BP Neural Network Research Articles

Rotational Risley prisms: Fast and high-accuracy inverse solution and application based on back propagation neural networks

In the application of rotational Risley prism systems, there is a conflict between inverse solution time and accuracy. This paper proposes an inverse solution method based on back propagation neural networks to solve this problem. The training set data for prism rotation, azimuth, and elevation angles are produced based on the beam deflection model of the actual application. After that, two independent networks are trained individually. One network is used to calculate the angular difference, while the other network calculates the synchronous rotation angle to satisfy the solutions of elevation and azimuth angles of target. The azimuth and elevation root mean square errors for the inverse solution of the spiral trajectory using the trained network were 8.04e-04 arcsec and 2.60e-3 arcsec, respectively. At a solution time of 80 µs, the pointing accuracy in the experiment was less than 1 arcsec. The experimental results demonstrate that the proposed inverse solution method can obtain high-accuracy analytical solutions with low time consumption.

A robust approach for bond strength prediction of mortar using machine learning with SHAP interpretability

Application of machine learning (ML) in predicting mortars bond strength contributes to low experimental cost and high accuracy. This study explores the performance of four ML models—Back Propagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—using 304 experimental data points for training and 76 for testing. Among these, the XGBoost model demonstrated the highest predictive accuracy, with R², MAE, and RMSE values of 0.95, 0.06, and 0.13 in the training set, and 0.94, 0.08, and 0.16 in the testing set, respectively. Compared with traditional models, ML approaches, particularly XGBoost, were more effective in providing reliable predictions of bond strength. SHapley Additive exPlanation (SHAP) show that substrate type, ethylene-vinyl acetate (EVA) content and water to binder ratio W/C have the most remarkable effect on bond strength, while the type of cement and admixture shows the least effect. These findings highlight the potential of ML models in optimizing mortar formulations and improving prediction reliability.

Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design

This paper presents a comprehensive study on the design optimization of a robotic gripper, focusing on both the gripper modeling and the optimization of its parallel mechanism structure. This study integrates the Black-winged Kite Algorithm (BKA), Finite Element Analysis (FEA), Backpropagation Neural Network (BPNN), and response surface optimization techniques. The Good Point Set (GPS), nonlinear convergence factor, and adaptive t-distribution method improve BKA, which enhances exploration and exploitation performance, convergence speed, and solution quality. Subsequently, the parallel mechanism structure is designed to minimize the total mass, total deformation, and maximum equivalent stress. The central composite design (CCD) method was used to design the FEA experiment and establish the BKA-BPNN regression prediction model. The RMSE of this model’s training set and test set are 0.001615 and 0.0029328. A response surface optimization model is constructed to determine the best design solution. The optimized design achieves a 33.12% reduction in maximum equivalent stress, a 1.47% decrease in total mass, and a 0.16% reduction in maximum total deformation. This study provides valuable insights into the design optimization process for robotic grippers, showcasing the effectiveness of the proposed methodologies in enhancing performance while reducing mass and improving structural integrity.

Hydrogen-Bonded Organic Frameworks for Antibiotic Fluorescent Sensing Artificial Intelligence-Enhanced Anticounterfeiting.

The paramount importance of anticounterfeiting measures in safeguarding consumers from counterfeit products lies in their ability to ensure product safety and reliability. Advanced luminescent anticounterfeiting materials, particularly those responsive to multiple stimuli, afford a dynamic and multilayered security assurance. This study presents the synthesis of a novel material, Eu/Tb@GC-3, via postsynthetic modification, which exhibits notable photoluminescent properties with emission at 544 and 614 nm. The material demonstrates high selectivity and sensitivity in detecting Nitrofural and Enrofloxacin, with limits of detection at 0.0122 and 0.0280 μM, respectively. Furthermore, multistimulus responsive luminescent fibers and inks were developed, facilitating intelligent anticounterfeiting labels. The integration of these labels with back-propagation neural networks (BPNNs) significantly enhances pattern recognition and authentication capabilities, providing an efficacious strategy to combat counterfeit products and ensure consumer safety.

Optimization of thermal management performance of direct-cooled power battery based on backpropagation neural network and deep reinforcement learning

The batteries of new energy vehicles generate a large amount of heat when discharged at large multiplication rates, which can cause safety hazards if the heat is not eliminated in time. In this paper, a Swiss roll-type cooling belt is used to improve the thermal performance of lithium-ion battery packs. This paper selects six key parameters as optimization parameters, including the structure of Swiss roll-type cooling belt, coolant type, inlet coolant mass flow rate, inlet coolant gas volume fraction, battery discharge rate, and ambient temperature. Use neural network fitting and deep reinforcement learning to optimize these six parameters in order to achieve optimized cooling performance. The results show that after the cold plate has been structurally optimised and the inlet parameters have been optimised by deep reinforcement learning, the thermal performance has been significantly improved. Not only does the cold plate inlet require less mass flow, but the battery pack also performs better in terms of temperature difference.

A highly accurate and robust prediction framework for drilling rate of penetration based on machine learning ensemble algorithm

The rate of penetration (ROP) is a key indicator of drilling efficiency. Many researchers have explored the application of machine learning in ROP prediction. However, few studies have focused on the robustness of the constructed models, and developing a ROP prediction model that can achieve both high accuracy and strong robustness remains a challenge. This paper introduces a novel machine learning approach to constructing a ROP prediction model through ensemble learning algorithms. The model is based on field data from oilfields, incorporating 16 input parameters that influence ROP. First, the feasibility of the collected dataset is verified using correlation analysis. Then, ROP prediction models are developed based on various machine learning algorithms, including Decision Tree Regression (DTR), Random Forest (RF), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). By comparing the performance of these models under noise levels of 0%, 1.7%, and 5.1%, RF, LGBM, XGB, and SVR are selected as base learners. These base learners are then combined to construct multiple ensemble models, and the performance of the optimal ensemble model is evaluated under varying noise levels. The results show that the prediction error of the optimal model remains within 10%, and R2 is greater than 0.96. Finally, the Shapley Additive Explanations (SHAP) method is applied to perform interpretability analysis on the optimal ROP prediction model, examining the impact of different input factors on the model's predictive performance. Compared to single models and other ensemble models, the proposed ensemble model not only achieves higher accuracy but also demonstrates strong robustness and generalization capability.

Study on the response mechanisms and evolution prediction of groundwater microbial-toxicological indicators.

This study aims to investigate the response mechanisms of groundwater microbial-toxicological indicators, specifically total bacteria count (TBC) and total coliform count (TCC), to water quality indicators and environmental conditions. Using data from a water source in the western plateau of China, a predictive model focusing on TBC and TCC was developed. An orthogonal experimental design was employed to manipulate environmental conditions such as temperature, pH, and porosity, facilitating laboratory experiments. These experiments measured pH, chemical oxygen demand (COD), oxidation-reduction potential (ORP), TBC, and TCC at varying depths and environmental conditions. Principal component analysis elucidated the mechanisms by which water quality indicators and environmental conditions affect groundwater microbial-toxicological indicators. A prediction model for these indicators in plateau regions was established based on a backpropagation neural network (BP-NN), using TBC and TCC as target variables and the newly extracted principal components as influencing factors. The results demonstrate that environmental conditions and water quality indicators primarily influence the evolution of groundwater microbial-toxicological indicators by altering the ionic charge quantities, redox conditions, and temperature of the groundwater. The predictive model for groundwater microbial-toxicological indicators shows trends consistent with experimental outcomes, with an average relative error of less than 15%, meeting engineering requirements. PRACTITIONER POINTS: The values of total bacteria count (TBC) and total coliform count (TCC) under different conditions were obtained by column experiments. The influence mechanism of environmental conditions and groundwater indicators on TBC and TCC was elaborated by principal component analysis. TBC and TCC prediction models were established through the investigation of water sources in a plateau area and laboratory experiments.

Forecasting of the Event-driven Processes Using LSTM Network in the Context of Time of Arrival of On-demand City Transport

Abstract The precise forecasting of bus arrival times is an important element of implementing on demand city transport. This research uses of Long Short Term Memory (LSTM) networks for predicting bus arrival times in Sofia, Bulgaria. We evaluate the LSTM model against advanced models such as ARIMAX, VARX SARIMAX with Fourier terms Vector Autoregression, Bayesian Fourier models and Backpropagation Neural Networks using Root Mean Squared Error (RMSE) as the performance measure. The results points towards LSTM being better than approaches on routes by adeptly capturing intricate temporal relationships showcasing its promise in dynamic urban transit settings. Moreover the Bayesian Fourier model excels across routes underscoring its ability to accommodate trends effectively. These results suggest that incorporating LSTM networks into on demand transportation systems can notably enhance arrival time forecasts ultimately benefiting passengers and operational efficiency. This study demonstrates the usefulness of machine learning methods in urban transport planning and management by demonstrating how LSTM networks can improve optimization, on demand transportation.

Development and characterization of carboxylated copper oxide conjugated polymeric nanocomposites and correlating with computational techniques

The quality and effectiveness of composite-based components are influenced by their mechanical characteristics. Hence, a structured approach is essential to identify the key factors that contribute to achieving superior mechanical characteristics. This study investigates the utilization of an artificial neural network (ANN) to predict the mechanical characteristics, explicitly density, and hardness, of carboxyl copper oxide nanoparticle (CCONP)/DL-lactide and glycolide copolymer (PDLG) nanocomposites fabricated through microwave-assisted sintering. The research explores the effects of numerous microwave sintering conditions on the microstructure and mechanical properties of the nanocomposites. Physical characterization techniques including FTIR and TEM analysis are employed to confirm physical interactions and nanocomposite size. A back-propagation neural network with a 2–10-2 architecture was developed to predict density and hardness. The network was trained using the Levenberg-Marquardt algorithm and the Scaled Conjugate Gradient method. The predicted values are compared with experimental results, showing good agreement with correlation coefficients (R) of 0.883 for density and 0.9737 for hardness. This demonstrates the effectiveness of the ANN approach in evaluating the mechanical properties of CCONP/PDLG nanocomposites, providing a reliable tool for decision-making and potentially reducing experimental characterization costs.

Estimating water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images

UAV imaging technology has become one of the means to quickly monitor water quality parameters in freshwater aquaculture ponds. The change of sunlight during a long flight affects the quality of UAV images, which will reduce the accuracy of monitoring water quality. This study aims to propose a method to correct spectral variation during UAV imaging and apply it to detect dissolved organic matter (DOM) concentration and dissolved oxygen (DO) content in freshwater aquaculture ponds. Firstly, a spectral correction method was used to transform UAV-based multispectral images. The spectral data before and after correction was extracted. Secondly, 18 spectral indices before and after correction were constructed. The optimal combination of indices was identified using correlation analysis algorithm. The estimation models of water quality parameters were then constructed and compared using the Random Forest (RF), Support Vector Regression (SVR), and BP neural network (BP) methods. The results showed that the accuracy of estimating DOM concentration using corrected spectral indices was significantly improved compared to pre-correction models, with the highest improvement of 38 % (SVR), the lowest of 23 % (BP), and an average improvement of 31 %. The RF model performed best, achieving R² = 0.81, RMSE = 3.34 mg/L, and MAE = 2.17 mg/L. For DO content estimation, the accuracy of models using corrected spectral indices was also improved significantly, with the highest improvement of 97 % (RF), the lowest of 39 % (SVR), and an average improvement rate of 67 %. The Random Forest model was again optimal, with R² = 0.69, RMSE = 1.97 mg/L, and MAE = 1.47 mg/L. This study indicates that the proposed spectral correction method helps to map the concentration of DOM and DO in freshwater aquaculture ponds with high accuracy using UAV-based multispectral images.

Enhancing Weather Forecast Accuracy Through the Integration of WRF and BP Neural Networks: A Novel Approach

AbstractIn the past century, scholars from both domestic and international communities have delved into the study of numerical weather prediction models to promptly understand meteorological factors and mitigate the impacts of extreme weather events on humanity. Effective and precise prediction models enable the forecasting of meteorological conditions in the upcoming days, empowering individuals to implement proactive measures to minimize the adverse effects of extreme weather (Liang et al., 2021). The WRF (Weather Research and Forecasting) modeling system is commonly used for forecasting meteorological elements. However, uncertainties terribly hamper the correctness of the forecasting results. To this end, the present study was conducted to build a secondary model on the basis of the WRF forecast model. The WRF‐BPNN model was proposed for verification after constructing the network, the temperature vertical profile and the mixing ratio vertical profile were predicted, and the results on the validation set were tested. The results showed that the WRF‐BPNN model could effectively predict the temperature profile and mixing ratio profile, presenting better performance than the traditional WRF model.

Co-seismic landslide susceptibility mapping for the Luding earthquake area based on heterogeneous ensemble machine learning models

ABSTRACT Considering the complexity of landslide susceptibility mapping (LSM) at the municipal-scale, it is difficult for the assumption space of a single machine learning (ML) algorithm to fully and accurately reflect the intrinsic correlation between the landslide influencing factors and the actual catastrophic events under all scenarios. This study addresses this by integrating three heterogeneous ensemble learning (HEL) algorithms(Bagging, Blending, and Stacking) with four ML models (Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Backpropagation-Neural Network (BPNN)), to construct 27 HEL LSM models, and to realize optimization and selection of the models and algorithms through the comparative analysis of the results of the LSM and the accuracy of the prediction of the individual models. The final results indicate that the Bagging and Blending HEL algorithms fail to enhance model prediction accuracy effectively. In contrast, the Stacking HEL algorithm significantly improves model prediction accuracy (with an accuracy increase of over 1.4% and AUC values above 0.95). The Stacking (GBDT-XGBoost) model had the highest prediction accuracy (AUC = 0.964) and thus was selected as the optimal LSM model. This study's modeling and mapping results are intended to serve as a reference for geohazard prevention and sustainability studies in related cities.

ILKD: An Incremental Learning and Knowledge Distillation Framework for Coronavirus Risk Prediction

Abstract The coronavirus pandemic has seriously affected public health and social order. Prediction methods based on machine learning can identify the infectivity phenotype and pandemic risk of coronavirus. Currently, six types of coronaviruses that infect humans have been discovered, with significant differences in viral genome sequences. Continuous genetic variation of the virus will lead to reduced performance of machine learning models and potential learning forgetting. To solve this challenge, we propose an incremental learning and knowledge distillation framework (ILKD). First, we employ Dna2Vec to extract virus features and encode the virus sequence into virus feature vector. Second, we use hierarchical clustering to continuously identify new coronavirus groups. Third, ILKD employ a combined strategy of incremental learning and knowledge distillation to transform the Back Propagation (BP) neural network to continuously learn and predict the phenotypes of human-to-human coronavirus infection. Experimental results show that ILKD can effectively alleviate the learning forgetting phenomenon. Further analysis reveals ILKD has better performance than other incremental learning models, and has important public health application value.

FusionOC: Research on optimal control method for infrared and visible light image fusion

Infrared and visible light image fusion can solve the limitations of single-type visual sensors and can boost the target detection performance. However, since the traditional fusion strategy lacks the controllability and feedback mechanism, the fusion model cannot precisely perceive the relationship between the requirements of the fusion task, the fused image quality, and the source image features. To this end, this paper establishes a fusion model based on the optimal controlled object and control mode called FusionOC. This method establishes two types of mathematical models of the controlled objects by verifying the factors and conflicts affecting the quality of the fused image. It combines the image fusion model with the quality evaluation function to determine the two control factors separately. At the same time, two proportional-integral-derivative (PID) control and regulation modes based on the backpropagation (BP) neural network are designed according to the control factor characteristics. The fusion system can adaptively select the regulation mode to regulate the control factor according to the user requirements or the task to make the fusion system perceive the connection between the fusion task and the result. Besides, the fusion model employs the feedback mechanism of the control system to perceive the feature difference between the fusion result and the source image, realize the guidance of the source image feature to the entire fusion process, and improve the fusion algorithm's generalization ability and intelligence level when handling different fusion tasks. Experimental results on multiple public datasets demonstrate the advantages of FusionOC over advanced methods. Meanwhile, the benefits of our fusion results in object detection tasks have been demonstrated.

Research on Polyurethane Creep Prediction Model Based on BWO-BP

Abstract Polyurethane foam materials can creep under the influence of temperature, humidity, and pressure during use, which seriously affects their safety and state of use. Therefore, studying the creep behavior of polyurethane foam materials and developing corresponding creep mechanism models have significant scientific value for understanding their damage patterns. As a type of machine learning, Backpropagation (BP) neural network can be used for data analysis. However, there are many issues when determining the network structure. Whale Optimization (BWO) algorithm, as a type of swarm intelligence algorithm, can efficiently determine the framework of the BP model. BP which has been optimized through the BWO algorithm, has been utilized to construct a model for the prediction of polyurethane’s creep failure. The optimization process has significantly lowered the mean squared error from 0.30 in the non-optimized BP model to 0.07 in the enhanced BWO-BP model, which validates the effectiveness of the proposed algorithm.

Machine learning-assisted source tracing in domestic-industrial wastewater: A fluorescence information-based approach

An emergency water pollution incident poses a significant risk to the proper functioning of wastewater treatment plants, particularly in domestic-industrial integrated facilities. Source tracing is recognized as an effective method to mitigate ongoing impacts. Machine learning-assisted traceability is emerging as a more efficient and faster method compared to traditional methods. In this study, a total of 712 sets of characterization wastewater information from effluent samples from14 discharge enterprises across 6 different sectors, as well as domestic wastewater was collected using 3-dimensional fluorescence spectroscopy. After data cleaning and augmentation, a feature fingerprint database of wastewater was constructed to train a traceability model. Several machine learning algorithms, including Back Propagation neural network (BP), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest Neighbors (KNN), were selected for constructing the traceability framework. Subsequently, an advanced Particle Swarm Optimization Random Forest model (PSO-RF), capable of automatically optimizing model parameters, was proposed and applied to trace the sources of wastewater in integrated wastewater treatment plant. The PSO-RF achieved and accuracy of 96.55 % in sector identification and 94.25 % in manufacturer identification. As part of the validation process, laboratory simulations were conducted using blended wastewater with different volume ratios of domestic and industrial wastewater to evaluated the potential application of PSO-RF. The results consistently demonstrated PSO-RF's effectiveness, particularly in tracing pharmaceutical wastewater sources, maintaining an accuracy of over 85 %. This work presents a novel strategy for tracing abnormal sources during emergency pollutant incidents, providing essential support for integrating artificial intelligence (AI) into meticulous wastewater management.

Robust ELM-PID tracing control on autonomous mobile robot via transformer-based pavement crack segmentation

Pavement crack tracing is paramount to missions encompassing automated crack sealing for road maintenance. However, existing methods still face several challenges, including the incapability to precisely extract crack trajectories and the challenge of tuning control parameters within intricate backgrounds. To address these limitations, the ViT-S2T network and the ELM-PID control system are proposed for crack tracing. Specifically, the ViT-S2T consists of two branches. The transformer-based feature extraction module (TFEM) integrates multi-head attention mechanism and multi-layer perceptron to capture global contextual crack semantic features. The incoherent segmentation masks (ISM) employs a binary classifier to predict the coarsest irrelevant mask and further performs up-sampling fusion of higher-resolution features. Moreover, the Neural-PID control method is designed to track crack trajectories, combining Extreme Learning Machines (ELM) and Proportional Integral Derivative (PID). The ELM-PID controller utilizes a three-layer backpropagation neural network and proposes the ELM model for adaptive adjustment by predicting the tuning parameters of PID. This framework is applied to real-time visual tracing for edge AI. Extensive tests performed on three arduous datasets of DeepCrack, CFD, and S2TCrack, achieving a precision of 82.76% and mAP@0.5 of 75.63% and speed of 0.0513 m/s, demonstrating the superior and robust nature of our approach in pavement crack tracing.

Construction of data base containing flow field information under different blade placement of pumping station in digital twin

Abstract The data base in digital twin technology includes various data such as the structure, performance, and operating status of the physical object. And the data base is the key point for the construction of the digital twin system. However, the data bases of different digital twin systems usually contain different information. In this paper, the flow state of the pumping station under different angles of four blades is selected as the research object. The research methods in this paper mainly include Latin hypercube sampling, numerical simulation and neural network prediction. The Latin hypercube sampling is used to select the typical Angle difference model. The numerical simulation is used to obtain flow field information. The neural network prediction method is used to construct a complete data base. The study incorporates an actual large-scale mixed-flow pump station to head, efficiency, and shaft power as the prediction targets. It utilizes the different angle difference data of ten sets of four blades as the training set, forty sets of remaining samples as the experiment set, and employs the backpropagation neural network method for constructing the prediction network. By changing the experiment set and test set, the method can efficiently complete the construction of data base including actual test and numerical simulation, which provides a solid foundation for the construction of digital twin platform.

Optimized BP-LSTM neural network based on IGA for heat exchanger outlet temperature prediction

Abstract In this paper, an improved Genetic Algorithm (IGA) is proposed to optimize the BP-LSTM neural network for predicting heat exchanger temperature. The study introduces an enhanced genetic algorithm designed to optimize both short- and long-term memory networks (LSTM) in conjunction with the BP neural network temperature prediction model and the BP-LSTM neural network model. Simulation and comparison results demonstrate that the proposed prediction method can effectively forecast heat exchanger temperatures.

Application of PCA-LSTM algorithm in predicting component faults in CNC machining centers

Abstract In order to further explore the relationship between component failures and CNC machining center failures, a method of predicting component failures in advance was proposed to reduce the likelihood of machining center failures and improve CNC machining efficiency. In this paper, the principal component analysis method is used to optimize the Long short-term memory neural network to build a network model to study and predict the fault status of NC machining center components in work. The use of Principal Component Analysis (PCA) to extract the main features of machine tool faults can improve the speed of iterative training networks during the machining process and reduce the input dimension and prediction difficulty of LSTM. The extracted data is used as input for the LSTM network. The fault type is output. The comparison of diagnostic results between PCA-LSTM, LSTM, and BP neural networks shows that PCA-LSTM is a high-precision prediction model. The root mean square difference is reduced to 1.572. The predicted error results can effectively reflect the changes that occur in the machining center components with the occurrence of faults. It can be seen that it is feasible to use PCA-LSTM as a tool for predicting the faults of CNC machining center components.