Optimal parameterization of specified segment on the algebraic curves is a hot issue in CAGD and CG. Take the optimal approximation of arc-length parameterization as the criterion of optimal parameterization, and the ...Optimal parameterization of specified segment on the algebraic curves is a hot issue in CAGD and CG. Take the optimal approximation of arc-length parameterization as the criterion of optimal parameterization, and the optimal or close to optimal rational parameterization formula of any specified segment on the conic curves is obtained. The new method proposed in this paper has ad- vantage in quantity of calculation and has strong self-adaptability. Finally, a experimental comparison of the results obtained by this method and by the traditional parametric algorithm is conducted.展开更多
In this paper, we rewrote the equation of algebraic curve segmentswith the geometric informationonboth ends. The optimal or nearly optimal rationalparametric equation is determinedbythe principle that parametricspeeds...In this paper, we rewrote the equation of algebraic curve segmentswith the geometric informationonboth ends. The optimal or nearly optimal rationalparametric equation is determinedbythe principle that parametricspeedsat both endsareequal. Comparing withotherliteratures, the methodofthis paper has advantage in efficiency andiseasy to realize. The equation of optimal rational parameterization can be obtained directly by the information of both ends. Large numbers ofexperimental data show that our method hasbeen given withmore self-adaptability and accuracy than that ofotherliteratures, and if the parametricspeedat any end reaches its maximum or minimum value, the parameterization is optimal; otherwise itis close tooptimal rational parameterization.展开更多
In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint me...In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.展开更多
It is a challenging issue to obtain the minimum amplitude control for linear systems subject to amplitudebounded disturbances.The difficulty is how to accurately give the quantitative relationship between the system H...It is a challenging issue to obtain the minimum amplitude control for linear systems subject to amplitudebounded disturbances.The difficulty is how to accurately give the quantitative relationship between the system H∞norm and control parameters.An optimal-Lyapunov-function-based controller design concept is proposed,and a minimum amplitude control scheme is presented under amplitude-bounded disturbances.Firstly,the optimal Lyapunov function is proposed by analyzing the geometric characteristics of the system H∞norm,and the necessary and sufficient condition of the optimal Lyapunov function parameter matrix is given.Secondly,the optimal Lyapunov function parameter matrix is constructed in the parameterized matrix equation,and the accurate quantitative relationship between the system H∞norm and control parameters is given.Finally,the control parameter optimization method is proposed according to the quantitative relationship between the system H∞norm and control parameters.Unlike robust optimization control methods,the presented minimum amplitude control scheme avoids the improper selection of the Lyapunov function in the controller design,and provides a novel way to design the minimum amplitude control under the given control accuracy.A buck converter example is given to illustrate the effectiveness and practicability of the presented scheme.展开更多
The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution l...The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.展开更多
In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory mode...In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory model tests with FLAC3D simulations to evaluate the stabilizing role of prestressed anchor cables and to establish an energy-balance framework for support optimization.Comparative model tests of existing and enlarged tunnel sections,with and without anchors,show that reinforcement increases load-carrying capacity,reduces displacement,and confines damage to more localized zones.The numerical simulations reproduce displacement fields,shear-strain localization,and plastic-zone evolution with good agreement against the experimental observations.The energy framework is implemented in the in-situ simulations by quantifying unloading-related energy release in the rock mass and reinforcement work contributed by the anchors,and by introducing an energy release–reinforcement ratio as a stability indicator.Parametric analyses indicate that anchor length,spacing,and prestress influence stability in a nonlinear manner,with diminishing returns once reinforcement extends beyond the mechanically dominant deformation zone.An efficient parameter window is identified that improves deformation and yielding control while avoiding unnecessary reinforcement.The results provide an energy-consistent and design-oriented basis for prestressed anchorage selection in large-span tunnel expansion.展开更多
Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drill...Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drilling extension limits,underground target distribution and trajectory collision risks,a model of platform location-wellbore trajectory collaborative optimization for a complex-structure well factory is developed.A hybrid heuristic algorithm is proposed by combining an improved sparrow search algorithm(ISSA)for optimizing platform parameters in the outer layer and a directed artificial bee colony algorithm(DABC)for optimizing trajectory parameters in the inner layer.The alternating iteration of ISSA-DABC facilitates the resolution of the collaborative optimization problem.The ISSA-DABC provides an effective solution to the platform-trajectory collaborative optimization problem for complex-structure well factories and overcomes the tendency of the traditional platform-trajectory stepwise optimization workflow to become trapped in local optima and yield inconsistent designs.The ISSA-DABC has a strong global search capability,fast convergence and good robustness,and can simultaneously satisfy multiple engineering constraints on drilling footage,trajectory complexity and collision risk,and enables automated,workflow-wide generation of constraint-compliant,near-globally optimal platform-trajectory configurations.Field applications further demonstrate that ISSA-DABC significantly reduces the objective function value and collision risk,yielding more rational platform layouts and well factory design parameters.展开更多
Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the opt...Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the optimal collaborative control parameters that support rapid drilling is crucial for improving the combined performance.This study used average drilling speed,average torque,and total specificenergy for quantitative analysis to characterize the efficiencyand economy of combined rock breaking.Given the advantage of the response surface methodology in providing high-precision predictions with limited experimental data,regression models of the average drilling speed,average torque,and total specificenergy were established.The results showed that as the laser power and irradiation time increased,the average drilling speed firstincreased rapidly and then leveled off,while the average torque decreased sharply before decelerating.The total specificenergy initially decreased and then increased,with the combined drilling outperforming conventional mechanical drilling within specific parameter ranges.As the weight on bit increased,both the average torque and total specificenergy first decreased and then increased.With rising rotating speed,the average torque exhibited a trend of initial increase,then decrease,and finalincrease,whereas the total specificenergy increased slowly at firstand then sharply.Both parameters exhibited optimal values at which the average torque and total specific energy remained at minimal levels.For granite combined drilling,the optimal performance was achieved at a laser power of 3000 W,irradiation time of 31 s,the weight on bit of 2.4 kN,and the rotating speed of 97 r/min.展开更多
The adjustable parameters in the popular conductor-like screening model for real solvents(COSMO-RS)within the Amsterdam density functional(ADF)framework have been re-optimized to fit for the systems containing ionic l...The adjustable parameters in the popular conductor-like screening model for real solvents(COSMO-RS)within the Amsterdam density functional(ADF)framework have been re-optimized to fit for the systems containing ionic liquids(ILs).To get the optimal values of misfit energy constant a^0,hydrogen bond coefficient c_(hb)and effective contact surface area of a segment a_(eff),2283 activity coefficient data points at infinite dilution and 1433 CO_2 solubility data points exhaustively collected from references were used as training set.The average relative deviations(ARDs)of activity coefficients at infinite dilution and CO_2 solubility between experimental data and predicted values are 32.22%and17.61%,respectively,both of which are significantly lower than the original COSMO-RS versions.Predictions for other activity coefficients of solutes in ILs,solubility data of CO_2 in pure ILs and the binary mixtures of ILs at either high or low temperatures,and vapor–liquid equilibrium(VLE)for binary systems involving ILs have also been performed to demonstrate the validity of the parameterization of COSMO-RS model for ILs.The results showed that the predicted results by COSMO-RS model with the new optimized parameters are in much better agreement with experimental data than those by the original versions over a wide temperature and pressure range.The COSMO-RS model for ILs presented in this work improves the prediction accuracy of thermodynamic properties for the systems containing ILs,which is always highly desirable for general chemical engineers.展开更多
In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental ...In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.展开更多
An extension of the conditional nonlinear optimal parameter perturbation （CNOP-P） method is applied to the parameter optimization of the Common Land Model （CoLM） for the North China Plain with the differential evo...An extension of the conditional nonlinear optimal parameter perturbation （CNOP-P） method is applied to the parameter optimization of the Common Land Model （CoLM） for the North China Plain with the differential evolution （DE） method. Using National Meteorological Center （NMC） Reanalysis 6-hourly surface flux data and National Center for Environmental Prediction/Department of Energy （NCEP/DOE） Atmospheric Model Intercomparison Project II （AMIP-II） 6-hourly Reanalysis Gaussian Grid data, two experiments （I and II） were designed to investigate the impact of the percentages of sand and clay in the shallow soil in CoLM on its ability to simulate shallow soil moisture. A third experiment （III） was designed to study the shallow soil moisture and latent heat flux simultaneously. In all the three experiments, after the optimization stage, the percentages of sand and clay of the shallow soil were used to predict the shallow soil moisture in the following month. The results show that the optimal parameters can enable CoLM to better simulate shallow soil moisture, with the simulation results of CoLM after the double-parameter optimal ex- periment being better than the single-parameter optimal experiment in the optimization slot. Purthermore, the optimal parameters were able to significantly improve the prediction results of CoLM at the prediction stage. In addition, whether or not the atmospheric forcing and observational data are accurate can seriously affect the results of optimization, and the more accurate the data are, the more significant the results of optimization may be.展开更多
Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm...Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm directly affects the validity of the clustering results, which is still an open issue. For this purpose, this paper proposes a fuzzy CLOPE algorithm, and presents a method for the optimal parameter choice by defining a modified partition fuzzy degree as a clustering validity function. The experimental results with real data set illustrate the effectiveness of the proposed fuzzy CLOPE algorithm and optimal parameter choice method based on the modified partition fuzzy degree.展开更多
In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously decreasing.In such scenario,Internet of Things(IoT)network which is comprised of a set o...In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously decreasing.In such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary applications.But network security poses a serious challenge to UAV networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the UAV networks.Classical IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data traffic.In order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection problem.In this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for UAV networks.In addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion detection.For parameter optimization of DRL technique,BWO algorithm is applied.It helps in improving the intrusion detection performance of UAV networks.An extensive set of experimental analysis was performed to highlight the supremacy of the proposed model.From the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.展开更多
How to accelerate the convergence speed and avoid computing the inversion of a Jacobian matrix is important in the solution of nonlinear algebraic equations(NAEs).This paper develops an approach with a splitting-linea...How to accelerate the convergence speed and avoid computing the inversion of a Jacobian matrix is important in the solution of nonlinear algebraic equations(NAEs).This paper develops an approach with a splitting-linearizing technique based on the nonlinear term to reduce the effect of the nonlinear terms.We decompose the nonlinear terms in the NAEs through a splitting parameter and then linearize the NAEs around the values at the previous step to a linear system.Through the maximal orthogonal projection concept,to minimize a merit function within a selected interval of splitting parameters,the optimal parameters can be quickly determined.In each step,a linear system is solved by the Gaussian elimination method,and the whole iteration procedure is convergent very fast.Several numerical tests show the high performance of the optimal split-linearization iterative method(OSLIM).展开更多
Spectrum sensing is an important part of cognitive radio systems to find spectrum hole for transmission which enables cognitive radio systems coexist with the authorized radio systems without harmful interference.In t...Spectrum sensing is an important part of cognitive radio systems to find spectrum hole for transmission which enables cognitive radio systems coexist with the authorized radio systems without harmful interference.In this paper,an improved cyclostationary feature detection method is proposed to reduce computational complexity without loss of good performance based on the optimal parameter selection strategy for choosing detection parameters of cyclic frequency and lag.Taking binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals as examples,the theoretical analyses are presented for choosing the optimal parameters.Simulation results are given to certify the correctness of the proposed parameter selection strategy and show the performance of the proposed method.展开更多
The aim of the study is to determine the optimal structural parameters for a plastic centrifugal pump inducer within the framework of an orthogonal experimental method.For this purpose,a numerical study of the related...The aim of the study is to determine the optimal structural parameters for a plastic centrifugal pump inducer within the framework of an orthogonal experimental method.For this purpose,a numerical study of the related flow field is performed using CFX.The shaft power and the head of the pump are taken as the evaluation indicators.Accordingly,the examined variables are the thickness(S),the blade cascade degree(t),the blade rim angle(β1),the blade hub angle(β2)and the hub length(L).The impact of each structural parameter on each evaluation index is examined and special attention is paid to the following combinations:S2 mm,t 2,β1235°,β2360°and L 140 mm(corresponding to a maximum head of 98.15 m);S 5 mm,t 1.6,β1252°,β2350°and L 140 mm(corresponding to a minimum shaft power of 63.06 KW).Moreover,using least squares and fish swarm algorithms,the pump shaft power and head are further optimized,yielding the following optimal combination:S 5 mm,t 1.9,β1252°,β2360°and L 145 mm(corresponding to the maximum head of 91.90 m,and a minimum shaft power of 64.83 KW).展开更多
A new mothod was presented to find the optimal location and parameter setting of Thyristor Controlled Series Compensator (TCSC) to maxmize the transfer capability.Firstly the sensitivity of the transfer capability wit...A new mothod was presented to find the optimal location and parameter setting of Thyristor Controlled Series Compensator (TCSC) to maxmize the transfer capability.Firstly the sensitivity of the transfer capability with respect was described to the line's reactance was described to find the more sensitive lines for installing TCSC,however,the line which has the most sesitivity value is always not the best line for installing TCSC.For solving this problem,the more sensitive m lines were selected as the alternative line group of installing TCSC,and then modified particle swarm optimization (MPSO) was used to find out the optimal location and the optimal parameter settings of TCSC.Particle swarm optimization (PSO) algorithm can results premature convergence.For solving this problem,population entropy and cellular automata were introduced to it.Simulation results of IEEE 30-bus system proved the effectiveness of the method and its application values.展开更多
By using the weight function method,the matching parameters of the half discrete Hilbert type multiple integral inequality with a non-homogeneous kernel K(n,||x||ρ,m)=G(nλ1||x||ρmλ,2)are discussed,some equivalent ...By using the weight function method,the matching parameters of the half discrete Hilbert type multiple integral inequality with a non-homogeneous kernel K(n,||x||ρ,m)=G(nλ1||x||ρmλ,2)are discussed,some equivalent conditions of the optimal matching parameter are established,and the expression of the optimal constant factor is obtained.Finally,their applications in operator theory are considered.展开更多
Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented...Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.展开更多
The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources.This paper introduces novel dual-backup controllers utilizing advanced fractional order ...The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources.This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative(FOPID)controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration.To improve load frequency control,the proposed controllers are applied to a two-area interconnectedmicrogrid system incorporating diverse energy sources,such as wind turbines,photovoltaic cells,diesel generators,and various storage technologies.A novelmeta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers.The efficacy of the advanced FOPID controllers is demonstrated through comparative analyses against traditional proportional integral derivative(PID)and FOPID controllers,showcasing superior performance inmanaging systemfluctuations.The optimization algorithm is also evaluated against other artificial intelligent methods for parameter optimization,affirming the proposed solution’s efficiency.The robustness of the intelligent controllers against system uncertainties is further validated under extensive power disturbances,proving their capability to maintain grid stability.The dual-controller configuration ensures redundancy,allowing them to operate as mutual backups,enhancing system reliability.This research underlines the importance of sophisticated control strategies for future-proofing microgrid operations against the backdrop of evolving energy landscapes.展开更多
文摘Optimal parameterization of specified segment on the algebraic curves is a hot issue in CAGD and CG. Take the optimal approximation of arc-length parameterization as the criterion of optimal parameterization, and the optimal or close to optimal rational parameterization formula of any specified segment on the conic curves is obtained. The new method proposed in this paper has ad- vantage in quantity of calculation and has strong self-adaptability. Finally, a experimental comparison of the results obtained by this method and by the traditional parametric algorithm is conducted.
文摘In this paper, we rewrote the equation of algebraic curve segmentswith the geometric informationonboth ends. The optimal or nearly optimal rationalparametric equation is determinedbythe principle that parametricspeedsat both endsareequal. Comparing withotherliteratures, the methodofthis paper has advantage in efficiency andiseasy to realize. The equation of optimal rational parameterization can be obtained directly by the information of both ends. Large numbers ofexperimental data show that our method hasbeen given withmore self-adaptability and accuracy than that ofotherliteratures, and if the parametricspeedat any end reaches its maximum or minimum value, the parameterization is optimal; otherwise itis close tooptimal rational parameterization.
基金Application investigation of conditional nonlinear optimal perturbation in typhoon adaptive observation (40830955)
文摘In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.
基金supported in part by the National Natural Science Foundation of China(62373089).
文摘It is a challenging issue to obtain the minimum amplitude control for linear systems subject to amplitudebounded disturbances.The difficulty is how to accurately give the quantitative relationship between the system H∞norm and control parameters.An optimal-Lyapunov-function-based controller design concept is proposed,and a minimum amplitude control scheme is presented under amplitude-bounded disturbances.Firstly,the optimal Lyapunov function is proposed by analyzing the geometric characteristics of the system H∞norm,and the necessary and sufficient condition of the optimal Lyapunov function parameter matrix is given.Secondly,the optimal Lyapunov function parameter matrix is constructed in the parameterized matrix equation,and the accurate quantitative relationship between the system H∞norm and control parameters is given.Finally,the control parameter optimization method is proposed according to the quantitative relationship between the system H∞norm and control parameters.Unlike robust optimization control methods,the presented minimum amplitude control scheme avoids the improper selection of the Lyapunov function in the controller design,and provides a novel way to design the minimum amplitude control under the given control accuracy.A buck converter example is given to illustrate the effectiveness and practicability of the presented scheme.
文摘The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.
基金funded by the National Key R&D Program of China,China(No.2024YFF0507903)the National Key Research and Development Program of China(Grant No.2024YFF0507904)the National Natural Science Foundation of China,China(Grant No.52379114).
文摘In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory model tests with FLAC3D simulations to evaluate the stabilizing role of prestressed anchor cables and to establish an energy-balance framework for support optimization.Comparative model tests of existing and enlarged tunnel sections,with and without anchors,show that reinforcement increases load-carrying capacity,reduces displacement,and confines damage to more localized zones.The numerical simulations reproduce displacement fields,shear-strain localization,and plastic-zone evolution with good agreement against the experimental observations.The energy framework is implemented in the in-situ simulations by quantifying unloading-related energy release in the rock mass and reinforcement work contributed by the anchors,and by introducing an energy release–reinforcement ratio as a stability indicator.Parametric analyses indicate that anchor length,spacing,and prestress influence stability in a nonlinear manner,with diminishing returns once reinforcement extends beyond the mechanically dominant deformation zone.An efficient parameter window is identified that improves deformation and yielding control while avoiding unnecessary reinforcement.The results provide an energy-consistent and design-oriented basis for prestressed anchorage selection in large-span tunnel expansion.
基金Supported by Key Program of Natural Science Foundation of China(52234002)Major Program Project of the National Natural Science Foundation of China(52394255)。
文摘Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drilling extension limits,underground target distribution and trajectory collision risks,a model of platform location-wellbore trajectory collaborative optimization for a complex-structure well factory is developed.A hybrid heuristic algorithm is proposed by combining an improved sparrow search algorithm(ISSA)for optimizing platform parameters in the outer layer and a directed artificial bee colony algorithm(DABC)for optimizing trajectory parameters in the inner layer.The alternating iteration of ISSA-DABC facilitates the resolution of the collaborative optimization problem.The ISSA-DABC provides an effective solution to the platform-trajectory collaborative optimization problem for complex-structure well factories and overcomes the tendency of the traditional platform-trajectory stepwise optimization workflow to become trapped in local optima and yield inconsistent designs.The ISSA-DABC has a strong global search capability,fast convergence and good robustness,and can simultaneously satisfy multiple engineering constraints on drilling footage,trajectory complexity and collision risk,and enables automated,workflow-wide generation of constraint-compliant,near-globally optimal platform-trajectory configurations.Field applications further demonstrate that ISSA-DABC significantly reduces the objective function value and collision risk,yielding more rational platform layouts and well factory design parameters.
基金funded by the National Natural Science Foundation of China(Grand No.52325904)National Key Research and Development Program of China(Grant No.2023YFB2390200)the National Natural Science Foundation of China(Grant No.52309134).
文摘Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the optimal collaborative control parameters that support rapid drilling is crucial for improving the combined performance.This study used average drilling speed,average torque,and total specificenergy for quantitative analysis to characterize the efficiencyand economy of combined rock breaking.Given the advantage of the response surface methodology in providing high-precision predictions with limited experimental data,regression models of the average drilling speed,average torque,and total specificenergy were established.The results showed that as the laser power and irradiation time increased,the average drilling speed firstincreased rapidly and then leveled off,while the average torque decreased sharply before decelerating.The total specificenergy initially decreased and then increased,with the combined drilling outperforming conventional mechanical drilling within specific parameter ranges.As the weight on bit increased,both the average torque and total specificenergy first decreased and then increased.With rising rotating speed,the average torque exhibited a trend of initial increase,then decrease,and finalincrease,whereas the total specificenergy increased slowly at firstand then sharply.Both parameters exhibited optimal values at which the average torque and total specific energy remained at minimal levels.For granite combined drilling,the optimal performance was achieved at a laser power of 3000 W,irradiation time of 31 s,the weight on bit of 2.4 kN,and the rotating speed of 97 r/min.
基金financially supported by the National Natural Science Foundation of China under Grants (Nos. 21476009, 21406007 and U1462104)
文摘The adjustable parameters in the popular conductor-like screening model for real solvents(COSMO-RS)within the Amsterdam density functional(ADF)framework have been re-optimized to fit for the systems containing ionic liquids(ILs).To get the optimal values of misfit energy constant a^0,hydrogen bond coefficient c_(hb)and effective contact surface area of a segment a_(eff),2283 activity coefficient data points at infinite dilution and 1433 CO_2 solubility data points exhaustively collected from references were used as training set.The average relative deviations(ARDs)of activity coefficients at infinite dilution and CO_2 solubility between experimental data and predicted values are 32.22%and17.61%,respectively,both of which are significantly lower than the original COSMO-RS versions.Predictions for other activity coefficients of solutes in ILs,solubility data of CO_2 in pure ILs and the binary mixtures of ILs at either high or low temperatures,and vapor–liquid equilibrium(VLE)for binary systems involving ILs have also been performed to demonstrate the validity of the parameterization of COSMO-RS model for ILs.The results showed that the predicted results by COSMO-RS model with the new optimized parameters are in much better agreement with experimental data than those by the original versions over a wide temperature and pressure range.The COSMO-RS model for ILs presented in this work improves the prediction accuracy of thermodynamic properties for the systems containing ILs,which is always highly desirable for general chemical engineers.
基金supported by the National Basic Research Program of China (the 973 Program,Grant No.2010CB951102)the National Supporting Plan Program of China (Grants No.2007BAB28B01 and 2008BAB42B03)the National Natural Science Foundation of China (Grant No. 50709042),and the Regional Water Theme in the Water for a Healthy Country Flagship
文摘In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.
基金supported by the National Natural Science Foundations of China (Grant Nos. 40805020 and 10901047)the Natural Science Foundation of Henan Province (Grant No. 112300410054)
文摘An extension of the conditional nonlinear optimal parameter perturbation （CNOP-P） method is applied to the parameter optimization of the Common Land Model （CoLM） for the North China Plain with the differential evolution （DE） method. Using National Meteorological Center （NMC） Reanalysis 6-hourly surface flux data and National Center for Environmental Prediction/Department of Energy （NCEP/DOE） Atmospheric Model Intercomparison Project II （AMIP-II） 6-hourly Reanalysis Gaussian Grid data, two experiments （I and II） were designed to investigate the impact of the percentages of sand and clay in the shallow soil in CoLM on its ability to simulate shallow soil moisture. A third experiment （III） was designed to study the shallow soil moisture and latent heat flux simultaneously. In all the three experiments, after the optimization stage, the percentages of sand and clay of the shallow soil were used to predict the shallow soil moisture in the following month. The results show that the optimal parameters can enable CoLM to better simulate shallow soil moisture, with the simulation results of CoLM after the double-parameter optimal ex- periment being better than the single-parameter optimal experiment in the optimization slot. Purthermore, the optimal parameters were able to significantly improve the prediction results of CoLM at the prediction stage. In addition, whether or not the atmospheric forcing and observational data are accurate can seriously affect the results of optimization, and the more accurate the data are, the more significant the results of optimization may be.
基金Supported by the National Natural Science Foundation of China (No.60202004).
文摘Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm directly affects the validity of the clustering results, which is still an open issue. For this purpose, this paper proposes a fuzzy CLOPE algorithm, and presents a method for the optimal parameter choice by defining a modified partition fuzzy degree as a clustering validity function. The experimental results with real data set illustrate the effectiveness of the proposed fuzzy CLOPE algorithm and optimal parameter choice method based on the modified partition fuzzy degree.
基金This work is also supported by the Faculty of Computer Science and Information Technology,University of Malaya under Postgraduate Research Grant(PG035-2016A).
文摘In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously decreasing.In such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary applications.But network security poses a serious challenge to UAV networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the UAV networks.Classical IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data traffic.In order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection problem.In this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for UAV networks.In addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion detection.For parameter optimization of DRL technique,BWO algorithm is applied.It helps in improving the intrusion detection performance of UAV networks.An extensive set of experimental analysis was performed to highlight the supremacy of the proposed model.From the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.
基金support provided by the Ministry of Science and Technology,Taiwan,ROC under Contract No.MOST 110-2221-E-019-044.
文摘How to accelerate the convergence speed and avoid computing the inversion of a Jacobian matrix is important in the solution of nonlinear algebraic equations(NAEs).This paper develops an approach with a splitting-linearizing technique based on the nonlinear term to reduce the effect of the nonlinear terms.We decompose the nonlinear terms in the NAEs through a splitting parameter and then linearize the NAEs around the values at the previous step to a linear system.Through the maximal orthogonal projection concept,to minimize a merit function within a selected interval of splitting parameters,the optimal parameters can be quickly determined.In each step,a linear system is solved by the Gaussian elimination method,and the whole iteration procedure is convergent very fast.Several numerical tests show the high performance of the optimal split-linearization iterative method(OSLIM).
基金the National Natural Science Founda-tion of China (Nos. 60802058 and 60832009)the SMC Young Teacher Sponsorship of Shanghai JiaotongUniversity
文摘Spectrum sensing is an important part of cognitive radio systems to find spectrum hole for transmission which enables cognitive radio systems coexist with the authorized radio systems without harmful interference.In this paper,an improved cyclostationary feature detection method is proposed to reduce computational complexity without loss of good performance based on the optimal parameter selection strategy for choosing detection parameters of cyclic frequency and lag.Taking binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals as examples,the theoretical analyses are presented for choosing the optimal parameters.Simulation results are given to certify the correctness of the proposed parameter selection strategy and show the performance of the proposed method.
基金project of the“The University Synergy Innovation Program of Anhui Province(GXXT-2019-004)”,“Natural Science Research Project of Anhui Universities(KJ2021ZD0144)”,“Wuhu Key R&D Project:Research and Industrialization of Intelligent Control Method of Engine Energy-Feeding Hydraulic Semi-Active Mount”.
文摘The aim of the study is to determine the optimal structural parameters for a plastic centrifugal pump inducer within the framework of an orthogonal experimental method.For this purpose,a numerical study of the related flow field is performed using CFX.The shaft power and the head of the pump are taken as the evaluation indicators.Accordingly,the examined variables are the thickness(S),the blade cascade degree(t),the blade rim angle(β1),the blade hub angle(β2)and the hub length(L).The impact of each structural parameter on each evaluation index is examined and special attention is paid to the following combinations:S2 mm,t 2,β1235°,β2360°and L 140 mm(corresponding to a maximum head of 98.15 m);S 5 mm,t 1.6,β1252°,β2350°and L 140 mm(corresponding to a minimum shaft power of 63.06 KW).Moreover,using least squares and fish swarm algorithms,the pump shaft power and head are further optimized,yielding the following optimal combination:S 5 mm,t 1.9,β1252°,β2360°and L 145 mm(corresponding to the maximum head of 91.90 m,and a minimum shaft power of 64.83 KW).
基金Sponsored by the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No. 20050213006)
文摘A new mothod was presented to find the optimal location and parameter setting of Thyristor Controlled Series Compensator (TCSC) to maxmize the transfer capability.Firstly the sensitivity of the transfer capability with respect was described to the line's reactance was described to find the more sensitive lines for installing TCSC,however,the line which has the most sesitivity value is always not the best line for installing TCSC.For solving this problem,the more sensitive m lines were selected as the alternative line group of installing TCSC,and then modified particle swarm optimization (MPSO) was used to find out the optimal location and the optimal parameter settings of TCSC.Particle swarm optimization (PSO) algorithm can results premature convergence.For solving this problem,population entropy and cellular automata were introduced to it.Simulation results of IEEE 30-bus system proved the effectiveness of the method and its application values.
基金Supported by National Natural Science Foundation of China(Grant No.12071491)Guangzhou Science and Technology Plan Project(Grant No.202102080177).
文摘By using the weight function method,the matching parameters of the half discrete Hilbert type multiple integral inequality with a non-homogeneous kernel K(n,||x||ρ,m)=G(nλ1||x||ρmλ,2)are discussed,some equivalent conditions of the optimal matching parameter are established,and the expression of the optimal constant factor is obtained.Finally,their applications in operator theory are considered.
文摘Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.
文摘The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources.This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative(FOPID)controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration.To improve load frequency control,the proposed controllers are applied to a two-area interconnectedmicrogrid system incorporating diverse energy sources,such as wind turbines,photovoltaic cells,diesel generators,and various storage technologies.A novelmeta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers.The efficacy of the advanced FOPID controllers is demonstrated through comparative analyses against traditional proportional integral derivative(PID)and FOPID controllers,showcasing superior performance inmanaging systemfluctuations.The optimization algorithm is also evaluated against other artificial intelligent methods for parameter optimization,affirming the proposed solution’s efficiency.The robustness of the intelligent controllers against system uncertainties is further validated under extensive power disturbances,proving their capability to maintain grid stability.The dual-controller configuration ensures redundancy,allowing them to operate as mutual backups,enhancing system reliability.This research underlines the importance of sophisticated control strategies for future-proofing microgrid operations against the backdrop of evolving energy landscapes.
