Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to...Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to be established by integrating robust predictions and an understanding of mechanisms underlying tree growth.Hybrid ecophysiological models,such as potentially useable light sum equation(PULSE)models,are useful tools requiring minimal input data that meet the requirements of SRF.PULSE models have been tested and calibrated for different evergreen conifers and broadleaves at both juvenile and mature stages of tree growth with coarse soil and climate data.Therefore,it is prudent to question:can adding detailed soil and climatic data reduce errors in this type of model?In addition,PULSE techniques have not been used to model deciduous species,which are a challenge for ecophysiological models due to their phenology.This study developed a PULSE model for a clonal Populus tomentosa plantation in northern China using detailed edaphic and climatic data.The results showed high precision and low bias in height(m)and basal area(m^(2)·ha^(-1))predictions.While detailed edaphoclimatic data produce highly precise predictions and a good mechanistic understanding,the study suggested that local climatic data could also be employed.The study showed that PULSE modelling in combination with coarse level of edaphic and local climate data resulted in reasonably precise tree growth prediction and minimal bias.展开更多
Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calc...Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calculation model is delineated for the optimization of process parameters via the particle swarm optimization algorithm.Subsequently,a hybrid strip width prediction model is proposed by effectively combining the respective advantages of the improved mechanism model and the data-driven model.In acknowledgment of prerequisite for positive error in strip width prediction,an adaptive width error compensation algorithm is proposed.Finally,comparative simulation experiments are designed on the actual rolling dataset after completing data cleaning and feature engineering.The experimental results show that the hybrid prediction model proposed has superior precision and robustness compared with the improved mechanism model and the other eight common data-driven models and satisfies the needs of practical applications.Moreover,the hybrid model can realize the complementary advantages of the mechanism model and the data-driven model,effectively alleviating the problems of difficult to improve the accuracy of the mechanism model and poor interpretability of the data-driven model,which bears significant practical implications for the research of strip width control.展开更多
In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD models.Many previous CAD segmentation methods have achieved impressive performance using singl...In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD models.Many previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point clouds.However,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological information.Hence,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation accuracy.To combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh models.For complex CAD models,model segmentation is crucial for model retrieval and reuse.In partial retrieval,it aims to segment a complex CAD model into several simple components.The first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh models.The second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD models.This combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object semantics.This study uses the Fusion 360 Gallery dataset.Experimental results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.展开更多
In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reve...In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy,personalising treatment plans,and optimising resource allocation to enhance clinical outcomes.Nonetheless,this domain faces unique challenges,such as irregular data collection,inconsistent data quality,and patient-specific structural variations.This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges.The strategy combines HPO-based optimal Density-Based Spatial Clustering of Applications with Noise for clustering patient exercise data,facilitating efficient anomaly identification.Subsequently,a stochastic method based on the Interquartile Range filters unreliable data points,ensuring that medical tools and professionals receive only the most pertinent and accurate information.The primary objective of this study is to equip healthcare pro-fessionals and researchers with a robust tool for managing extensive,high-dimensional clinical datasets,enabling effective isolation and removal of aberrant data points.Furthermore,a sophisticated regression model has been developed using Automated Machine Learning(AutoML)to assess the impact of the ensemble abnormal pattern detection approach.Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML.Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhance-ment in AutoML performance,with an average improvement of 0.041 in the R2 score,surpassing the effectiveness of traditional regression models.展开更多
Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit signifi...Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.展开更多
Alcohol oxidation is a widely used green chemical reaction.The reaction process produces flammable and explosive hydrogen,so the design of the reactor must meet stringent safety requirements.Based on the limited exper...Alcohol oxidation is a widely used green chemical reaction.The reaction process produces flammable and explosive hydrogen,so the design of the reactor must meet stringent safety requirements.Based on the limited experimental data,utilizing the traditional numerical method of computational fluid dynamics(CFD)to simulate the gas-liquid two-phase flow reactor can mitigate the risk of danger under varying working conditions.However,the calculation process is highly time-consuming.Therefore,by integrating process simulation,computational fluid dynamics,and deep learning technologies,an intelligent hybrid chemical model based on machine learning was proposed to expedite CFD calculations,enhance the prediction of flow fields,conversion rates,and concentrations inside the reactor,and offer insights for designing and optimizing the reactor for the alcohol oxidation system.The results show that the hybrid model based on the long and short-term memory neural network achieves 99.8%accuracy in conversion rate prediction and 99.9%accuracy in product concentration prediction.Through validation,the hybrid model is accelerated by about 360 times compared with instrumental analysis in conversion rate prediction and about 45 times compared with CFD calculation in concentration distribution prediction.This hybrid model can quickly predict the conversion rate and product concentration distribution in the gas-liquid two-phase flow reactor and provide a model reference for fast prediction and accurate control in the actual chemical production process.展开更多
Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricac...Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting.To address these difficulties,this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory(LSTM)network with a Single Candidate Optimizer(SCO)algorithm.In contrast to conventional techniques that rely on random parameter initialization,the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work with a single candidate solution,thereby substantially reducing the computational overhead compared to traditional population-based metaheuristics.The performance of the model was benchmarked against various classical and deep learning models across datasets from three geographically diverse sites,using multiple evaluation metrics.Experimental findings demonstrate that the SCO-optimized model enhances prediction accuracy by up to 12.5%over standard LSTM implementations.展开更多
Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping...Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping software.Following modern vibration standards and guidelines,the vibration velocity level in a building in each frequency band is expressed as the sum of a force density(source term),line source transfer mobility(propagation term)and building correction factor(receiver term).A hybrid approach is used that allows for a combination of experimental data and numerical predictions,providing increased flexibility and applicability.The train and track properties can be selected from a database or entered as numerical values.The user can select soil impedance and transfer functions from a database,pre-computed for a wide range of parameters with state-of-the-art models.An experimental database of force densities,transfer functions,free field vibration and input parameters is also provided.The building response is estimated by means of building correction factors.Assumptions within the modelling approach are made to reduce computation time but these can influence prediction accuracy;this is quantified for the case of a nominal intercity train running at different speeds on a ballasted track supported by homogeneous soil of varying stiffness.The paper focuses on the influence of these parameters on the compliance of the track–soil system and the free field response.We also demonstrate the use and discuss the validation of the vibration prediction tool for the case of a high-speed train running on a ballasted track in Lincent(Belgium).展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of ...A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of shielding gases with varying He-Ar ratios,and the coupling between arc plasma and laser-induced metal plume.The accuracy of the model is validated using a high-speed camera.The effects of varying He contents in the shielding gas on both the temperature and flow velocity of hybrid plasma,as well as the distribu-tion of laser-induced metal vapor mass,were investigated separately.The maximum temperature and size of arc plasma decrease as the He volume ratio increases,the arc distribution becomes more concentrated,and its flow velocity initially decreases and then sharply increases.At high helium content,both the flow velocity of hybrid plasma and metal vapor are high,the metal vapor is con-centrated on the right side of keyhole,and its flow appears chaotic.The flow state of arc plasma is most stable when the shielding gas consists of 50%He+50%Ar.展开更多
This work investigates water-based micropolar hybrid nanofluid(MHNF) flow on an elongating variable porous sheet.Nanoparticles of diamond and copper have been used in the water to boost its thermal conductivity. The m...This work investigates water-based micropolar hybrid nanofluid(MHNF) flow on an elongating variable porous sheet.Nanoparticles of diamond and copper have been used in the water to boost its thermal conductivity. The motion of the fluid is taken as two-dimensional with the impact of a magnetic field in the normal direction. The variable, permeable, and stretching nature of sheet's surface sets the fluid into motion. Thermal and mass diffusions are controlled through the use of the Cattaneo–Christov flux model. A dataset is generated using MATLAB bvp4c package solver and employed to train an artificial neural network(ANN) based on the Levenberg–Marquardt back-propagation(LMBP) algorithm. It has been observed as an outcome of this study that the modeled problem achieves peak performance at epochs 637, 112, 4848, and 344 using ANN-LMBP. The linear velocity of the fluid weakens with progression in variable porous and magnetic factors.With an augmentation in magnetic factor, the micro-rotational velocity profiles are augmented on the domain 0 ≤ η < 1.5 due to the support of micro-rotations by Lorentz forces close to the sheet's surface, while they are suppressed on the domain 1.5 ≤ η < 6.0 due to opposing micro-rotations away from the sheet's surface. Thermal distributions are augmented with an upsurge in thermophoresis, Brownian motion, magnetic, and radiation factors, while they are suppressed with an upsurge in thermal relaxation parameter. Concentration profiles increase with an expansion in thermophoresis factor and are suppressed with an intensification of Brownian motion factor and solute relaxation factor. The absolute errors(AEs) are evaluated for all the four scenarios that fall within the range 10^(-3)–10^(-8) and are associated with the corresponding ANN configuration that demonstrates a fine degree of accuracy.展开更多
Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environment...Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.展开更多
This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)syste...This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)system connected to the local grid.The study focuses on Dakhla,Morocco,a region with vast untapped renewable energy potential.By leveraging GIS,we are innovatively analyzing geographical and environmental factors that influence optimal site selection and system design.The incorporation of VR technologies offers an unprecedented level of realism and immersion,allowing stakeholders to virtually experience the project's impact and design in a dynamic,interactive environment.This novel methodology includes extensive data collection,advanced modeling,and simulations,ensuring that the hybrid system is precisely tailored to the unique climatic and environmental conditions of Dakhla.Our analysis reveals that the region possesses a photovoltaic solar potential of approximately2400 k Wh/m^(2) per year,with an average annual wind power density of about 434 W/m^(2) at an 80-meter hub height.Productivity simulations indicate that the 20 MW hybrid system could generate approximately 60 GWh of energy per year and 1369 GWh over its 25-year lifespan.To validate these findings,we employed the System Advisor Model(SAM)software and the Global Solar Photovoltaic Atlas platform.This comprehensive and interdisciplinary approach not only provides a robust assessment of the system's feasibility but also offers valuable insights into its potential socio-economic and environmental impact.展开更多
The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based ...The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.展开更多
Ethylene glycol(EG)plays a pivotal role as a primary raw material in the polyester industry,and the syngas-to-EG route has become a significant technical route in production.The carbon monoxide(CO)gas-phase catalytic ...Ethylene glycol(EG)plays a pivotal role as a primary raw material in the polyester industry,and the syngas-to-EG route has become a significant technical route in production.The carbon monoxide(CO)gas-phase catalytic coupling to synthesize dimethyl oxalate(DMO)is a crucial process in the syngas-to-EG route,whereby the composition of the reactor outlet exerts influence on the ultimate quality of the EG product and the energy consumption during the subsequent separation process.However,measuring product quality in real time or establishing accurate dynamic mechanism models is challenging.To effectively model the DMO synthesis process,this study proposes a hybrid modeling strategy that integrates process mechanisms and data-driven approaches.The CO gas-phase catalytic coupling mechanism model is developed based on intrinsic kinetics and material balance,while a long short-term memory(LSTM)neural network is employed to predict the macroscopic reaction rate by leveraging temporal relationships derived from archived measurements.The proposed model is trained semi-supervised to accommodate limited-label data scenarios,leveraging historical data.By integrating these predictions with the mechanism model,the hybrid modeling approach provides reliable and interpretable forecasts of mass fractions.Empirical investigations unequivocally validate the superiority of the proposed hybrid modeling approach over conventional data-driven models(DDMs)and other hybrid modeling techniques.展开更多
The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper ...The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.展开更多
This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative ...This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.展开更多
In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six dist...In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed,India.These models included the artificial neural network(ANN),k-nearest neighbour regression model(KNN),extreme gradient boosting(XGBoost)regression model,random forest regression model(RF),convolutional neural network(CNN),and CNN-RNN(convolutional recurrent neural network).The years 2003-2007 are classified as the calibration or training period,while the years 2008-2009 are classified as the validation or testing period for the span of time 2003 to 2009.The available rainfall,maximum and minimum temperatures,and discharge data were collected and utilized in the models.To compare the performance of the models,five criteria were employed:R^(2),NSE,MAE,RMSE,and PBIAS.The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods(training:R^(2) is 0.99,NSE is 0.99,MAE is 1.76,RMSE is 3.11,and PBIAS is1.45;testing:R^(2) is 0.97,NSE is 0.97,MAE is 2.05,RMSE is 3.60,and PBIAS is3.94).These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study.The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management,flood control,and environmental planning.展开更多
Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fibers.Basalt fibe...Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fibers.Basalt fiber strengthened vinyl ester matrix polymeric composite with filler addition of nano-and micro-sized silicon carbide(SiC)element spanning from 2 weight percent to 10 weight percent was studied for its mechanical and wear properties.The application of Artificial Neural Network(ANN)to correlate the filler addition composition for optimum mechanical properties is required due to the non-linear mechanical and tribological features of composites.The stuffing blend and composition of the composite are optimized using the hybrid model and Genetic Algorithm(GA)to maximize the mechanical and wear-resistant properties.The predicted and tested ANN–GA optimal values obtained for the composite combination had a tensile,flexural,impact resilience,hardness and wear properties of 202.93 MPa,501.67 MPa,3.460 J/s,43 HV and 0.196 g,respectively,for its optimum combination of filler and reinforcement.It can be noted that the nano-sized SiC filler particle enhances most of the properties of the composite which diversifies its applications.The predicted mechanical and wear values of the developed ANN–GA model were in closer agreement with the experimental values which validate the model.展开更多
基金The National Key Research and Development Program of China(Grant No.2021YFD2201203)the 5·5 Engineering Research&Innovation Team Project of Beijing Forestry University(No.BLRC2023C05)the Key Research and Development Program of Shandong Province(No.2021SFGC02050102)。
文摘Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to be established by integrating robust predictions and an understanding of mechanisms underlying tree growth.Hybrid ecophysiological models,such as potentially useable light sum equation(PULSE)models,are useful tools requiring minimal input data that meet the requirements of SRF.PULSE models have been tested and calibrated for different evergreen conifers and broadleaves at both juvenile and mature stages of tree growth with coarse soil and climate data.Therefore,it is prudent to question:can adding detailed soil and climatic data reduce errors in this type of model?In addition,PULSE techniques have not been used to model deciduous species,which are a challenge for ecophysiological models due to their phenology.This study developed a PULSE model for a clonal Populus tomentosa plantation in northern China using detailed edaphic and climatic data.The results showed high precision and low bias in height(m)and basal area(m^(2)·ha^(-1))predictions.While detailed edaphoclimatic data produce highly precise predictions and a good mechanistic understanding,the study suggested that local climatic data could also be employed.The study showed that PULSE modelling in combination with coarse level of edaphic and local climate data resulted in reasonably precise tree growth prediction and minimal bias.
基金supported by the National Natural Science Foundation of China(No.62273234)Key Research and Development Program of Shaanxi(Program No.2022GY-306)Technology Innovation Leading Program of Shaanxi(Program No.2022QFY01-16).
文摘Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calculation model is delineated for the optimization of process parameters via the particle swarm optimization algorithm.Subsequently,a hybrid strip width prediction model is proposed by effectively combining the respective advantages of the improved mechanism model and the data-driven model.In acknowledgment of prerequisite for positive error in strip width prediction,an adaptive width error compensation algorithm is proposed.Finally,comparative simulation experiments are designed on the actual rolling dataset after completing data cleaning and feature engineering.The experimental results show that the hybrid prediction model proposed has superior precision and robustness compared with the improved mechanism model and the other eight common data-driven models and satisfies the needs of practical applications.Moreover,the hybrid model can realize the complementary advantages of the mechanism model and the data-driven model,effectively alleviating the problems of difficult to improve the accuracy of the mechanism model and poor interpretability of the data-driven model,which bears significant practical implications for the research of strip width control.
基金Supported by the National Key Research and Development Program of China(2024YFB3311703)National Natural Science Foundation of China(61932003)Beijing Science and Technology Plan Project(Z221100006322003).
文摘In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD models.Many previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point clouds.However,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological information.Hence,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation accuracy.To combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh models.For complex CAD models,model segmentation is crucial for model retrieval and reuse.In partial retrieval,it aims to segment a complex CAD model into several simple components.The first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh models.The second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD models.This combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object semantics.This study uses the Fusion 360 Gallery dataset.Experimental results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.
文摘In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy,personalising treatment plans,and optimising resource allocation to enhance clinical outcomes.Nonetheless,this domain faces unique challenges,such as irregular data collection,inconsistent data quality,and patient-specific structural variations.This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges.The strategy combines HPO-based optimal Density-Based Spatial Clustering of Applications with Noise for clustering patient exercise data,facilitating efficient anomaly identification.Subsequently,a stochastic method based on the Interquartile Range filters unreliable data points,ensuring that medical tools and professionals receive only the most pertinent and accurate information.The primary objective of this study is to equip healthcare pro-fessionals and researchers with a robust tool for managing extensive,high-dimensional clinical datasets,enabling effective isolation and removal of aberrant data points.Furthermore,a sophisticated regression model has been developed using Automated Machine Learning(AutoML)to assess the impact of the ensemble abnormal pattern detection approach.Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML.Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhance-ment in AutoML performance,with an average improvement of 0.041 in the R2 score,surpassing the effectiveness of traditional regression models.
基金Supported by the Laoshan Laboratory(No.LSKJ 202202404)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42000000)+1 种基金the National Natural Science Foundation of China(NSFC)(No.42030410)the Startup Foundation for Introducing Talent of NUIST,and the Jiangsu Innovation Research Group(No.JSSCTD 202346)。
文摘Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.
基金the support from the National Natural Science Foundation of China(22478429)the Special Project Fund of Taishan-Scholars(tsqn202408101)+3 种基金the Natural Science Foundation of Shandong Province(ZR2023YQ009)CNPC Innovation Found(2024DQ02-0504)Fundamental Research Funds for the Central Universities,Ocean University of China(202364004)the State Key Laboratory of Heavy Oil Processing(SKLHOP202403003)。
文摘Alcohol oxidation is a widely used green chemical reaction.The reaction process produces flammable and explosive hydrogen,so the design of the reactor must meet stringent safety requirements.Based on the limited experimental data,utilizing the traditional numerical method of computational fluid dynamics(CFD)to simulate the gas-liquid two-phase flow reactor can mitigate the risk of danger under varying working conditions.However,the calculation process is highly time-consuming.Therefore,by integrating process simulation,computational fluid dynamics,and deep learning technologies,an intelligent hybrid chemical model based on machine learning was proposed to expedite CFD calculations,enhance the prediction of flow fields,conversion rates,and concentrations inside the reactor,and offer insights for designing and optimizing the reactor for the alcohol oxidation system.The results show that the hybrid model based on the long and short-term memory neural network achieves 99.8%accuracy in conversion rate prediction and 99.9%accuracy in product concentration prediction.Through validation,the hybrid model is accelerated by about 360 times compared with instrumental analysis in conversion rate prediction and about 45 times compared with CFD calculation in concentration distribution prediction.This hybrid model can quickly predict the conversion rate and product concentration distribution in the gas-liquid two-phase flow reactor and provide a model reference for fast prediction and accurate control in the actual chemical production process.
文摘Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting.To address these difficulties,this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory(LSTM)network with a Single Candidate Optimizer(SCO)algorithm.In contrast to conventional techniques that rely on random parameter initialization,the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work with a single candidate solution,thereby substantially reducing the computational overhead compared to traditional population-based metaheuristics.The performance of the model was benchmarked against various classical and deep learning models across datasets from three geographically diverse sites,using multiple evaluation metrics.Experimental findings demonstrate that the SCO-optimized model enhances prediction accuracy by up to 12.5%over standard LSTM implementations.
基金the project SILVARSTAR funded from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 101015442。
文摘Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping software.Following modern vibration standards and guidelines,the vibration velocity level in a building in each frequency band is expressed as the sum of a force density(source term),line source transfer mobility(propagation term)and building correction factor(receiver term).A hybrid approach is used that allows for a combination of experimental data and numerical predictions,providing increased flexibility and applicability.The train and track properties can be selected from a database or entered as numerical values.The user can select soil impedance and transfer functions from a database,pre-computed for a wide range of parameters with state-of-the-art models.An experimental database of force densities,transfer functions,free field vibration and input parameters is also provided.The building response is estimated by means of building correction factors.Assumptions within the modelling approach are made to reduce computation time but these can influence prediction accuracy;this is quantified for the case of a nominal intercity train running at different speeds on a ballasted track supported by homogeneous soil of varying stiffness.The paper focuses on the influence of these parameters on the compliance of the track–soil system and the free field response.We also demonstrate the use and discuss the validation of the vibration prediction tool for the case of a high-speed train running on a ballasted track in Lincent(Belgium).
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金supported by the National Natural Science Foundation of China(Grant No.52375340,51975263,52405366).
文摘A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of shielding gases with varying He-Ar ratios,and the coupling between arc plasma and laser-induced metal plume.The accuracy of the model is validated using a high-speed camera.The effects of varying He contents in the shielding gas on both the temperature and flow velocity of hybrid plasma,as well as the distribu-tion of laser-induced metal vapor mass,were investigated separately.The maximum temperature and size of arc plasma decrease as the He volume ratio increases,the arc distribution becomes more concentrated,and its flow velocity initially decreases and then sharply increases.At high helium content,both the flow velocity of hybrid plasma and metal vapor are high,the metal vapor is con-centrated on the right side of keyhole,and its flow appears chaotic.The flow state of arc plasma is most stable when the shielding gas consists of 50%He+50%Ar.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through large Research Group Project (Grant No. RGP2/198/45)Project supported by Prince Sattam bin Abdulaziz University (Grant No. PSAU/2025/R/1446)。
文摘This work investigates water-based micropolar hybrid nanofluid(MHNF) flow on an elongating variable porous sheet.Nanoparticles of diamond and copper have been used in the water to boost its thermal conductivity. The motion of the fluid is taken as two-dimensional with the impact of a magnetic field in the normal direction. The variable, permeable, and stretching nature of sheet's surface sets the fluid into motion. Thermal and mass diffusions are controlled through the use of the Cattaneo–Christov flux model. A dataset is generated using MATLAB bvp4c package solver and employed to train an artificial neural network(ANN) based on the Levenberg–Marquardt back-propagation(LMBP) algorithm. It has been observed as an outcome of this study that the modeled problem achieves peak performance at epochs 637, 112, 4848, and 344 using ANN-LMBP. The linear velocity of the fluid weakens with progression in variable porous and magnetic factors.With an augmentation in magnetic factor, the micro-rotational velocity profiles are augmented on the domain 0 ≤ η < 1.5 due to the support of micro-rotations by Lorentz forces close to the sheet's surface, while they are suppressed on the domain 1.5 ≤ η < 6.0 due to opposing micro-rotations away from the sheet's surface. Thermal distributions are augmented with an upsurge in thermophoresis, Brownian motion, magnetic, and radiation factors, while they are suppressed with an upsurge in thermal relaxation parameter. Concentration profiles increase with an expansion in thermophoresis factor and are suppressed with an intensification of Brownian motion factor and solute relaxation factor. The absolute errors(AEs) are evaluated for all the four scenarios that fall within the range 10^(-3)–10^(-8) and are associated with the corresponding ANN configuration that demonstrates a fine degree of accuracy.
基金supported by the Startup Grant(PG18929)awarded to F.Shokoohi.
文摘Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.
文摘This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)system connected to the local grid.The study focuses on Dakhla,Morocco,a region with vast untapped renewable energy potential.By leveraging GIS,we are innovatively analyzing geographical and environmental factors that influence optimal site selection and system design.The incorporation of VR technologies offers an unprecedented level of realism and immersion,allowing stakeholders to virtually experience the project's impact and design in a dynamic,interactive environment.This novel methodology includes extensive data collection,advanced modeling,and simulations,ensuring that the hybrid system is precisely tailored to the unique climatic and environmental conditions of Dakhla.Our analysis reveals that the region possesses a photovoltaic solar potential of approximately2400 k Wh/m^(2) per year,with an average annual wind power density of about 434 W/m^(2) at an 80-meter hub height.Productivity simulations indicate that the 20 MW hybrid system could generate approximately 60 GWh of energy per year and 1369 GWh over its 25-year lifespan.To validate these findings,we employed the System Advisor Model(SAM)software and the Global Solar Photovoltaic Atlas platform.This comprehensive and interdisciplinary approach not only provides a robust assessment of the system's feasibility but also offers valuable insights into its potential socio-economic and environmental impact.
基金financially supported by the National Natural Science Foundation of China (Nos.51974023 and52374321)the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,China (No.41620007)。
文摘The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.
基金supported in part by the National Key Research and Development Program of China(2022YFB3305300)the National Natural Science Foundation of China(62173178).
文摘Ethylene glycol(EG)plays a pivotal role as a primary raw material in the polyester industry,and the syngas-to-EG route has become a significant technical route in production.The carbon monoxide(CO)gas-phase catalytic coupling to synthesize dimethyl oxalate(DMO)is a crucial process in the syngas-to-EG route,whereby the composition of the reactor outlet exerts influence on the ultimate quality of the EG product and the energy consumption during the subsequent separation process.However,measuring product quality in real time or establishing accurate dynamic mechanism models is challenging.To effectively model the DMO synthesis process,this study proposes a hybrid modeling strategy that integrates process mechanisms and data-driven approaches.The CO gas-phase catalytic coupling mechanism model is developed based on intrinsic kinetics and material balance,while a long short-term memory(LSTM)neural network is employed to predict the macroscopic reaction rate by leveraging temporal relationships derived from archived measurements.The proposed model is trained semi-supervised to accommodate limited-label data scenarios,leveraging historical data.By integrating these predictions with the mechanism model,the hybrid modeling approach provides reliable and interpretable forecasts of mass fractions.Empirical investigations unequivocally validate the superiority of the proposed hybrid modeling approach over conventional data-driven models(DDMs)and other hybrid modeling techniques.
文摘The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.
基金supported by the National Natural Science Foundation of China (62073303,61673356)Hubei Provincial Natural Science Foundation of China (2015CFA010)the 111 Project(B17040)。
文摘This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.
文摘In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed,India.These models included the artificial neural network(ANN),k-nearest neighbour regression model(KNN),extreme gradient boosting(XGBoost)regression model,random forest regression model(RF),convolutional neural network(CNN),and CNN-RNN(convolutional recurrent neural network).The years 2003-2007 are classified as the calibration or training period,while the years 2008-2009 are classified as the validation or testing period for the span of time 2003 to 2009.The available rainfall,maximum and minimum temperatures,and discharge data were collected and utilized in the models.To compare the performance of the models,five criteria were employed:R^(2),NSE,MAE,RMSE,and PBIAS.The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods(training:R^(2) is 0.99,NSE is 0.99,MAE is 1.76,RMSE is 3.11,and PBIAS is1.45;testing:R^(2) is 0.97,NSE is 0.97,MAE is 2.05,RMSE is 3.60,and PBIAS is3.94).These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study.The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management,flood control,and environmental planning.
文摘Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fibers.Basalt fiber strengthened vinyl ester matrix polymeric composite with filler addition of nano-and micro-sized silicon carbide(SiC)element spanning from 2 weight percent to 10 weight percent was studied for its mechanical and wear properties.The application of Artificial Neural Network(ANN)to correlate the filler addition composition for optimum mechanical properties is required due to the non-linear mechanical and tribological features of composites.The stuffing blend and composition of the composite are optimized using the hybrid model and Genetic Algorithm(GA)to maximize the mechanical and wear-resistant properties.The predicted and tested ANN–GA optimal values obtained for the composite combination had a tensile,flexural,impact resilience,hardness and wear properties of 202.93 MPa,501.67 MPa,3.460 J/s,43 HV and 0.196 g,respectively,for its optimum combination of filler and reinforcement.It can be noted that the nano-sized SiC filler particle enhances most of the properties of the composite which diversifies its applications.The predicted mechanical and wear values of the developed ANN–GA model were in closer agreement with the experimental values which validate the model.
