enhancing the precision and effectiveness of skin cancer therapies. The review begins by highlighting the escalating global burden of skin cancer and the limitations of conventional treatment approaches. With the rise in concerns about the adverse effects of synthetic drugs, researchers have turned their attention towards exploring the therapeutic potential of plant-derived biocomponents. These natural compounds are known for their rich bioactive constituents that exhibit anti-cancer properties, making them suitable candidates for skin cancer treatment. One of the key challenges in harnessing the potential of plant-derived compounds is the need for accurate screening and analysis of their effects. This is where backpropagation neural networks, a type of artificial neural network, comes into play. These networks can process complex data and recognize intricate patterns, enabling them to predict the efficacy of various biocomponents in combating skin cancer. The review delves into the functioning of BPNN and its applications in drug discovery and treatment evaluation. Furthermore, the review explores several case studies that demonstrate the successful integration of plant-derived compounds with BPNN in the context of skin cancer treatment. These studies provide evidence of how this synergistic approach can lead to improved treatment outcomes by minimizing adverse effects and maximizing therapeutic benefits. The methodology section discusses the steps involved in training the neural network using relevant datasets and optimizing its performance for accurate predictions. While the integration of plant-derived compounds and BPNN shows great promise, the review also addresses the existing challenges and limitations. These include the need for comprehensive and standardized datasets, potential biases in training data, and the complexity of neural network architectures. The regulatory considerations surrounding plant-based therapies are also discussed, highlighting the importance of rigorous testing and validation.

Analysis of Canopy Interception Characteristics and Influencing Factors in Typical Artificial Forest in the Loess Plateau Semi-Arid Region

Interception loss (IL) is an important process in the hydrological cycle within semi-arid forest ecosystems, directly affecting the amount of effective rainfall. However, the factors influencing IL during individual rainfall events remain to be quantified. This study collected rainfall, vegetation, and interception data during the 2022 and 2023 growing seasons in a typical black locust forest within the Zhifanggou watershed. It employed the Random Forest Regression (RFR) and back-propagation neural network (BPNN) methods to quantitatively evaluate the contribution rates of various factors to the IL and interception loss percentage (ILP). The IL among the 48 effective rainfall events was 172.05 mm, accounting for 19.54% of the rainfall amount. IL and ILP increased as the distance from the trunk decreased. During all rainfall events, both IL and ILP were significantly negatively correlated with the leaf area index (LAI) and canopy cover (CC); IL is significantly positively correlated with total rainfall (TR) and rainfall intensity (RI), while ILP is significantly negatively correlated with TR, RI, and rainfall duration (RD). The BPNN and RFR results indicated that rainfall, canopy, and tree characteristics contributed 43.06%, 44.79%, and 12.15% to IL, respectively, and 57.27%, 34.09%, and 8.63% to ILP, respectively. TR, CC, and LAI represented the primary influencing factors. Rainfall and canopy characteristics were the main factors affecting IL (ILP). As rainfall event magnitude increases, canopy contributions to IL and ILP decrease. In semi-arid areas, managing forest canopies to control IL helps address water imbalances in ecosystems.

Prediction of moisture content ratio of emulsified asphalt chip seal based on machine learning and electrical parameters

The moisture content ratio (MCR) of the emulsified asphalt chip seal can determine its curing degree. However, the MCR of emulsified asphalt chip seal is difficult to measure on actual projects, and there is a lack of a method to assess its MCR. The objective of this study is to establish a prediction methodology for the MCR of emulsified asphalt chip seal based on machine learning and electrical parameters. Features such as electrical parameters and MCR of emulsified asphalt chip seal at different times were measured experimentally. The importance of the features was evaluated using a Random Forest (RF) model. A Back Propagation Neural Network (BPNN) prediction model was established using the important features. The weights and biases of the BPNN were optimized and initialized using the Improved Particle Swarm Optimization (IMPSO) algorithm. As a result, the RF-IMPSO-BPNN emulsified asphalt chip seal MCR prediction model was developed. This model was compared with five other models. The results show that compared to the RF-PSO-BPNN model, the improved RF-IMPSO-BPNN model can improve the ability of the neural network to find the global optimal solution. Compared to the other four machine learning models, the RF-IMPSO-BPNN model can achieve higher prediction accuracy while reducing the human and material resources of the various devices to collect part of the data. In addition, the emulsified asphalt chip seal cures at low MCR. The model predictions are more accurate at low MCR. Therefore, this study developed the RF-IMPSO-BPNN emulsified asphalt chip seal MCR prediction model, which can use fewer features to achieve higher accuracy and provide a rapid and non-destructive idea for judging its curing.

Effect of surface square textures on the physical field of static and dynamic pressure thrust bearings and multi-objective optimization

Purpose The lubrication performance of static and dynamic pressure thrust bearings is improved by introducing texture on the sealing edge. Design/methodology/approach Through model building, meshing and boundary condition setting, the influence of square texture on oil film lubrication performance was simulated and analyzed, and an improved algorithm was applied to perform optimization of lubrication performance. Findings The findings of this study reveal that the optimum lubrication performance is attained when adjusting the parameters of the square texture to 0.12 mm, 0.1 mm, 1 mm and 34 mm. In such circumstances, the thrust bearing with square textures demonstrates an increase in loading capacity of around 19% and a temperature reduction of about 2ºC compared to a smooth thrust bearing. Originality/value The original Reynolds equation is revised, and the influence of square texture on the physical field of oil film is analyzed, considering the turbulence state and cavitation phenomenon. The multi-objective function under square texture parameters was established using BP neural network, and the improved multi-objective salp swarm algorithm was used to optimize the process parameters.

A Short-term PV Power Prediction and Uncertainty Analysis Model Based on CEEMDAN and AHA-BP

The stochastic and intermittent nature of photovoltaic (PV) generation brings a series of scheduling problems to the power system. An effective prediction of PV power is essential to minimize the impact of uncertainty. Therefore, this paper presents an integrated prediction model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the artificial hummingbird algorithm (AHA), and the BP neural network (BPNN) for predicting power generation from PV power plants, and a methodology for uncertainty analysis by using the nonparametric kernel density estimation (NPKDE). First, one month of PV power is decomposed into an array of components using CEEMDAN. Then, the weights and thresholds of the BPNN are optimized by using AHA. These components are trained using the BPNN. Finally, the final prediction results are obtained by superimposing the components, and NPKDE is employed to compute the probability density and confidence interval of the prediction error. The proposed prediction method demonstrates superior predictive performance in comparison with other models. Also, the NPKDE approach better describes the accuracy of the probability density distribution.

A rate of penetration (ROP) prediction method based on improved dung beetle optimization algorithm and BiLSTM-SA

In the field of oil drilling, accurately predicting the Rate of Penetration (ROP) is crucial for improving drilling efficiency and reducing costs. Traditional prediction methods and existing machine learning approaches often lack accuracy and generalization capabilities, leading to suboptimal results in practical applications. This study proposes an end-to-end ROP prediction model based on BiLSTM-SA-IDBO, which integrates Bidirectional Long Short-Term Memory (BiLSTM), a Self-Attention mechanism (SA), and an Improved Dung Beetle Optimization algorithm (IDBO), incorporating the Bingham physical equation.We enhanced the DBO algorithm by using Sobol sequences for population initialization and integrating the Golden Sine algorithm and dynamic subtraction factors to develop a more robust IDBO. This optimized the BiLSTM-SA model, resulting in a BiLSTM-SA-IDBO model with an RMSE of 0.065, an R² of 0.963, and an MAE of 0.05 on the test set. Compared to the original BiLSTM-SA model, these metrics improved by 78%, 21%, and 83%, respectively. Additionally, we compared this model with BP Neural Network, Random Forest, XGBoost, and LSTM models, and found that our proposed model significantly outperformed these traditional models. Finally, through practical testing, the model’s excellent predictive ability and generalization were verified, demonstrating its great potential for practical applications.

A Model for Fat Content Detection in Walnuts Based on Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIR) is an efficient and accurate method for fat content detection in walnuts. ‘Wen 185’ walnut is grown in large quantities in southern Xinjiang, and its fat content is an important indicator for evaluating the intrinsic quality. The excessive pursuit of yield efficiency, combined with the neglect of quality, agricultural product safety and other factors, has led to the production of poor-quality walnuts. Moreover, research on predicting walnut kernel fat content based on near-infrared spectroscopy technology is rarely reported. Therefore, a technical framework for walnut kernel detection based on near-infrared spectroscopy and the technical standards for ‘Wen 185’ are urgently needed. After first optimizing the initial spectrum data using five preprocessing methods, we established separate prediction models for walnut kernel fat content based on either a back propagation neural network or a support vector regression (SVR) algorithm. The results show that the correction set and validation set coefficients of determination of the walnut kernel fat content prediction model using the back propagation neural network algorithm were 0.86 and 0.89, respectively, with root mean square errors of 1.56 and 1.58, and an RPD value of 2.57; the coefficients of determination for the calibration and validation sets of SVR were 0.90 and 0.83, respectively, with root mean square errors of 1.76 and 1.70, respectively, and an RPD value of 1.70. Thus, near-infrared spectroscopy can be used to establish a foundation for realizing the rapid detection of walnut kernel fat content.

An Improved Neural Network Algorithm for Energy Consumption Forecasting

Accurate and efficient forecasting of energy consumption is a crucial prerequisite for effective energy planning and policymaking. The BP neural network has been widely used in forecasting, machine learning, and various other fields due to its nonlinear fitting ability. In order to improve the prediction accuracy of the BP neural network, this paper introduces the concept of forecast lead time and establishes a mathematical model accordingly. Prior to training the neural network, the input layer data are preprocessed based on the forecast lead time model. The training and forecasting results of the BP neural network when and when not considering forecast lead time are compared and verified. The findings demonstrate that the forecast lead time model can significantly improve the prediction speed and accuracy, proving to be highly applicable for short-term energy consumption forecasting.

Multi-objective optimization of switched reluctance motor driving electric vehicles based on vehicle operating condition

To improve the comprehensive performance of the switched reluctance motor (SRM) for electric vehicles (EVs) under actual driving conditions, a novel geometrical optimization method based on the time-varying phase current characteristics and the comprehensive indicator with justified weight coefficients is proposed in this paper. First, based on the established dynamic models of EV and SRM, the actual operating phase currents of the motor working under the New European Driving Cycle (NEDC) are extracted and classified to develop the representative optimization conditions. Then, considering the requirements of EV, five indicators are defined to evaluate the comprehensive performance of SRM, and a new multi-objective synchronization optimization function is proposed with five weight coefficients obtained by the statistical analysis method for the sample data of the indicators. Furthermore, the geometrical optimization for a four-phase 8/6 SRM is carried out by the combination of Particle Swarm Optimization (PSO) and BP neural network algorithms, after analyzing the effects of key geometrical parameters on the indicators. Finally, the work performance of the optimized SRM is evaluated and compared with that of the initial prototype, and the results show that the optimized SRM with the proposed multi-objective synchronization optimization method can improve the comprehensive indicator by 59.59%, and can significantly enhance the vehicle actual working economy by 50.74% under NEDC cycle.

Agent-based modeling system for real-time characterization of film thickness in digital twin coating

In the automated spraying workshop of ship segments, poor visibility caused by dust and fog hampers the effectiveness of the video monitoring system. The traditional method used during the automated spraying process fails to provide real-time measurement of film thickness and prediction of spraying quality, resulting in uneven film thickness and high rework rates. Robotic spraying technology also faces challenges, such as the time-consuming and labor-intensive process of building and computing a highly reliable Computational Fluid Dynamics (CFD) based spraying film-forming simulation model, which cannot be reflected in real-time during the spraying process. The digital twin characterization method, based on the agent model, offers a solution to these limitations and challenges, providing new ideas and approaches for improving and optimizing the spray film formation model. This paper presents the development of a real-time characterization system for sprayed film thickness based on digital twin technology. The spraying film thickness under different working conditions is obtained using Ansys-Fluent simulation software by varying the gun height and gun speed. The predictive model for spraying film thickness is then established using the obtained simulation data and the corresponding gun height and gun speed, employing the surrogate modeling method (BP neural network). The real-time characterization system for spraying film thickness is developed by combining C# and Unity3D. Finally, in order to ensure the accuracy of the predictive model, a spraying experimental platform is built to verify the accuracy of the predictive model.

Exploring nanoparticle dynamics in binary chemical reactions within magnetized porous media: a computational analysis

Artificial Neural Networks are incredibly efficient at handling complicated and nonlinear mathematical problems, making them very useful for tackling these challenges. Artificial neural networks offer a special computational architecture that is extremely valuable in disciplines like biotechnology, biological computing, and computational fluid dynamics. The present work investigates the applicability of back-propagation artificial neural networks in conjunction with the Levenberg-Marquardt algorithm for evaluating heat transmission in hybrid nanofluids. This work focuses on the computational analysis of a MgO + GO/EG hybrid nanofluid’s steady mixed convection flow over an exponentially stretched sheet, considering multiple slip boundary conditions, thermal conductivity, heat generation, and thermal radiation. A nonlinear system of ordinary differential equations is produced from the basic associated partial differential system by performing the proper exponential similarities modifications. For generating benchmark datasets, the resulting ordinary differential equations are processed employing the bvp4c method. Considering benchmark datasets set aside for training (70%), testing (15%), and validation (15%), the Levenberg-Marquardt algorithm, which employs back-propagation in artificial neural networks, is implemented. The accuracy of the suggested strategy for handling nonlinear problems is verified utilizing mean squared error, error histograms, and regression analysis, which are all used to evaluate the methodology. Outstanding agreement is seen when ANN outputs are compared to numerical results. The flow properties, including temperature, velocity, and concentration profiles, are shown graphically and numerically. For practical purposes, it is therefore essential to analyze the flow and heat transfer in hybrid nanofluids over exponentially extending and shrinking surfaces under mixed convection and heat source scenarios. Hybrid nanofluid problems have a wide range of practical and industrial applications, such as medication delivery, manufacturing, microelectronics, nuclear plant cooling, and marine engineering.

Spectral Variations of Reclamation Vegetation in Rare Earth Mining Areas Using Continuous–Discrete Wavelets and Their Impact on Chlorophyll Estimation

Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, mining activities have led to extensive deforestation of the original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges in surviving under environmental stresses, including heavy metal pollution, ammonia nitrogen contamination, and soil drought. To rapidly and accurately monitor the growth of reclamation vegetation, this study investigates the spectral variations and their impact on the accuracy of chlorophyll estimation, utilizing hyperspectral data and relative chlorophyll content (SPAD). Specifically, continuous–discrete wavelet transforms were applied, along with the original spectra and first derivative spectra, to enhance spectral anomalies in the reclamation vegetation and identify chlorophyll-sensitive spectral features. Additionally, multiple linear stepwise regression and backpropagation neural network models were employed to estimate chlorophyll content. The results revealed the following: (1) the d5 and d6 scales of the discrete wavelet effectively highlighted spectral anomalies in the reclamation vegetation; (2) Salix japonica (Salix fragilis L.), among typical reclamation species, exhibited poor adaptability to the environmental conditions of the rare earth mining area; (3) the backpropagation neural network model demonstrated superior performance in chlorophyll estimation, with the spectral features Fir, Fir_d4, Fir_d5, and Fir_d6 significantly enhancing the accuracy of the model, achieving an R2 of 0.93 for Photinia glabra (Photinia glabra (Thunb.) Maxim.). The application of continuous–discrete wavelet transforms to hyperspectral data significantly improves the precision of chlorophyll estimation, underscoring the potential of this method for the rapid monitoring of reclamation vegetation growth.

Trend Prediction of Vibration Signals for Pumped-Storage Units Based on BA-VMD and LSTM

Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately.

Beam Orbital Parameter Prediction Based on the Deployment of Cascaded Neural Networks at Edge Intelligence Acceleration Nodes

During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded backpropagation neural network (CBPNN) that is informed by the physical structure. This system serves as an environmental map for reinforcement learning, aiding beam current correction. The CBPNN was implemented on the acceleration node to hasten the forward inference process, leveraging sparsification, quantization algorithms, and pipelining techniques. Our experimental results demonstrated that the simulated inference speed reached 28 μs with FPGA hardware as the edge acceleration node, achieving forward inference speeds 35.66 and 12.66 times faster than those of the CPU and GPU. The energy efficiency ratio was 10.582 MOPS/W, which was 989 and 410 times that of the CPU and GPU, respectively. This confirms the designed architecture’s energy efficiency and low latency attributes.

Predicting ignitability classification of thermally thick solids using hybrid GA-BPNN and PSO-BPNN algorithms

Two hybrid binary classification models combining the merits of GA (genetic algorithm) and PSO (particle swarm optimization) with BPNN (backpropagation neural network) are proposed to estimate the ignitability of thermally thick solids. A 1D numerical model verified by analytical correlation and experimental measurements is employed to yield training, validation and testing datasets. Surface absorptivity (ε), density (ρ), specific heat (Cp), thermal conductivity (k), critical temperature (Tcri), and incident heat flux (HF) which are highly related to solid ignition are selected as inputs, and ignitability serves as output. A one-hidden-layer BPNN is first constructed, and then GA and PSO are encoded to form GA-BPNN and PSO-BPNN where connection weights and biases are optimized by GA/PSO and the predicted error serves as objective function of GA/PSO. Results show that the optimal neuron number in hidden layer is 34. Compared to BPNN, GA-BPNN and PSO-BPNN feature higher accuracy but more training time. Accuracy and convergence efficiency of PSO-BPNN are higher than GA-BPNN, nevertheless GA-BPNN exhibits better robustness. Predictive accuracies of BPNN, GA-BPNN, and PSO-BPNN on testing dataset are 99.05%, 99.55%, and 99.55%, respectively. ROC (receiver operating characteristic) curves are plotted to rank the performance of the three models, revealing 0.5 is an appropriate threshold to classify ignitability. Feature importance of the models is analyzed using PI (permutation importance) and MIV (mean impact value) methods. It is found HF and Tcri are the most important two inputs followed by ε, while ρ, Cp, and k show least and approximately identical impact. Reliability of the three models is verified by predicting critical heat flux of ignition of three polymers, and good agreement with literature is observed.

Spatial downscaling of SMAP soil moisture to high resolution using machine learning over China’s Loess Plateau

Soil moisture (SM) is a critical physical parameter in land surface processes that affects the atmospheric and hydrological cycles. Soil Moisture Active Passive (SMAP) mission produces high-quality global SM data, but its low spatial resolution limits the applications at a regional or local scale. In this study, we developed a novel method to select optimal factors from 34 candidate downscaling factors for each month using three machine learning methods (Back Propagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF)) to produce monthly time series SM products at a spatial resolution of 1 km for the entire Loess Plateau in China from 2015 to 2023. 11 in-situ SM measurements distributed in Loess Plateau and precipitation data were used to evaluate the downscaling performances of the three machine learning approaches. The results show that the spatial trends of the three downscaled SM were essentially the same as the SMAP SM, with similar spatial patterns, and all three downscaled SM can provide more spatial information and texture features than SMAP SM. Among the three types of downscaled SM, the RF downscaled SM reached the highest R (0.654) and the smallest unbiased Root Mean Square Error (ubRMSE) (0.044 m3m−3), better than the BPNN and SVM downscaled SM, and most closely matched the in-situ measurements. The dynamics of SM were successfully captured by RF downscaled SM, which exhibited strong temporal consistency with the in-situ SM and responded well to precipitation events, with significant increases in SM values in the month of high precipitation and subsequent months. In conclusion, the RF model has the best downscaling effect and it can be used to provide high spatial resolution SM data for applications at a regional or local scale across the Loess Plateau.

Multi-objective optimization of automotive seat frames using machine learning

The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a hybrid machine learning prediction model that combines the improved gray wolf optimizer (IGWO) and back propagation neural network (BPNN) has been proposed, and the prediction effect of the model was validated using the seat simulation data. Initially, based on the experimental data, finite element models were developed for eight typical working conditions of automobile seats and their accuracy was validated. Subsequently, the energy absorption to mass ratio method was employed to screen the design variables, resulting in the selection of 17 thickness variables and 15 material variables. Thereafter, the gray wolf optimizer (GWO) algorithm underwent enhancement through the incorporation of the dynamic leadership hierarchy (DLH) mechanism and the revision of the positional formula, yielding the IGWO algorithm. Following this, the IGWO algorithm was applied to optimize the hyperparameters of BPNN, culminating in the establishment of the IGWO-BPNN model. Ultimately, the seat multi-objective optimization design process was addressed using multi-objective gray wolf optimizer (MOGWO) to achieve the Pareto frontier, while the decision-making was conducted using the combined compromise solution (CoCoSo) method to determine the best trade-off solution. Furthermore, the effectiveness of the proposed optimal design method is evidenced by comparing the baseline design, simulation analysis, and optimal design methods. The results indicate that the optimized automotive seat frame achieves a reduction in cost by 20.7 % and mass by 22.9 %, simultaneously maintaining safety performance. Consequently, the proposed optimization design methodology is demonstrated to be highly effective for the multi-objective optimization design of automotive seat frames.

Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on Heuristic-Algorithm-Optimized Backpropagation Neural Network Model

A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of TCFDST members due to the variations in cross-section, this paper applied heuristic optimization algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO) to enhance a Backpropagation Neural Network (BPNN) model. A predictive model incorporating both global and local optimization strategies for the axial compressive capacity of a TCFDST structure is proposed. A comprehensive axial database for TCFDST members, comprising 1327 sets of experimental and finite element analysis results, was established, with ten types of component dimensions and material parameters selected as input variables and compressive bearing capacity as the output variable. This study developed and assessed four BPNN models, each optimized by a different heuristic algorithm, against various machine learning algorithms and standards. The heuristic-algorithm-optimized BPNN models demonstrated superior accuracy in predicting the axial compressive capacity of TCFDST members. Through parametric analysis, this study identified the relationship between the model’s bearing capacity predictions and each input parameter, confirming the model’s broad applicability. The optimized BPNN model, refined with heuristic algorithms, provides a significant reference for addressing the computational challenges associated with the load-bearing capacity of TCFDST structures and facilitating their application.

Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining

As the basic guarantee for people’s production and life, the safe operation of the power system has an important impact on the development and operation of society. To ensure the safe and stable operation of the power grid, predicting potential faults and taking reasonable preventive measures can effectively avoid the occurrence of power accidents. However, due to the difficulty in ensuring the prediction accuracy of traditional methods, there are issues of protection misoperation and rejection. Therefore, in order to achieve accurate prediction of power grid faults and avoid protection misoperation and rejection issues, a distribution network fault classification prediction model using a combination of three-layer data mining model (TLDM) and adaptive moment estimation (Adam) algorithm/random gradient descent algorithm improved backpropagation neural network (BPNN) is proposed. The implementation results showed that the classification accuracy of artificial fish school [Formula: see text] priori, [Formula: see text]-means clustering convolutional neural network model and TLDM for single-phase grounding faults was 93.2%, 91.5% and 96.6%, respectively. The classification accuracy for two-phase faults was 92.8%, 92.4% and 95.7%, respectively. The classification accuracy for two-phase grounding faults was 93.7%, 91.2% and 96.9%, respectively. The classification accuracy for three-phase faults was 93.3%, 92.1% and 97.1%, respectively. The TLDM had the highest classification accuracy. The average accuracy, average accuracy and average recall of the BPNN improved by the combination of the ADAM algorithm and random gradient descent algorithm were 94.1%, 90.9% and 88%, respectively, which were higher than the BPNN improved by the combination of ADAM algorithm and random gradient descent algorithm. The above results indicate that the proposed distribution network fault classification and prediction model has good performance and can achieve accurate prediction of distribution network faults.

Visualization Analysis of Integrating College Sports Training and Psychology into Basketball Physical Education Teaching System Based on Image Recognition Algorithm

ABSTRACT On this basis, a basketball movement model based on ORB (Oriented FAST and Rotated BRIEF) local feature extraction has been proposed, and a basketball teaching visualization analysis system has been constructed. By extracting images from athletes’ videos in basketball, and using their training as a standard, tracking the frequency of their various celebration postures appearing and improving their scores in the game, corresponding feature points are extracted, and then the BP (back propagation) neural network algorithm is used to classify feature points. Ten different participants were selected from 10 games. Their movements were tracked, and then the extracted feature actions were imported into the visualization system for visual analysis, comparing the changes before and after psychological intervention. The results showed that there were significant differences in scores, errors, hit rates, steals, and motivation among subjects who received psychological cues and incentives compared to the data before psychological counseling. The scores and positive and negative values increased to 2–4 points, which had a certain positive impact on the competitive results.