Traditional BP Research Articles

Identification Method for XRF Spectral Analysis Based on an AGA-BP-Attention Neural Network

X-ray fluorescence (XRF) spectroscopy is a non-destructive differential measurement technique widely utilized in elemental analysis. However, due to its inherent high non-linearity and noise issues, it is challenging for XRF spectral analysis to achieve high levels of accuracy. In response to these challenges, this paper proposes a method for XRF spectral analysis that integrates an adaptive genetic algorithm with a backpropagation neural network, enhanced by an attention mechanism, termed as the AGA-BP-Attention method. By leveraging the robust feature extraction capabilities of the neural network and the ability of the attention mechanism to focus on significant features, spectral features are extracted for elemental identification. The adaptive genetic algorithm is subsequently employed to optimize the parameters of the BP neural network, such as weights and thresholds, which enhances the model’s accuracy and stability. The experimental results demonstrate that, compared to traditional BP neural networks, the AGA-BP-Attention network can more effectively address the non-linearity and noise issues of XRF spectral signals. In XRF spectral analysis of air pollutant samples, it achieved superior prediction accuracy, effectively suppressing the impact of background noise on spectral element recognition.

A Study on the Impact of Corporate Financial Accounting Management System on Corporate Innovation Under Sustainable Development Strategy

In the complex and changeable macro-economy, the sustainable development of enterprises is closely related to their ability to resist risks. To analyze the influencing factors of listed enterprises’ anti risk ability, this study constructs an indicator system of influencing variables through principal component analysis. The financial risk early warning method based on improved RBF neural network is constructed. Afterwards, the relevance of the indicator system was confirmed by the Kaiser-Meyer–Olkin (KMO) test. And in simulation experiments, it compared the classification performance of the BP neural network and the improved RBF model combined with the clustering algorithm. In the training dataset, the classification accuracy of the BP neural network was 79.6% while that of the improved RBF model was as high as 93.6%. In the 30 test datasets, the BP neural network appeared to have seven false positives. In the 30 test datasets, the BP neural network produced seven false judgments, while the proposed method only had three judgment errors. Several experiments showed that the BGD-RBF financial risk early warning model effectively maintained a high performance in the classification accuracy of enterprise financial status. It is more reliable than the traditional BP neural network method.

A calibration method for infrared measurements on building facades based on a WOA-BP neural network

As a critical method for analyzing the thermal properties of buildings, the audit results of infrared thermography are affected by the accuracy of temperature measurement. However, it is still a challenge to determine the actual temperature of the target surface due to various factors (e.g., object emissivity, ambient temperature, relative humidity, measurement distance, etc.). To this end, this paper proposes a robust temperature calibration model by utilizing a BP neural network trained with numerous sets of laboratory temperature measuring data, using the Whale Optimization Algorithm (WOA) to optimize the initial network parameters, and rigorously validating the model with field measurements. The comparison of WOA-BP with traditional BP, SVM, RBF, PSO-BP and GWO-BP confirms its superiority in terms of correction error and robustness, which can effectively solve the problems of temperature measurement deviations in building facades.

Cultural Entrepreneurship and Rural Revitalization: Mapping China’s Development Strategy

Cultural entrepreneurship is the cornerstone of sustainable development in rural China, especially within the realm of cultural industries. This study aims to investigate the pivotal role of cultural industries in China’s rural revitalization and the influence of entrepreneurship on sustainable enterprise development. Using a quantitative approach, the study gathered data from 171 entrepreneurs in China, employing SPSS for analysis. In the first week of March 2023, the survey was carried out throughout China. The research focuses on exploring the impact of rural revitalization strategies on fostering entrepreneurship within cultural industries. Experimenting with an enhanced BP-L-M algorithm, the finding reveals its high accuracy when compared to traditional BP neural networks. The novelty of this study provides valuable insights into the unique challenges and an opportunity faced by rural entrepreneurs in the cultural sector and provides practical recommendations for implementing rural revitalization for entrepreneurship in cultural industries. By emphasizing the significance of entrepreneurship in rural areas and its potential to alleviate poverty and boost economic growth through cultural industries, this research underscores the transformative power of cultural initiatives in shaping the nation’s economic landscape.

Student sports data analysis and physical fitness evaluation based on convolutional neural networks

<p>In order to achieve the analysis of student sports data and physical fitness evaluation, the author proposes a method based on convolutional neural networks. A hybrid algorithm combining genetic algorithm and error backpropagation algorithm (BP) is used to train convolutional neural networks. The algorithm first uses genetic algorithm for global training, and then uses BP algorithm for local precise training. This overcomes the drawbacks of traditional BP networks such as long training time and frequent local atmospheric drift, and improves global circulation performance. A neural network model was established to display the relationship between the total physical activity score and multiple test scores of high school students by utilizing electrical networks to demonstrate the connectivity of the neural network. This model aims to evaluate the athletic performance of college students and compare the results with other experimental models. The results indicate that the neural network-based model for evaluating college student physical activity can reflect the differences in physical activity and scores among all students, making it a suitable standard for evaluating high school student physical activity. The fitting accuracy of deterministic neural network models is higher than that of multiple linear regression models, which means that neural network models better reflect the performance of the network. The accuracy of various indicators of student physical fitness and total score makes the model easy to operate, accurate to predict, and effective analysis is scientifically reasonable.</p>

Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance

Abstract Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.

Analysis of new energy vehicle battery temperature prediction by combining BP neural network

Abstract With the rapid development of the new energy industry, the safety research of battery technology has become a key topic. This paper focuses on the temperature prediction of new energy vehicle batteries, aiming to improve the safety and efficiency of batteries. Based on the new energy vehicle battery management system, the article constructs a new battery temperature prediction model, SOA-BP neural network, using BP neural network optimized by SOA algorithm. This model can accurately predict the battery temperature, and the effectiveness of its temperature control is verified through experiments. The results show that the SOA-BP neural network model outperforms the traditional BP, CNN, and RNN models in temperature prediction. Regarding evaluation indexes, the model’s root mean square error (RMSE), mean absolute error (MAE), and R2_Score are 0.953, 0.909, and 0.837, respectively. It is worth noting that the model can effectively regulate and control the battery temperatures at different temperatures, ensuring that the maximum temperature difference of each battery is maintained within 5°C. The model can also be used to predict the temperature of the batteries in different temperatures. This battery temperature prediction model not only provides an effective means for predicting and controlling the battery temperature of new energy vehicles, but also provides an essential reference for improving the vehicle’s performance and developing energy management strategies. This study offers a new solution for the safety and efficiency of new energy vehicle batteries.

Research on the Innovation of Talent Cultivation Mode and Industry-Education Integration Mechanism of College Education in the Internet Era

Abstract In this paper, in order to obtain a better classification evaluation effect, a feedback connection model is added to the convolutional neural network to establish the evaluation model of the integration of industry and education in colleges and universities based on FCNN. Compare the MSE loss values of the traditional BP neural network model and the FCNN model. Indicator system construction, with the help of the accuracy of the convolutional neural network, to carry out the whole process of evaluation around the indicators, weights, and the quality of the implementation results. The data of students’ micro-expression concentration recognition test is used as the evaluation data of students’ project participation, comparing the recognition rate of the participation evaluation system proposed in this paper and the traditional participation evaluation system to complete the quality evaluation of the talent cultivation model of college education. Analyze the data on the graduation rates of college graduates to determine the effectiveness of the university’s integration of college education. The analysis shows that in 2022, the professional matching employment rate of graduates was 86.28%, which reflects the high efficiency of the university’s industry-teaching integration on the cultivation of professional and applied talents, and the mechanism of industry-teaching integration is well affiliated.

Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images

Ecological forests are an important part of terrestrial ecosystems, are an important carbon sink and play a pivotal role in the global carbon cycle. At present, the comprehensive utilization of optical and radar data has broad application prospects in forest parameter extraction and biomass estimation. In this study, tree and topographic data of 354 plots in key nature reserves of Liaoning Province were used for biomass analysis. Remote sensing parameters were extracted from Landsat 8 OLI and Sentinel-1A radar data. Based on the strong correlation factors obtained via Pearson correlation analysis, a linear model, BP neural network model and PSO neural network model were used to simulate the biomass of the study area. The advantages of the three models were compared and analyzed, and the optimal model was selected to invert the biomass of Liaoning province. The results showed that 44 factors were correlated with forest biomass (p < 0.05), and 21 factors were significantly correlated with forest biomass (p < 0.01). The comparison between the prediction results of the three models and the real results shows that the PSO-improved neural network simulation results are the best, and the coefficient of determination is 0.7657. Through analysis, it is found that there is a nonlinear relationship between actual biomass and remote sensing data. Particle swarm optimization (PSO) can effectively solve the problem of low accuracy in traditional BP neural network models while maintaining a good training speed. The improved particle swarm model has good accuracy and speed and has broad application prospects in forest biomass inversion.

Association of cumulative systolic blood pressure load and stroke in patients without diabetes

Abstract Objectives The purpose of this study was to assess the association between cumulative systolic blood pressure (SBP) load and risk of stroke in patients without previous stroke and diabetes. Methods The present study was a post hoc analysis of patients enrolled in the SPRINT study. Cumulative SBP load was defined as the area under curve for SBP values >120 or 130 or 140mmHg respectively (named SBP load120, SBP load130 and SBP load 140 respectively) divided by the area under curve for all measured SBP values over a 48-month exposure period. Hazard ratios for the association between cumulative SBP load with stroke were estimated using Cox models. Results Only SBP load130, but not mean SBP, SBP standard deviation (SBP SD), time at therapeutic range (TTR); had a well-correlation with the incidence of stroke according to the restricted cubic spline curve (RCS). Grouping participants with tertiles of cumulative SBP load130 distinguished the incidence of stroke by K-M curve. Different Cox models indicated SBP load130 was an independent risk factor for stroke. For the prediction of stroke, only cumulative SBP load130，but not SBP load120 and SBP load140, outperformed mean SBP, TTR, SBP SD in terms of C-statistics and net reclassification indexes. Conclusions Cumulative SBP load may provide better prediction of stroke compared with traditional BP measures among patients without previous stroke and diabetes. 130mmHg could be the best cutoff value for predicting incidence of stroke by SBP load.abstract figure

Data-driven prediction of the equivalent sand-grain roughness

Surface roughness affects the near-wall fluid velocity profile and surface drag, and is commonly quantified by the equivalent sand-grain roughness {k}_{s}. It is essential to estimate {k}_{s} for accurate fluid dynamic problem modeling. While numerous roughness correlation formulas have been proposed to predict {k}_{s} in the fully rough regime, most of them are restricted to certain roughness types, with various geometric parameters considered in each case, leading to ongoing disagreements regarding its parameterization and lack of universality. In this study, a Particle Swarm Optimized Backpropagation (PSO-BP) method is proposed to predict {k}_{s} based on the selected surface parameters from previous DNS, LES, and experimental results for flow behavior over various surface roughness. The PSO-BP model’s ability to predict {k}_{s} in the fully rough region is evaluated and compared with both the existing roughness correction formulas as well as the traditional BP model. An optimized polynomial function is also proposed to serve as a ‘white box’ for predicting {k}_{s}. It turns out that the PSO-BP method has better performance in the evaluation metrics compared to other methods, yielding a Mean Absolute Error (MAE) of 0.0390, a Mean Squared Error (MSE) of 0.0026 and a Mean Absolute Percentage Error (MAPE) of 28.12%. This novel approach for estimating {k}_{s} has practical applicability and holds promise for improving the precision and efficiency of calculations related to equivalent sand-grain roughness, and thus provides more accurate and effective solutions for CFD and other engineering applications.

Application of Bp neural network model based on modern popular music and traditional music culture fusion teaching in colleges and universities

Abstract This paper analyzes the characteristics of music teaching development, constructs a variety of music integration teaching methods in the background of the “Internet+” era, and evaluates the quality of this teaching method based on the BP neural network algorithm. Since the traditional BP neural network cannot consider modeling the critical point of adjacent teaching quality levels, a Markov chain Monte Carlo simulation with adaptive sampling and BP network is used to study the teaching evaluation problem. Simulation through the constructed evaluation system yields that the prediction accuracy of the model constructed in this paper reaches more than 94%, and the absolute value of the relative error between the predicted and expected results of the model in this paper is controlled within 1.5%. According to this study, teaching the integration of modern pop music and traditional music culture on the Internet is feasible.

Soil Heavy-Metal Pollution Prediction Methods Based on Two Improved Neural Network Models

Current soil pollution prediction methods need improvement, especially with regard to accuracy in supplementing missing heavy-metal values in soil, and the accuracy and slow convergence speed of methods for predicting heavy-metal content at unknown points. To reduce costs and improve prediction accuracy, this study used two neural network models (SA-FOA-BP and SE-GCN) to supplement missing heavy-metal values and efficiently predict heavy-metal content in soil. The SA-FOA-BP model combines simulated annealing and fruit fly algorithms to optimize the parameter search method in traditional BP neural networks and improve prediction of missing heavy-metal values in soil. A spatial information fusion graph convolutional network prediction model (SE-GCN) constructs a spatial information encoder that can perceive spatial context information, and embeds it with spatial autocorrelation used for auxiliary learning to predict the heavy-metal content in soil. From the experimental results, the SE-GCN model demonstrates improved performance in terms of evaluation indicators compared with other models. Application analysis of the two improved neural network models was conducted; application scenarios and suitability were analyzed, showing that these models have practical application value for soil pollution prediction.

Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.

Word difficulty prediction—based on GA-BP neural network and TOPSIS-GRA method

The game WORDLE is very popular around the world, so our team tried to build a suitable model to analyze the difficulty of the words in the game. Firstly, this paper preprocesses the data to ensure the accuracy and integrity of the data. After extracting five representative characteristics, such as "letter frequency score", "word distance" and "syllable harmony", GA-BP neural network was selected to predict the percentage distribution of word attempts. The results show that compared with the traditional BP neural network, the GA-BP model with learning rate of 0.3, crossover rate of 0.7 and variation rate of 0.01 maintains a higher prediction accuracy, and the MSE value is significantly reduced. In addition, this paper combines GRA model and TOPSIS method, and uses entropy weight method to calculate the objective weight of indicators to comprehensively evaluate the difficulty of words. The example analysis shows that the improved model is more distinguishable and overcomes the defect of TOPSIS method that Euclidean distance can’t distinguish ranking. At the same time, the ranking results of the two are close, which indicates the reliability and rationality of the difficulty prediction by using the model.

Numerical simulation method of aluminum alloy heat treatment process based on BP neural network

Abstract To address the shortcomings of the traditional BP neural network, this paper uses MEA to optimize the weights and thresholds in the traditional BP neural network, generates sufficient training samples to enhance the generalization ability of the model, and introduces a maturity judgment function to determine whether convergence is achieved. Based on the MEA-BP neural network, the effect of multiple aging on the microstructure of the aluminum alloy is investigated by numerical simulation method with the help of aging treatment to determine the heat treatment process of aluminum alloy. The results show that after the aging time exceeds 24 h, the hardness of the alloy tends to increase significantly, and the precipitation rate of the precipitated phase decreases. The peak hardness of the alloy at 75-120°C is the highest in the hardness curve at the fourth aging temperature of 90°C, which is maximum at the 7thh (119.4 HV). For the effect of the microstructure of the aluminum alloy, the T-phase was not found in the sweep diagrams of the specimens from the three aging states. This study can provide a theoretical basis and technical support for the formulation and optimization of the production process of aluminum alloy materials.

Carbon Brush Temperature Prediction under Bayesian-BP Neural Network

Accurate prediction of carbon brush temperature is of vital importance for the safe, stable, and reliable operation of hydro turbines and hydropower plants. The carbon brush temperature prediction model based on the traditional BP neural network exhibits slow convergence and is prone to local minima. Therefore, this study proposes a Bayesian-based optimization model for the BP neural network, combining the principles of Bayesian inference and the BP neural network model. This model constructs the likelihood function of the weights and thresholds of the BP neural network to obtain the prior probability distribution and posterior probability distribution using Bayesian theorem. The optimal hyperparameters for the weights and thresholds are iteratively updated using the maximum likelihood method, and the learning efficiency is iteratively updated based on the number of training iterations. When the model’s convergence conditions and the maximum number of iterations are met, the optimal values for the weights, thresholds, and learning efficiency are obtained, effectively avoiding local optimization problems. Experimental results demonstrate that compared to the traditional BP neural network and LSTM neural network, the Bayesian-BP neural network achieves higher fidelity in fitting historical data and exhibits superior convergence accuracy and speed in temperature prediction. Therefore, it better fulfills the requirements for predicting carbon brush temperature in hydro turbines.

A Novel Method for Battery SOC Estimation Based on Slime Mould Algorithm Optimizing Neural Network under the Condition of Low Battery SOC Value

The State of Charge (SOC) is a crucial parameter in battery management systems, making accurate estimation of SOC essential for adjusting control strategies in automotive energy management and ensuring the performance of electric vehicles. In order to solve the problem that the estimation error of the traditional BP neural network increases sharply under complex conditions and low battery SOC values, a recurrent neural network estimation method based on slime mould algorithm optimization is proposed. Firstly, the data are serialized to include multiple discharge data. Secondly, the data are input into a recurrent neural network for SOC estimation, with a self-attention mechanism added to the network. Furthermore, it is found in the experiment that parameters have an impact on the estimation accuracy of the neural network, so the slime mould algorithm is introduced to optimize the parameters of the neural network. The experiment results show that the maximum error of the novel method is limited to within 5% under two conditions. It is worth noting that the SOC estimation error at low SOC value decreases instead of increasing, which shows the advantages of the novel method.

Abstract P114: Patient Preferences And Experiences With Cuffless Wrist Versus Upper Arm Cuff Blood Pressure Measurements In The CO ntinual Vs. O ccasiona L B lood P ressure Monitoring (COOL-BP) Study

Background: Cuffless BP devices can potentially enhance hypertension management and remote BP monitoring. Patient experience with these devices is a key driver of their acceptability and uptake. Methods: We implemented a remote hypertension program within the Mass General Brigham health system using traditional home BP monitoring to adjust medications. Selected patients in this program were enrolled in an ancillary study where BP was also measured via a cuffless wrist monitor (Aktiia SA, Switzerland). We analyzed patient experience surveys of all patients who successfully used each device for at least one full week in the program and completed surveys at 30, 60, and 180 days after enrollment. Final (180-day) surveys were analyzed. Results: Of 35 patients who used both devices, 34 completed all 3 surveys. Thirty-one patients (91%) indicated they preferred using the cuffless wrist monitor over the upper arm BP cuff, 1 (3%) preferred the BP cuff, and 2 (6%) had no preference. Top reasons for preference of the bracelet included ease of use, convenience, and number of readings (Figure). Most patients (27/34, 79%) used the cuffless device either daily or most days of the week, and 23 (68%) found it easy or very easy to use. Twenty-eight (82%) patients believed the bracelet could help manage their hypertension; 32 (94%) indicated that direct transmission of bracelet BP to their providers would be valuable. Conclusion: Among patients who used both traditional home BP monitoring and a cuffless continual BP monitoring bracelet in a remote hypertension management program, the vast majority found the cuffless device easier to use and more convenient, and preferred it over traditional BP cuffs.

Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure

Heat exchangers in operation often experience scaling, which can lead to a decrease in heat exchange efficiency and even safety accidents when fouling accumulates to a certain thickness. To address this issue, manual intervention is currently employed to monitor fouling thickness in advance. In this study, we propose a two-layer LSTM neural network model with an attention mechanism to effectively learn fouling thickness data under different working conditions. The model accurately predicts the scaling thickness of the heat exchanger during operation, enabling timely human intervention and ensuring that the scaling remains within a safe range. The experimental results demonstrate that our proposed neural network model (TA-LSTM) outperforms both the traditional BP neural network model and the LSTM neural network model in terms of accuracy and stability. Our findings provide valuable technical support for future research on heat exchanger descaling and fouling growth detection.