As computer graphics technology continues to advance,Collision Detection(CD)has emerged as a critical element in fields such as virtual reality,computer graphics,and interactive simulations.CD is indispensable for ens...As computer graphics technology continues to advance,Collision Detection(CD)has emerged as a critical element in fields such as virtual reality,computer graphics,and interactive simulations.CD is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments,particularly within complex scenarios like virtual assembly,where both high precision and real-time responsiveness are imperative.Despite ongoing developments,current CD techniques often fall short in meeting these stringent requirements,resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly systems.To address these limitations,this study introduces a novel algorithm that leverages the capabilities of a Backpropagation Neural Network(BPNN)to optimize the structural composition of the Hybrid Bounding Volume Tree(HBVT).Through this optimization,the research proposes a refined Hybrid Hierarchical Bounding Box(HHBB)framework,which is specifically designed to enhance the computational efficiency and precision of CD processes.The HHBB framework strategically reduces the complexity of collision detection computations,thereby enabling more rapid and accurate responses to collision events.Extensive experimental validation within virtual assembly environments reveals that the proposed algorithm markedly improves the performance of CD,particularly in handling complex models.The optimized HBVT architecture not only accelerates the speed of collision detection but also significantly diminishes error rates,presenting a robust and scalable solution for real-time applications in intricate virtual systems.These findings suggest that the proposed approach offers a substantial advancement in CD technology,with broad implications for its application in virtual reality,computer graphics,and related fields.展开更多
Objective:Non-small cell lung cancer(NSCLC)patients often experience significant fear of recurrence.To facilitate precise identification and appropriate management of this fear,this study aimed to compare the efficacy...Objective:Non-small cell lung cancer(NSCLC)patients often experience significant fear of recurrence.To facilitate precise identification and appropriate management of this fear,this study aimed to compare the efficacy and accuracy of a Backpropagation Neural Network(BPNN)against logistic regression in modeling fear of cancer recurrence prediction.Methods:Data from 596 NSCLC patients,collected between September 2023 and December 2023 at the Cancer Hospital of the Chinese Academy of Medical Sciences,were analyzed.Nine clinically and statistically significant variables,identified via univariate logistic regression,were inputted into both BPNN and logistic regression models developed on a training set(N=427)and validated on an independent set(N=169).Model performances were assessed using Area Under the Receiver Operating Characteristic(ROC)Curve and Decision Curve Analysis(DCA)in both sets.Results:The BPNN model,incorporating nine selected variables,demonstrated superior performance over logistic regression in the training set(AUC=0.842 vs.0.711,p<0.001)and validation set(0.7 vs.0.675,p<0.001).Conclusion:The BPNN model outperforms logistic regression in accurately predicting fear of cancer recurrence in NSCLC patients,offering an advanced approach for fear assessment.展开更多
Ionic polymer-metal composites(IPMCs)constitute a new type of artificial muscle material that is commonly used in bionic soft robots and medical devices because of its small driving voltage and considerable deformatio...Ionic polymer-metal composites(IPMCs)constitute a new type of artificial muscle material that is commonly used in bionic soft robots and medical devices because of its small driving voltage and considerable deformation.However,IPMCs are limited by performance issues such as low output force and small operating time away from water.Silicon dioxide sulfonated graphene(SiO_(2)-SGO)particles are often used to improve the performance of polymer membranes because of their hydrophilicity and high chemical stability.Reported here is the addition of SiO_(2)-SGO particles prepared by in situ hydrolysis to perfluorosulfonic acid in order to improve the IPMC properties.Also,a predictive model was constructed based on a backpropagation neural network,with the SiO_(2)-SGO doping amount and the IPMC excitation voltage in the input layer and the driving displacement in the output layer.The results show that the IPMC prepared with 1.0 wt.%doping content performed the best,with a maximum output displacement of 47.7 mm.The correlation coefficient(R2)was 0.9842 and the mean square error was 0.00037073,which show that the predictive model has high predictive accuracy and is suitable for predicting the performance of the SiO_(2)-SGO-modified IPMC.展开更多
The evaluation of construction safety risks has become a crucial task with the increasing development of bridge construction.This paper aims to provide an overview of the application of backpropagation neural networks...The evaluation of construction safety risks has become a crucial task with the increasing development of bridge construction.This paper aims to provide an overview of the application of backpropagation neural networks in assessing safety risks during bridge construction.It introduces the situation,principles,methods,and advantages,as well as the current status and future development directions of backpropagation-related research.展开更多
Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on s...Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.展开更多
The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner dia...The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.展开更多
The model describing the dependence of the mechanical properties on the chemical composition and as deformation techniques of tungsten heavy alloy is established by the method of improved the backpropagation neural ne...The model describing the dependence of the mechanical properties on the chemical composition and as deformation techniques of tungsten heavy alloy is established by the method of improved the backpropagation neural network. The mechanical properties＇ parameters of tungsten alloy and deformation techniques for tungsten alloy are used as the inputs. The chemical composition and deformation amount of tungsten alloy are used as the outputs. Then they are used for training the neural network. At the same time, the optimal number of the hidden neurons is obtained through the experiential equations, and the varied step learning method is adopted to ensure the stability of the training process. According to the requirements for mechanical properties, the chemical composition and the deformation condition for tungsten heavy alloy can be designed by this artificial neural network system.展开更多
The dispersion of vehicular paths is a common phenomenon in the inner area of signalized intersections due to heterogeneous driver behavior and interactions.This study aims to develop an explainable neural network-bas...The dispersion of vehicular paths is a common phenomenon in the inner area of signalized intersections due to heterogeneous driver behavior and interactions.This study aims to develop an explainable neural network-based model to describe the vehicle path dispersion by exploring the relationship between the path dispersion and external factors.A backpropagation neural network model was established to analyze the effects of external factors on the dispersion of through and left-turn paths based on real trajectory data collected from 20 intersections in Shanghai,China.Twelve influencing factors in varying geometric,traffic,signalization,and traffic management conditions were considered.The predictive power and transferability of the model were verified by applying the trained model on the four new intersections.The contributions of the influencing factors on the path dispersion were explored based on the neural interpretation diagram,relative importance of influencing factors,and sensitivity analysis to offer explanatory insights for the proposed model.The results show that the mean absolute percentage errors of the path dispersion models for the through and left-turn movements are only 14.67%and 17.65%,respectively.The through path dispersion is primarily influenced by the number of exit lanes,the offset degree between the approach and exit lanes,and the traffic saturation degree on the through lane.In contrast,the path dispersion of the left turn is mainly affected by the number of exit lanes,the left-turn angle,and the setting of guide lines.展开更多
Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applic...Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.展开更多
The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optim...The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.展开更多
This study aimed to develop a neural network(NN)-based method to predict the long-term mean radiant temperature(MRT)around buildings by using meteorological parameters as training data.The MRT dramatically impacts bui...This study aimed to develop a neural network(NN)-based method to predict the long-term mean radiant temperature(MRT)around buildings by using meteorological parameters as training data.The MRT dramatically impacts building energy consumption and significantly affects outdoor thermal comfort.In NN-based long-term MRT prediction,two main restrictions must be overcome to achieve precise results:first,the difficulty of preparing numerous training datasets;second,the challenge of developing an accurate NN model.To overcome these restrictions,a combination of principal component analysis(PCA)and K-means clustering was employed to reduce the training data while maintaining high prediction accuracy.Second,three widely used NN models(feedforward NN(FFNN),backpropagation NN(BPNN),and BPNN optimized using a genetic algorithm(GA-BPNN))were compared to identify the NN with the best long-term MRT prediction performance.The performances of the tested NNs were evaluated using the mean absolute percentage error(MAPE),which was≤3%in each case.The findings indicate that the training dataset was reduced effectively by the PCA and K-means.Among the three NNs,the GA-BPNN produced the most accurate results,with its MAPE being below 1%.This study will contribute to the development of fast and feasible outdoor thermal environment prediction.展开更多
Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the c...Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m<sup>2</sup>] in the validation dataset.展开更多
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s...Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.展开更多
The flow of blasted ore during mining of moderately dipping medium-thick orebodies is a challenge.Selecting a suitable mining system for such ore bodies is difficult.This paper proposes a diamond layout sublevel open ...The flow of blasted ore during mining of moderately dipping medium-thick orebodies is a challenge.Selecting a suitable mining system for such ore bodies is difficult.This paper proposes a diamond layout sublevel open stoping system using fan blastholes with backfilling to mine such orebodies.To evaluate the performance of system the relationships between ore recovery and stope footwall dip angle,footwall surface roughness,drawpoint spacing and production blast ring burden were investigated.An ore recovery data set from 81 laboratory physical model experiments was established from combinations of the listed factors.Various modules in a back propagation neural network structure were compared,and an optimal network structure identified.An ore recovery backpropagation neural network(BPNN)forecast model was developed.Using the model and sensitivity analysis of the factors affecting the proposed open stope mining system,the significance of each factor on ore recovery was studied.The study results were applied to a case study at the Shandong Gold Group Jiaojia Gold Mine.The results showed that the application of a BPNN and sensitivity analysis models for ore recovery prediction in the proposed mining system and field experimental results confirm that the suggested mining method is feasible.展开更多
This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 ...This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 scaled model with a large-span retractable roof was tested in wind tunnel under both laminar and turbulent flow conditions.The distributions of mean and fluctuating wind pressure coefficients were first described,and the effects of inflow turbulence,wind direction,roof opening were examined separately.For the prediction of wind pressure,the POD-BPNN model was trained using measurement data from adjacent points.The prediction results are overall satisfactory.The root-mean-square-error(RMSE)between test and predicted data lies mostly within 10%.In particular,the prediction of mean wind pressure is found to be better than that of fluctuating wind pressure.The outcomes in this study highlight that the proposed POD-BPNN model can be well used as a useful tool to predict surface wind pressure.展开更多
Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have bee...Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data.展开更多
Rapid assessment of foliar chlorophyll content in tobacco is critical for assessment of growth and precise management to improve quality and yield while minimizing adverse environmental impact. Our objective is to dev...Rapid assessment of foliar chlorophyll content in tobacco is critical for assessment of growth and precise management to improve quality and yield while minimizing adverse environmental impact. Our objective is to develop a precise agricultural practice predicting tobacco-leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content. Reflectance experiments have been conducted on flue-cured tobacco over 3 consecutive years under different light quality. Leaf hyperspectral reflectance and chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content data have been collected at 15-day intervals from 30 days after transplant until harvesting. We identified the central band that is sensitive to tobacco-leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content and the optimum wavelength combinations for establishing new spectral indices (simple ratio index, RVI;normalized difference vegetation index, NDVI;and simple difference vegetation index, DVI). We then established linear and BackPropagation (BP) neural network models to estimate chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content. The central bands for leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content are concentrated in the visible range (410 - 680 nm) in combination with the shortwave infrared range (1900 - 2400 nm). The optimum spectral range for the spectral band combinations</span><span style="font-family:Verdana;"> RVI, NDVI, and DVI</span><span style="font-family:Verdana;"> are 440 and 470 nm, 440 and 470 nm, and 440 and 460 nm, respectively. The linear RVI, NDVI, and DVI models, SMLR model and the BP neural network model have respective R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> values of 0.76, 0.77, 0.69, 0.78 and 0.86, and root mean square error values of 0.63, 1.60, 1.59, 2.04 and 0.05 mg chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;">/g (fresh weight), respectively. Our results identified chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> sensitive spectral regions and new indices facilitate a rapid, non-destructive field estimation of leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content for tobacco.展开更多
The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of o...The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.展开更多
The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults,the transient process is analyzed in this paper.Through the study we t...The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults,the transient process is analyzed in this paper.Through the study we take that the main resonant frequency and its corresponding component is related to the fault distance.Based on this,a fault location method based on double-end wavelet energy ratio at the scale corresponding to the main resonant frequency is proposed.And back propagation neural network(BPNN)is selected to fit the non-linear relationship between the wavelet energy ratio and fault distance.The performance of this proposed method has been verified in different scenarios of a simulation model in PSCAD/EMTDC.展开更多
Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study propose...Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study proposes a cascade control to solve this problem.Based on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned vehicles.To improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving conditions.The speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed variations.Finally,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned vehicles.Under strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.展开更多
文摘As computer graphics technology continues to advance,Collision Detection(CD)has emerged as a critical element in fields such as virtual reality,computer graphics,and interactive simulations.CD is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments,particularly within complex scenarios like virtual assembly,where both high precision and real-time responsiveness are imperative.Despite ongoing developments,current CD techniques often fall short in meeting these stringent requirements,resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly systems.To address these limitations,this study introduces a novel algorithm that leverages the capabilities of a Backpropagation Neural Network(BPNN)to optimize the structural composition of the Hybrid Bounding Volume Tree(HBVT).Through this optimization,the research proposes a refined Hybrid Hierarchical Bounding Box(HHBB)framework,which is specifically designed to enhance the computational efficiency and precision of CD processes.The HHBB framework strategically reduces the complexity of collision detection computations,thereby enabling more rapid and accurate responses to collision events.Extensive experimental validation within virtual assembly environments reveals that the proposed algorithm markedly improves the performance of CD,particularly in handling complex models.The optimized HBVT architecture not only accelerates the speed of collision detection but also significantly diminishes error rates,presenting a robust and scalable solution for real-time applications in intricate virtual systems.These findings suggest that the proposed approach offers a substantial advancement in CD technology,with broad implications for its application in virtual reality,computer graphics,and related fields.
基金Supported by Beijing Hope Run Special Fund of Cancer Foundation of China(LC2022C05).
文摘Objective:Non-small cell lung cancer(NSCLC)patients often experience significant fear of recurrence.To facilitate precise identification and appropriate management of this fear,this study aimed to compare the efficacy and accuracy of a Backpropagation Neural Network(BPNN)against logistic regression in modeling fear of cancer recurrence prediction.Methods:Data from 596 NSCLC patients,collected between September 2023 and December 2023 at the Cancer Hospital of the Chinese Academy of Medical Sciences,were analyzed.Nine clinically and statistically significant variables,identified via univariate logistic regression,were inputted into both BPNN and logistic regression models developed on a training set(N=427)and validated on an independent set(N=169).Model performances were assessed using Area Under the Receiver Operating Characteristic(ROC)Curve and Decision Curve Analysis(DCA)in both sets.Results:The BPNN model,incorporating nine selected variables,demonstrated superior performance over logistic regression in the training set(AUC=0.842 vs.0.711,p<0.001)and validation set(0.7 vs.0.675,p<0.001).Conclusion:The BPNN model outperforms logistic regression in accurately predicting fear of cancer recurrence in NSCLC patients,offering an advanced approach for fear assessment.
基金funded by the Digital workshop dynamic Reconstruction modeling Technology(Grant No.2020YFB1710701)the National Natural Science Foundation of China(Grant No.52172099)the Provincial Joint Fund of Shaanxi(Grant No.2021JLM-28).
文摘Ionic polymer-metal composites(IPMCs)constitute a new type of artificial muscle material that is commonly used in bionic soft robots and medical devices because of its small driving voltage and considerable deformation.However,IPMCs are limited by performance issues such as low output force and small operating time away from water.Silicon dioxide sulfonated graphene(SiO_(2)-SGO)particles are often used to improve the performance of polymer membranes because of their hydrophilicity and high chemical stability.Reported here is the addition of SiO_(2)-SGO particles prepared by in situ hydrolysis to perfluorosulfonic acid in order to improve the IPMC properties.Also,a predictive model was constructed based on a backpropagation neural network,with the SiO_(2)-SGO doping amount and the IPMC excitation voltage in the input layer and the driving displacement in the output layer.The results show that the IPMC prepared with 1.0 wt.%doping content performed the best,with a maximum output displacement of 47.7 mm.The correlation coefficient(R2)was 0.9842 and the mean square error was 0.00037073,which show that the predictive model has high predictive accuracy and is suitable for predicting the performance of the SiO_(2)-SGO-modified IPMC.
基金Key natural science research project of Anhui Province in 2023 research on risk assessment of bridge engineering project based on BP neural network(2023AH052746)。
文摘The evaluation of construction safety risks has become a crucial task with the increasing development of bridge construction.This paper aims to provide an overview of the application of backpropagation neural networks in assessing safety risks during bridge construction.It introduces the situation,principles,methods,and advantages,as well as the current status and future development directions of backpropagation-related research.
文摘Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.
基金supported by the National Natural Science Foundation of China(Grant No.52271277)the Natural Science Foundation of Jiangsu Province(Grant.No.BK20211343)+1 种基金the State Key Laboratory of Ocean Engineering(Shanghai Jiao Tong University)(Grant.No.GKZD010081)Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant.No.SJCX22_1906).
文摘The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.
文摘The model describing the dependence of the mechanical properties on the chemical composition and as deformation techniques of tungsten heavy alloy is established by the method of improved the backpropagation neural network. The mechanical properties＇ parameters of tungsten alloy and deformation techniques for tungsten alloy are used as the inputs. The chemical composition and deformation amount of tungsten alloy are used as the outputs. Then they are used for training the neural network. At the same time, the optimal number of the hidden neurons is obtained through the experiential equations, and the varied step learning method is adopted to ensure the stability of the training process. According to the requirements for mechanical properties, the chemical composition and the deformation condition for tungsten heavy alloy can be designed by this artificial neural network system.
基金supported by grants from the National Natural Science Foundation of China(71971140 and 52122215)the Shanghai Pujiang Program(21PJC085)the Shanghai Shuguang Program(22SG45).
文摘The dispersion of vehicular paths is a common phenomenon in the inner area of signalized intersections due to heterogeneous driver behavior and interactions.This study aims to develop an explainable neural network-based model to describe the vehicle path dispersion by exploring the relationship between the path dispersion and external factors.A backpropagation neural network model was established to analyze the effects of external factors on the dispersion of through and left-turn paths based on real trajectory data collected from 20 intersections in Shanghai,China.Twelve influencing factors in varying geometric,traffic,signalization,and traffic management conditions were considered.The predictive power and transferability of the model were verified by applying the trained model on the four new intersections.The contributions of the influencing factors on the path dispersion were explored based on the neural interpretation diagram,relative importance of influencing factors,and sensitivity analysis to offer explanatory insights for the proposed model.The results show that the mean absolute percentage errors of the path dispersion models for the through and left-turn movements are only 14.67%and 17.65%,respectively.The through path dispersion is primarily influenced by the number of exit lanes,the offset degree between the approach and exit lanes,and the traffic saturation degree on the through lane.In contrast,the path dispersion of the left turn is mainly affected by the number of exit lanes,the left-turn angle,and the setting of guide lines.
基金supported by the National Key Research and Development Program of China(No.2023YFF0715103)-financial supportNational Natural Science Foundation of China(Grant Nos.62306237 and 62006191)-financial support+1 种基金Key Research and Development Program of Shaanxi(Nos.2024GX-YBXM-149 and 2021ZDLGY15-04)-financial support,NorthwestUniversity Graduate Innovation Project(No.CX2023194)-financial supportNatural Science Foundation of Shaanxi(No.2023-JC-QN-0750)-financial support.
文摘Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.
基金funded by the National Natural Science Foundation of China(Grant Nos.42472345,52325905,42407202).
文摘The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.
基金This study was supported by a Grant-in-Aid for Challenging Research(Exploratory)(No.19K22004)the China Scholarship Council(No.201708430100).
文摘This study aimed to develop a neural network(NN)-based method to predict the long-term mean radiant temperature(MRT)around buildings by using meteorological parameters as training data.The MRT dramatically impacts building energy consumption and significantly affects outdoor thermal comfort.In NN-based long-term MRT prediction,two main restrictions must be overcome to achieve precise results:first,the difficulty of preparing numerous training datasets;second,the challenge of developing an accurate NN model.To overcome these restrictions,a combination of principal component analysis(PCA)and K-means clustering was employed to reduce the training data while maintaining high prediction accuracy.Second,three widely used NN models(feedforward NN(FFNN),backpropagation NN(BPNN),and BPNN optimized using a genetic algorithm(GA-BPNN))were compared to identify the NN with the best long-term MRT prediction performance.The performances of the tested NNs were evaluated using the mean absolute percentage error(MAPE),which was≤3%in each case.The findings indicate that the training dataset was reduced effectively by the PCA and K-means.Among the three NNs,the GA-BPNN produced the most accurate results,with its MAPE being below 1%.This study will contribute to the development of fast and feasible outdoor thermal environment prediction.
文摘Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m<sup>2</sup>] in the validation dataset.
文摘Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.
基金funded by the State Key Research Development Program of China(2018YFC0604400)the National Science Foundation of China(No.51874068)+2 种基金the Fundamental Research Funds for the Central Universities(N160107001,N180701016)the 111 Project(B17009)Nazarbayev University for the Faculty Development Competitive Research Grant(240919FD3920)。
文摘The flow of blasted ore during mining of moderately dipping medium-thick orebodies is a challenge.Selecting a suitable mining system for such ore bodies is difficult.This paper proposes a diamond layout sublevel open stoping system using fan blastholes with backfilling to mine such orebodies.To evaluate the performance of system the relationships between ore recovery and stope footwall dip angle,footwall surface roughness,drawpoint spacing and production blast ring burden were investigated.An ore recovery data set from 81 laboratory physical model experiments was established from combinations of the listed factors.Various modules in a back propagation neural network structure were compared,and an optimal network structure identified.An ore recovery backpropagation neural network(BPNN)forecast model was developed.Using the model and sensitivity analysis of the factors affecting the proposed open stope mining system,the significance of each factor on ore recovery was studied.The study results were applied to a case study at the Shandong Gold Group Jiaojia Gold Mine.The results showed that the application of a BPNN and sensitivity analysis models for ore recovery prediction in the proposed mining system and field experimental results confirm that the suggested mining method is feasible.
基金This project was funded by grants from the National Natural Science Foundation of China(No.51778072 and No.51408062)Practice Innovation and Entrepreneurship Enhancement Plan of CSUST(SJCX202021).
文摘This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 scaled model with a large-span retractable roof was tested in wind tunnel under both laminar and turbulent flow conditions.The distributions of mean and fluctuating wind pressure coefficients were first described,and the effects of inflow turbulence,wind direction,roof opening were examined separately.For the prediction of wind pressure,the POD-BPNN model was trained using measurement data from adjacent points.The prediction results are overall satisfactory.The root-mean-square-error(RMSE)between test and predicted data lies mostly within 10%.In particular,the prediction of mean wind pressure is found to be better than that of fluctuating wind pressure.The outcomes in this study highlight that the proposed POD-BPNN model can be well used as a useful tool to predict surface wind pressure.
基金The National Key Research and Development Program of China under contract No.2018YFC1406206the National Natural Science Foundation of China under contract No.41876014.
文摘Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data.
文摘Rapid assessment of foliar chlorophyll content in tobacco is critical for assessment of growth and precise management to improve quality and yield while minimizing adverse environmental impact. Our objective is to develop a precise agricultural practice predicting tobacco-leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content. Reflectance experiments have been conducted on flue-cured tobacco over 3 consecutive years under different light quality. Leaf hyperspectral reflectance and chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content data have been collected at 15-day intervals from 30 days after transplant until harvesting. We identified the central band that is sensitive to tobacco-leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content and the optimum wavelength combinations for establishing new spectral indices (simple ratio index, RVI;normalized difference vegetation index, NDVI;and simple difference vegetation index, DVI). We then established linear and BackPropagation (BP) neural network models to estimate chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content. The central bands for leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content are concentrated in the visible range (410 - 680 nm) in combination with the shortwave infrared range (1900 - 2400 nm). The optimum spectral range for the spectral band combinations</span><span style="font-family:Verdana;"> RVI, NDVI, and DVI</span><span style="font-family:Verdana;"> are 440 and 470 nm, 440 and 470 nm, and 440 and 460 nm, respectively. The linear RVI, NDVI, and DVI models, SMLR model and the BP neural network model have respective R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> values of 0.76, 0.77, 0.69, 0.78 and 0.86, and root mean square error values of 0.63, 1.60, 1.59, 2.04 and 0.05 mg chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;">/g (fresh weight), respectively. Our results identified chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> sensitive spectral regions and new indices facilitate a rapid, non-destructive field estimation of leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content for tobacco.
基金This project was funded by the Open Fund of the Key Laboratory of Exploration Technologies for Oil and Gas Resources,the Ministry of Education(No.K2021-03)National Natural Science Foundation of China(No.42106213)+2 种基金the Hainan Provincial Natural Science Foundation of China(No.421QN281)the China Postdoctoral Science Foundation(Nos.2021M690161 and 2021T140691)the Postdoctorate Funded Project in Hainan Province.
文摘The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.
基金supported by National Key R&D Program of China(2017YFB0902800)Science and 333 Technology Project of State Grid Corporation of China(52094017003D).
文摘The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults,the transient process is analyzed in this paper.Through the study we take that the main resonant frequency and its corresponding component is related to the fault distance.Based on this,a fault location method based on double-end wavelet energy ratio at the scale corresponding to the main resonant frequency is proposed.And back propagation neural network(BPNN)is selected to fit the non-linear relationship between the wavelet energy ratio and fault distance.The performance of this proposed method has been verified in different scenarios of a simulation model in PSCAD/EMTDC.
基金the Natural Science Foundation of Guangxi(No.2020GXNSFDA238011)the Open Fund Project of Guangxi Key Laboratory of Automation Detection Technology and Instrument(No.YQ21203)the Independent Research Project of Guangxi Key Laboratory of Auto Parts and Vehicle Technology(No.2020GKLACVTZZ02)。
文摘Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study proposes a cascade control to solve this problem.Based on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned vehicles.To improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving conditions.The speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed variations.Finally,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned vehicles.Under strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.
