Water resources management is nowadays a significant stake for the world. However, missing or bad quality of the hydro-climatic historical data available of the studied area makes sometimes hydrological studies diffic...Water resources management is nowadays a significant stake for the world. However, missing or bad quality of the hydro-climatic historical data available of the studied area makes sometimes hydrological studies difficult. Generally, conceptual rain-flow models are designed to bring an appropriate answer with the correction of gaps and prediction of the flows. Historical hydro-climatic data of the Ity station, located on Cavally River, contain gaps which must be bridged. This study aims to establish a rainfall-runoff model through artificial neural networks for filling the gaps into the flow data series of the hydrometric station of Ity on the watershed of Cavally River. A multi-layer perceptron of feed forwards with two entries (monthly average rain and evapotranspiration) and an exit (flows) was established with flow evapotranspiration data. Comparison of the criteria of performance of the various architectures of the neural network model showed that architecture 2-3-1 gives best results. This architecture provides Nash coefficients of 75.79% and correlation linear coefficient of 95.64% for the calibration and Nash coefficients of 73.32% and correlation linear coefficient of 98.33% for the validation. The correlations between simulated flows and observed flows are strong. The correlation coefficients are 83.89% and 83.08% respectively for the calibration and validation.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the m...The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source.Cross vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption.In this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total resistance.The network was trained and tested using real data collected from a surface mining operation.The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.展开更多
Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injec...Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.展开更多
Core power is a key parameter of nuclear reactor.Traditionally,the proportional-integralderivative(PID)controllers are used to control the core power.Fractional-order PID(FOPID)controller represents the cutting edge i...Core power is a key parameter of nuclear reactor.Traditionally,the proportional-integralderivative(PID)controllers are used to control the core power.Fractional-order PID(FOPID)controller represents the cutting edge in core power control research.In comparing with the integer-order models,fractional-order models describe the variation of core power more accurately,thus provide a comprehensive and realistic depiction for the power and state changes of reactor core.However,current fractional-order controllers cannot adjust their parameters dynamically to response the environmental changes or demands.In this paper,we aim at the stable control and dynamic responsiveness of core power.Based on the strong selflearning ability of artificial neural network(ANN),we propose a composite controller combining the ANN and FOPID controller.The FOPID controller is firstly designed and a back propagation neural network(BPNN)is then utilized to optimize the parameters of FOPID.It is shown by simulation that the composite controller enables the real-time parameter tuning via ANN and retains the advantage of FOPID controller.展开更多
The performance of concrete can be affected by many factors,including the material composition,environmental conditions,and construction methods,and it is challenging to predict the performance evolution accurately.Th...The performance of concrete can be affected by many factors,including the material composition,environmental conditions,and construction methods,and it is challenging to predict the performance evolution accurately.The rise of artificial intelligence provides a way to meet the above challenges.This article elaborates on research overview of artificial neural network(ANN)and its prediction for concrete strength,deformation,and durability.The focus is on the comparative analysis of the prediction accuracy for different types of neural networks.Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the practical engineering applications.To further improve the applicability of ANN in concrete,the model can consider the combination of multiple algorithms and the expansion of data samples.The review can provide new research ideas for development of concrete performance prediction.展开更多
This research study focuses on predicting ferrofluids’viscosity using machine learning models,artificial neural networks(ANNs),and random forests(RFs)incorporating key parameters;ferrofluid type,concentration of magn...This research study focuses on predicting ferrofluids’viscosity using machine learning models,artificial neural networks(ANNs),and random forests(RFs)incorporating key parameters;ferrofluid type,concentration of magnetic nanoparticles,temperature,and magnetic field intensity as inputs.A comprehensive database of 333 datasets sourced from various literatures was utilized for training and validating models.The ANN model demonstrated high accuracy,with root mean square error(RMSE)values below 0.033 and mean absolute percentage error(MAPE)not exceeding 3.01%,while the RF model achieved similar accuracy with RMSE under 0.052 and MAPE below 4.82%.Maximum deviations observed were 9.14%for ANN and 16.48%for RF,confirming that both models accurately learned the underlying patterns without overestimating viscosity.Additionally,the ANN model successfully captured intricate physical relationships between input parameters and viscosity when it was used to predict viscosity for random input data,confirming its ability to generalize beyond the training dataset.The RF model,however,showed limitations in extrapolating beyond the range of the training data.This research study demonstrates machine learning models’effectiveness in capturing intricate relationships governing the viscosity of ferrofluid for different types,paving the way for an improved understanding of ferrofluid’s viscosity behavior.展开更多
This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and sol...This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and solved using the finite volume method.The simulations examine the impact of key parameters such as heat source length and position,Peclet number,porosity,and heat generation/absorption on flow patterns,temperature distribution,concentration profiles,and microorganism rotation.Results indicate that extending the heat source length enhances convective currents and heat transfer efficiency,while optimizing the heat source position reduces entropy generation.Higher Peclet numbers amplify convective currents and microorganism distribution complexity.Variations in porosity and heat generation/absorption significantly influence flow dynamics.Additionally,the artificial neural network model reliably predicts the mean Nusselt and Sherwood numbers(Nu&Sh),demonstrating its effectiveness for such analyses.The simulation results reveal that increasing the heat source length significantly enhances heat transfer,as evidenced by a 15%increase in the mean Nusselt number.展开更多
Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols ...Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods.A notable application of AI is in the photoFenton degradation of organic compounds.Despite the high novelty and recent surge of interest in this area,a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking.This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks(ANN)in the photo-Fenton process,with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables.It examines the types and architectures of ANNs,input and output variables,and the efficiency of these networks.The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process.This review also discusses the benefits and drawbacks of using ANNs,emphasizing the need for further research to advance this promising area.展开更多
The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system...The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.展开更多
The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio...The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.展开更多
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
Solar chimneys are renewable energy systems designed to enhance natural ventilation,improving thermal comfort in buildings.As passive systems,solar chimneys contribute to energy efficiency in a sustainable and environ...Solar chimneys are renewable energy systems designed to enhance natural ventilation,improving thermal comfort in buildings.As passive systems,solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way.The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions,both of which are critical for optimal performance.This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys.Thedataset includes information from 2784 solar chimney configurations,which encompasses various factors such as chimney height,channel thickness,glass thickness,paint,wall material,measurement date,and orientation.The case study examines the four cardinal orientations and weather data from Mexico City,covering the period from 01 January to 31 December 2024.The main results indicate that the proposed artificial neural network models achieved higher coefficient of determination values(0.905-0.990)than the baseline method across performance indicators of the solar chimney system,demonstrating greater accuracy and improved generalization.The proposed approach highlights the potential of using artificial neural networks as a decision-making tool in the design stage of solar chimneys in sustainable architecture.展开更多
This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a I-shaped enclosure with one to five rotating cylinders.The dimensionless e...This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a I-shaped enclosure with one to five rotating cylinders.The dimensionless equations of mass,momentum,and energy are solved using the finite element method as implemented in the COMSOL Multiphysics 6.3 software in different rotating Reynolds numbers and cylinder geometries.An artificial Neural Network that is trained using Bayesian Regularization on data produced by the COMSOL is utilized to estimate the average Nusselt numbers.The analysis is conducted for a wide range of rotational Reynolds numbers(Re_(w)=0-100),with the fixed Prandtl number.Results are presented in terms of streamline patterns,isotherm contours,and Nusselt numbers to assess heat transfer behavior.Findings revealed that increasing the number of cylinders and optimizing their placement significantly enhances convective mixing and thermal transport.The ANN model accurately predicts the Nusselt numbers across all configurations with negligible errors.Among all configurations,the third arrangement in Scenario 5 exhibits the highest heat transfer rates,attributed to intensified vortex interaction and reduced thermal resistance.Artificial neural networks and finite element-based models will be of great value to the design of miniature and energy-efficient enclosures and electronics cooling mechanisms that make use of nanofluids.展开更多
Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infec...Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infection,hematoma,and bleeding,with incidence rates of 3–4%.Previous studies have examined individual risk factors separately,but integrated predictive models are lacking.We compared the predictive performance and interpretability of artificial neural network(ANN)and logistic regression models to evaluate their respective strengths in clinical risk assessment.Methods:This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device(CIED)implantation in Taiwan between 2017 and 2018.To address class imbalance and enhance model training,the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique(SMOTE).A total of 13 clinical risk factors were evaluated(e.g.,age,body mass index(BMI),platelet count,left ventricular ejection fraction(LVEF),prothrombin time/international normalized ratio(PT/INR),hemoglobin(Hb),comorbidities,and antithrombotic use).Results:The most influential risk factors identified by the ANN model were platelet count,PT/INR,LVEF,Hb,and age.In the logistic regression analysis,reduced LVEF,lower hemoglobin levels,prolonged PT/INR,and lower BMI were significantly associated with an increased risk of complications.ANN model achieved a higher area under the curve(AUC=0.952)compared to the logistic regression model(AUC=0.802),indicating superior predictive performance.Additionally,the overall model quality was also higher for the ANN model(0.93)than for logistic regression(0.76).Conclusions:This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors.These findings support the use of ANN-based models for individualized risk stratification,enhancing procedural safety,improving patient outcomes,and potentially reducing healthcare costs associated with postoperative complications.展开更多
The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy...The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti_(34)Cu17Ni_(31.36)Hf_(17.64)and Ti_(36)Cu_(18)Ni_(29.44)Hf_(16.56)alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions.展开更多
The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property.The mechanical properties of these workpieces depend mainly on solid-solution temperature,solid...The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property.The mechanical properties of these workpieces depend mainly on solid-solution temperature,solid-solution time,artificial aging temperature and artificial aging time.An artificial neural network（ANN） model with a back-propagation（BP） algorithm was used to predict mechanical properties of A357 alloy,and the effects of heat treatment processes on mechanical behavior of this alloy were studied.The results show that this BP model is able to predict the mechanical properties with a high accuracy.This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy.Isograms of ultimate tensile strength and elongation were drawn in the same picture,which are very helpful to understand the relationship among aging parameters,ultimate tensile strength and elongation.展开更多
Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was esta...Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was established.In the network model,the input parameters of the model are strain,logarithm strain rate and temperature while flow stress is the output parameter.Multilayer perceptron（MLP） architecture with back-propagation algorithm is utilized.The present study achieves a good performance of the artificial neural network（ANN） model,and the predicted results are in agreement with experimental values.A processing map of Ti40 alloy is obtained with the flow stress predicted by the trained neural network model.The processing map developed by ANN model can efficiently track dynamic recrystallization and flow localization regions of Ti40 alloy during deforming.Subsequently,the safe and instable domains of hot working of Ti40 alloy are identified and validated through microstructural investigations.展开更多
The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron ...The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.展开更多
文摘Water resources management is nowadays a significant stake for the world. However, missing or bad quality of the hydro-climatic historical data available of the studied area makes sometimes hydrological studies difficult. Generally, conceptual rain-flow models are designed to bring an appropriate answer with the correction of gaps and prediction of the flows. Historical hydro-climatic data of the Ity station, located on Cavally River, contain gaps which must be bridged. This study aims to establish a rainfall-runoff model through artificial neural networks for filling the gaps into the flow data series of the hydrometric station of Ity on the watershed of Cavally River. A multi-layer perceptron of feed forwards with two entries (monthly average rain and evapotranspiration) and an exit (flows) was established with flow evapotranspiration data. Comparison of the criteria of performance of the various architectures of the neural network model showed that architecture 2-3-1 gives best results. This architecture provides Nash coefficients of 75.79% and correlation linear coefficient of 95.64% for the calibration and Nash coefficients of 73.32% and correlation linear coefficient of 98.33% for the validation. The correlations between simulated flows and observed flows are strong. The correlation coefficients are 83.89% and 83.08% respectively for the calibration and validation.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
基金CRC Mining and The University of Queensland for their financial support for this study
文摘The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source.Cross vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption.In this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total resistance.The network was trained and tested using real data collected from a surface mining operation.The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.
文摘Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.
文摘Core power is a key parameter of nuclear reactor.Traditionally,the proportional-integralderivative(PID)controllers are used to control the core power.Fractional-order PID(FOPID)controller represents the cutting edge in core power control research.In comparing with the integer-order models,fractional-order models describe the variation of core power more accurately,thus provide a comprehensive and realistic depiction for the power and state changes of reactor core.However,current fractional-order controllers cannot adjust their parameters dynamically to response the environmental changes or demands.In this paper,we aim at the stable control and dynamic responsiveness of core power.Based on the strong selflearning ability of artificial neural network(ANN),we propose a composite controller combining the ANN and FOPID controller.The FOPID controller is firstly designed and a back propagation neural network(BPNN)is then utilized to optimize the parameters of FOPID.It is shown by simulation that the composite controller enables the real-time parameter tuning via ANN and retains the advantage of FOPID controller.
基金funded by the Ningbo Construction Research Project(Nos.2024-23,2024-20)the National Natural Science Foundation of China(No.52478281)the Ningbo Public Welfare Science and Technology Project(No.2024S077).
文摘The performance of concrete can be affected by many factors,including the material composition,environmental conditions,and construction methods,and it is challenging to predict the performance evolution accurately.The rise of artificial intelligence provides a way to meet the above challenges.This article elaborates on research overview of artificial neural network(ANN)and its prediction for concrete strength,deformation,and durability.The focus is on the comparative analysis of the prediction accuracy for different types of neural networks.Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the practical engineering applications.To further improve the applicability of ANN in concrete,the model can consider the combination of multiple algorithms and the expansion of data samples.The review can provide new research ideas for development of concrete performance prediction.
文摘This research study focuses on predicting ferrofluids’viscosity using machine learning models,artificial neural networks(ANNs),and random forests(RFs)incorporating key parameters;ferrofluid type,concentration of magnetic nanoparticles,temperature,and magnetic field intensity as inputs.A comprehensive database of 333 datasets sourced from various literatures was utilized for training and validating models.The ANN model demonstrated high accuracy,with root mean square error(RMSE)values below 0.033 and mean absolute percentage error(MAPE)not exceeding 3.01%,while the RF model achieved similar accuracy with RMSE under 0.052 and MAPE below 4.82%.Maximum deviations observed were 9.14%for ANN and 16.48%for RF,confirming that both models accurately learned the underlying patterns without overestimating viscosity.Additionally,the ANN model successfully captured intricate physical relationships between input parameters and viscosity when it was used to predict viscosity for random input data,confirming its ability to generalize beyond the training dataset.The RF model,however,showed limitations in extrapolating beyond the range of the training data.This research study demonstrates machine learning models’effectiveness in capturing intricate relationships governing the viscosity of ferrofluid for different types,paving the way for an improved understanding of ferrofluid’s viscosity behavior.
基金Dean ship of Scientific Research at King Khalid University,Abha,Saudi Arabia,for funding this work through the Research Group Project(Grant No.RGP.2/610/45)funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project(Grant No.PNURSP2024R102),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and solved using the finite volume method.The simulations examine the impact of key parameters such as heat source length and position,Peclet number,porosity,and heat generation/absorption on flow patterns,temperature distribution,concentration profiles,and microorganism rotation.Results indicate that extending the heat source length enhances convective currents and heat transfer efficiency,while optimizing the heat source position reduces entropy generation.Higher Peclet numbers amplify convective currents and microorganism distribution complexity.Variations in porosity and heat generation/absorption significantly influence flow dynamics.Additionally,the artificial neural network model reliably predicts the mean Nusselt and Sherwood numbers(Nu&Sh),demonstrating its effectiveness for such analyses.The simulation results reveal that increasing the heat source length significantly enhances heat transfer,as evidenced by a 15%increase in the mean Nusselt number.
基金financial support provided by the Valencian Regional Governement(Grant No.CIPROM2023/037)Davide Palma and Alessandra Bianco Prevot acknowledge support from the Project CH4.0 under the MUR program"Dipartimenti di Eccellenza 2023-2027"(Grant No.CUP:D13C22003520001).
文摘Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods.A notable application of AI is in the photoFenton degradation of organic compounds.Despite the high novelty and recent surge of interest in this area,a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking.This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks(ANN)in the photo-Fenton process,with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables.It examines the types and architectures of ANNs,input and output variables,and the efficiency of these networks.The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process.This review also discusses the benefits and drawbacks of using ANNs,emphasizing the need for further research to advance this promising area.
文摘The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12322203).
文摘The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.
基金funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.
文摘This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
文摘Solar chimneys are renewable energy systems designed to enhance natural ventilation,improving thermal comfort in buildings.As passive systems,solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way.The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions,both of which are critical for optimal performance.This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys.Thedataset includes information from 2784 solar chimney configurations,which encompasses various factors such as chimney height,channel thickness,glass thickness,paint,wall material,measurement date,and orientation.The case study examines the four cardinal orientations and weather data from Mexico City,covering the period from 01 January to 31 December 2024.The main results indicate that the proposed artificial neural network models achieved higher coefficient of determination values(0.905-0.990)than the baseline method across performance indicators of the solar chimney system,demonstrating greater accuracy and improved generalization.The proposed approach highlights the potential of using artificial neural networks as a decision-making tool in the design stage of solar chimneys in sustainable architecture.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a I-shaped enclosure with one to five rotating cylinders.The dimensionless equations of mass,momentum,and energy are solved using the finite element method as implemented in the COMSOL Multiphysics 6.3 software in different rotating Reynolds numbers and cylinder geometries.An artificial Neural Network that is trained using Bayesian Regularization on data produced by the COMSOL is utilized to estimate the average Nusselt numbers.The analysis is conducted for a wide range of rotational Reynolds numbers(Re_(w)=0-100),with the fixed Prandtl number.Results are presented in terms of streamline patterns,isotherm contours,and Nusselt numbers to assess heat transfer behavior.Findings revealed that increasing the number of cylinders and optimizing their placement significantly enhances convective mixing and thermal transport.The ANN model accurately predicts the Nusselt numbers across all configurations with negligible errors.Among all configurations,the third arrangement in Scenario 5 exhibits the highest heat transfer rates,attributed to intensified vortex interaction and reduced thermal resistance.Artificial neural networks and finite element-based models will be of great value to the design of miniature and energy-efficient enclosures and electronics cooling mechanisms that make use of nanofluids.
文摘Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infection,hematoma,and bleeding,with incidence rates of 3–4%.Previous studies have examined individual risk factors separately,but integrated predictive models are lacking.We compared the predictive performance and interpretability of artificial neural network(ANN)and logistic regression models to evaluate their respective strengths in clinical risk assessment.Methods:This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device(CIED)implantation in Taiwan between 2017 and 2018.To address class imbalance and enhance model training,the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique(SMOTE).A total of 13 clinical risk factors were evaluated(e.g.,age,body mass index(BMI),platelet count,left ventricular ejection fraction(LVEF),prothrombin time/international normalized ratio(PT/INR),hemoglobin(Hb),comorbidities,and antithrombotic use).Results:The most influential risk factors identified by the ANN model were platelet count,PT/INR,LVEF,Hb,and age.In the logistic regression analysis,reduced LVEF,lower hemoglobin levels,prolonged PT/INR,and lower BMI were significantly associated with an increased risk of complications.ANN model achieved a higher area under the curve(AUC=0.952)compared to the logistic regression model(AUC=0.802),indicating superior predictive performance.Additionally,the overall model quality was also higher for the ANN model(0.93)than for logistic regression(0.76).Conclusions:This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors.These findings support the use of ANN-based models for individualized risk stratification,enhancing procedural safety,improving patient outcomes,and potentially reducing healthcare costs associated with postoperative complications.
基金supported by the National Natural Science Foundation of China(No.51601019)the Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515010233)+1 种基金the Key Project of Shaanxi Province of Qinchuangyuan“Scientist and Engineer”Team Construction,China(No.2023KXJ-123)the Natural Science Foundation of Shaanxi Province,China(No.2024JC-YBMS-014).
文摘The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti_(34)Cu17Ni_(31.36)Hf_(17.64)and Ti_(36)Cu_(18)Ni_(29.44)Hf_(16.56)alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions.
文摘The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property.The mechanical properties of these workpieces depend mainly on solid-solution temperature,solid-solution time,artificial aging temperature and artificial aging time.An artificial neural network（ANN） model with a back-propagation（BP） algorithm was used to predict mechanical properties of A357 alloy,and the effects of heat treatment processes on mechanical behavior of this alloy were studied.The results show that this BP model is able to predict the mechanical properties with a high accuracy.This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy.Isograms of ultimate tensile strength and elongation were drawn in the same picture,which are very helpful to understand the relationship among aging parameters,ultimate tensile strength and elongation.
基金Project(2007CB613807)supported by the National Basic Research Program of ChinaProject(NCET-07-0696)supported by the New Century Excellent Talents in University,ChinaProject(35-TP-2009)supported by the Fund of the State Key Laboratory of Solidification Processing in Northwestern Polytechnical University,China
文摘Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was established.In the network model,the input parameters of the model are strain,logarithm strain rate and temperature while flow stress is the output parameter.Multilayer perceptron（MLP） architecture with back-propagation algorithm is utilized.The present study achieves a good performance of the artificial neural network（ANN） model,and the predicted results are in agreement with experimental values.A processing map of Ti40 alloy is obtained with the flow stress predicted by the trained neural network model.The processing map developed by ANN model can efficiently track dynamic recrystallization and flow localization regions of Ti40 alloy during deforming.Subsequently,the safe and instable domains of hot working of Ti40 alloy are identified and validated through microstructural investigations.
基金Project(51344004)supported by the National Natural Science Foundation of China
文摘The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.
