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
In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of mul...In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of multiple energy sources can be realized,while the swing braking energy can be recovered and used by means of hydraulic energy.Additionally,considering the system constraints and comprehensive optimization conditions of energy efficiency and dynamic characteristics,an improved multi-objective particle swarm optimization(IMOPSO)combined with an adaptive grid is proposed for parameter optimization of the SSEHS.Meanwhile,a parameter rule-based control strategy is designed,which can switch to a reasonable working mode according to the real-time state.Finally,a physical prototype of a 50-t excavator and its AMESim model is established.The semi-simulation and semi-experiment results demonstrate that compared with a conventional swing system,energy consumption under the 90°rotation condition could be reduced by about 51.4%in the SSEHS before parameter optimization,while the energy-saving efficiency is improved by another 13.2%after parameter optimization.This confirms the effectiveness of the SSEHS and the IMOPSO parameter optimization method proposed in this paper.The IMOPSO algorithm is universal and can be used for parameter matching and optimization of hybrid power systems.展开更多
Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and ...Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and compaction parameters.Design/methodology/approach-To address these issues,a novel multi-indicator IVCT method was proposed,including physical indicator dry density(ρd)and mechanical indicators dynamic stiffness(Krb)and bearing capacity coefficient(K20).Then,a series of IVCTs on HRGA under different compaction parameters were conducted with an improved vibration compactor,which could monitor the physical-mechanical indicators in real-time.Finally,the optimal vibration compaction parameters,including the moisture content(ω),the diameter-to-maximum particle size ratio(Rd),the thickness-to-maximum particle size ratio(Rh),the vibration frequency(f),the vibration mass(Mc)and the eccentric distance(re),were determined based on the evolution characteristics for the physical-mechanical indicators during compaction.Findings-All results indicated that theρd gradually increased and then stabilized,and the Krb initially increased and then decreased.Moreover,the inflection time of the Krb was present as the optimal compaction time(Tlp)during compaction.Additionally,optimal compaction was achieved whenωwas the water-holding content after mud pumping,Rd was 3.4,Rh was 3.5,f was the resonance frequency,and the ratio between the excitation force and the Mc was 1.8.Originality/value-The findings of this paper were significant for the quality control of HRGA compaction.展开更多
The spatial optimization of best management practices(BMPs) plays a critical role in precise watershed pollution control. However, the effectiveness of BMPs exhibits a complex nonlinear dependence on both configuratio...The spatial optimization of best management practices(BMPs) plays a critical role in precise watershed pollution control. However, the effectiveness of BMPs exhibits a complex nonlinear dependence on both configuration unit scale and rainfall intensity, often leading to widespread spatiotemporal mismatches during implementation. To fill this gap, this study proposes a new framework:(a) delineating configuration units based on the implementation scale differences between structural and nonstructural BMPs;(b) incorporating BMP reduction thresholds to enable dynamic adjustment of design scales according to inflow loads;and(c) developing a staged allocation strategy tailored to varying rainfall scenarios. The framework is exemplified by an agricultural catchment in the southeastern Liaohe watershed, China. The results showed that the framework could improve the assessment accuracy and cost-effectiveness of pollution control. Specifically, neglecting BMP reduction thresholds resulted in a 51.35% underestimation of treatment costs. Incorporating these thresholds and dynamically adjusting BMP design scales reduced treatment costs by 62.70%. Furthermore, the framework facilitated more precise localization of structural BMPs(1 km^(2)) and improved optimization efficiency by 95.91%. The proposed staged allocation strategy ensured water quality compliance under varying rainfall intensities. Structural BMPs primarily addressed pollution from light to moderate rainfall in the initial stage, while nonstructural BMPs targeted heavy rainfall pollution in the subsequent stage. The proposed framework may enhance the spatiotemporal adaptability of BMP configuration to respond to the threats posed by climate change and human activities. It can also be extended to other agriculture-dominated watersheds.展开更多
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W...Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.展开更多
In this paper,we propose and analyze two second-order accurate finite difference schemes for the one-dimensional heat equation with concentrated capacity on a computa-tional domain=[a,b].We first transform the target ...In this paper,we propose and analyze two second-order accurate finite difference schemes for the one-dimensional heat equation with concentrated capacity on a computa-tional domain=[a,b].We first transform the target equation into the standard heat equation on the domain excluding the singular point equipped with an inner interface matching(IIM)condition on the singular point x=ξ∈(a,b),then adopt Taylor’s ex-pansion to approximate the IIM condition at the singular point and apply second-order finite difference method to approximate the standard heat equation at the nonsingular points.This discrete procedure allows us to choose different grid sizes to partition the two sub-domains[a,ξ]and[ξ,b],which ensures that x=ξ is a grid point,and hence the pro-posed schemes can be generalized to the heat equation with more than one concentrated capacities.We prove that the two proposed schemes are uniquely solvable.And through in-depth analysis of the local truncation errors,we rigorously prove that the two schemes are second-order accurate both in temporal and spatial directions in the maximum norm without any constraint on the grid ratio.Numerical experiments are carried out to verify our theoretical conclusions.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
To efficiently and fully utilize aircraft carrier resources,an optimization model is presented to deal with parameter matching between aircraft and carrier in the process of aircraft catapult launch.Based on carrier a...To efficiently and fully utilize aircraft carrier resources,an optimization model is presented to deal with parameter matching between aircraft and carrier in the process of aircraft catapult launch.Based on carrier aircraft longitudinal dynamic equations and theorem of kinetic energy in catapult launch course,the work characteristics of different forces are learned and a theory model of parameter matching is deduced.In view of the uncertainty of the model parameters of the theory model and the low matching accuracy of the approximate model,an optimization model of parameter matching is introduced in line with the structure of theory model and the approximate model and is generated by the proposed immune genetic algorithm.Compared with the original genetic algorithm and immune algorithm,the proposed algorithm has better calculation accuracy and convergence.The calculation results show that the optimization model occupies certain application value of engineering estimation from the comparison with the relevant literature data and has higher precision than the approximate models.The validity of the proposed approach is verified with numerical case study on a carrier based aircraft.展开更多
Particle accelerators are devices used for research in scientific problems such as high energy and nuclear physics.In a particle accelerator, the shape of particle beam envelope is changed dynamically along the forwar...Particle accelerators are devices used for research in scientific problems such as high energy and nuclear physics.In a particle accelerator, the shape of particle beam envelope is changed dynamically along the forward direction. Thus, this reference direction can be considered as an auxiliary "time" beam axis. In this paper, the optimal beam matching control problem for a low energy transport system in a charged particle accelerator is considered. The beam matching procedure is formulated as a finite "time" dynamic optimization problem, in which the Kapchinsky-Vladimirsky(K-V) coupled envelope equations model beam dynamics. The aim is to drive any arbitrary initial beam state to a prescribed target state, as well as to track reference trajectory as closely as possible, through the control of the lens focusing strengths in the beam matching channel. We first apply the control parameterization method to optimize lens focusing strengths, and then combine this with the time-scaling transformation technique to further optimize the drift and lens length in the beam matching channel. The exact gradients of the cost function with respect to the decision parameters are computed explicitly through the state sensitivity-based analysis method. Finally, numerical simulations are illustrated to verify the effectiveness of the proposed approach.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
Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driv...Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.展开更多
Most previous land-surface model calibration studies have defined globalranges for their parameters to search for optimal parameter sets. Little work has been conducted tostudy the impacts of realistic versus global r...Most previous land-surface model calibration studies have defined globalranges for their parameters to search for optimal parameter sets. Little work has been conducted tostudy the impacts of realistic versus global ranges as well as model complexities on the calibrationand uncertainty estimates. The primary purpose of this paper is to investigate these impacts byemploying Bayesian Stochastic Inversion (BSI) to the Chameleon Surface Model (CHASM). The CHASM wasdesigned to explore the general aspects of land-surface energy balance representation within acommon modeling framework that can be run from a simple energy balance formulation to a complexmosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem,importance sampling, and very fast simulated annealing. The model forcing data and surface flux datawere collected at seven sites representing a wide range of climate and vegetation conditions. Foreach site, four experiments were performed with simple and complex CHASM formulations as well asrealistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parametersets were used for each run. The results show that the use of global and realistic ranges givessimilar simulations for both modes for most sites, but the global ranges tend to produce someunreasonable optimal parameter values. Comparison of simple and complex modes shows that the simplemode has more parameters with unreasonable optimal values. Use of parameter ranges and modelcomplexities have significant impacts on frequency distribution of parameters, marginal posteriorprobability density functions, and estimates of uncertainty of simulated sensible and latent heatfluxes. Comparison between model complexity and parameter ranges shows that the former has moresignificant impacts on parameter and uncertainty estimations.展开更多
The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically inv...The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.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
Energy transmission efficiency in the magnetic pulse generators varies with saturated time of magnetic switch. An optimal matching time exists and depends on the compression ratio, under which, the energy transmission...Energy transmission efficiency in the magnetic pulse generators varies with saturated time of magnetic switch. An optimal matching time exists and depends on the compression ratio, under which, the energy transmission efficiency can reach approximate 100%. The equation of required magnetic core volume is obtained by taken into account the optimal matching mode. It indicates that a great reduction on the volume is feasible under the optimal matching mode. The circuit simulation code-PSPICE is also introduced to simulate a 3-stage magnetic pulse compressor, and the results are in accordance with those of equivalent circuit analyses.展开更多
Due to uncertainties in initial conditions and parameters, the stability and uncertainty of grassland ecosystem simulations using ecosystem models are issues of concern. Our objective is to determine the types and pat...Due to uncertainties in initial conditions and parameters, the stability and uncertainty of grassland ecosystem simulations using ecosystem models are issues of concern. Our objective is to determine the types and patterns of initial and parameter perturbations that yield the greatest instability and uncertainty in simulated grassland ecosystems using theoretical models. We used a nonlinear optimization approach, i.e., a conditional nonlinear optimal perturbation related to initial and parameter perturbations （CNOP） approach, in our work. Numerical results indicated that the CNOP showed a special and nonlinear optimal pattern when the initial state variables and multiple parameters were considered simultaneously. A visibly different complex optimal pattern characterizing the CNOPs was obtained by choosing different combinations of initial state variables and multiple parameters in different physical processes. We propose that the grassland modeled ecosystem caused by the CNOP-type perturbation is unstable and exhibits two aspects： abrupt change and the time needed for the abrupt change from a grassland equilibrium state to a desert equilibrium state when the initial state variables and multiple parameters are considered simultaneously. We compared these findings with results affected by the CNOPs obtained by considering only uncertainties in initial state variables and in a single parameter. The numerical results imply that the nonlinear optimal pattern of initial perturbations and parameter perturbations, especially for more parameters or when special parameters are involved, plays a key role in determining stabilities and uncertainties associated with a simulated or predicted grassland ecosystem.展开更多
A novel layered method was proposed to solve the problem of Web services composition.In this method,services composition problem was formally transformed into the optimal matching problem of every layer,then optimal m...A novel layered method was proposed to solve the problem of Web services composition.In this method,services composition problem was formally transformed into the optimal matching problem of every layer,then optimal matching problem was modeled based on the hypergraph theory,and solved by computing the minimal transversals of the hypergraph.Meanwhile,two optimization algorithms were designed to discard some useless states at the intermediary steps of the composition algorithm.The effectiveness of the composition method was tested by a set of experiments,in addition,an example regarding the travel services composition was also given.The experimental results show that this method not only can automatically generate composition tree whose leaf nodes correspond to services composition solutions,but also has better performance on execution time and solution quality by adopting two proposed optimization algorithms.展开更多
We propose a method to determine the optimal power of the microwave resonance transition that simultaneously improves the signal-to-noise ratio and reduces line width based on saturation broadening theory and experime...We propose a method to determine the optimal power of the microwave resonance transition that simultaneously improves the signal-to-noise ratio and reduces line width based on saturation broadening theory and experiment. Saturation broadening spectra of the ground state hyperfine transition of trapped 199Hg＋ ions are measured and analyzed. The value of the optimal microwave power is obtained by using the proposed method and is verified. Rabi oscillations decay spectra of trapped 199Hg＋ ions are observed and the optimal microwave irradiation time for the maximum transition signal intensity is determined. This work will help to improve the short-term frequency stability of the mercury ion microwave frequency standard.展开更多
This paper aims to improve the performance of a class of distributed parameter systems for the optimal switching of actuators and controllers based on event-driven control. It is assumed that in the available multiple...This paper aims to improve the performance of a class of distributed parameter systems for the optimal switching of actuators and controllers based on event-driven control. It is assumed that in the available multiple actuators, only one actuator can receive the control signal and be activated over an unfixed time interval, and the other actuators keep dormant. After incorporating a state observer into the event generator, the event-driven control loop and the minimum inter-event time are ultimately bounded. Based on the event-driven state feedback control, the time intervals of unfixed length can be obtained. The optimal switching policy is based on finite horizon linear quadratic optimal control at the beginning of each time subinterval. A simulation example demonstrate the effectiveness of the proposed policy.展开更多
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.
基金supported by the Changsha Major Science and Technology Plan Project,China(No.kq2207002)the Natural Science Foundation of Hunan Province(No.2023JJ40720)the Postgraduate Innovative Project of Central South University,China(No.2022XQLH058)。
文摘In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of multiple energy sources can be realized,while the swing braking energy can be recovered and used by means of hydraulic energy.Additionally,considering the system constraints and comprehensive optimization conditions of energy efficiency and dynamic characteristics,an improved multi-objective particle swarm optimization(IMOPSO)combined with an adaptive grid is proposed for parameter optimization of the SSEHS.Meanwhile,a parameter rule-based control strategy is designed,which can switch to a reasonable working mode according to the real-time state.Finally,a physical prototype of a 50-t excavator and its AMESim model is established.The semi-simulation and semi-experiment results demonstrate that compared with a conventional swing system,energy consumption under the 90°rotation condition could be reduced by about 51.4%in the SSEHS before parameter optimization,while the energy-saving efficiency is improved by another 13.2%after parameter optimization.This confirms the effectiveness of the SSEHS and the IMOPSO parameter optimization method proposed in this paper.The IMOPSO algorithm is universal and can be used for parameter matching and optimization of hybrid power systems.
基金funded by the National Key R&D Program“Transportation Infrastructure”project(No.2022YFB2603400)the Technology Research and Development Plan Program of China State Railway Group Co.,Ltd.(No.Q2024T001)the National project pre research project of Suzhou City University(No.2023SGY019).
文摘Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and compaction parameters.Design/methodology/approach-To address these issues,a novel multi-indicator IVCT method was proposed,including physical indicator dry density(ρd)and mechanical indicators dynamic stiffness(Krb)and bearing capacity coefficient(K20).Then,a series of IVCTs on HRGA under different compaction parameters were conducted with an improved vibration compactor,which could monitor the physical-mechanical indicators in real-time.Finally,the optimal vibration compaction parameters,including the moisture content(ω),the diameter-to-maximum particle size ratio(Rd),the thickness-to-maximum particle size ratio(Rh),the vibration frequency(f),the vibration mass(Mc)and the eccentric distance(re),were determined based on the evolution characteristics for the physical-mechanical indicators during compaction.Findings-All results indicated that theρd gradually increased and then stabilized,and the Krb initially increased and then decreased.Moreover,the inflection time of the Krb was present as the optimal compaction time(Tlp)during compaction.Additionally,optimal compaction was achieved whenωwas the water-holding content after mud pumping,Rd was 3.4,Rh was 3.5,f was the resonance frequency,and the ratio between the excitation force and the Mc was 1.8.Originality/value-The findings of this paper were significant for the quality control of HRGA compaction.
基金supported by the Fund for Innovative Research Group of the National Natural Science Foundation of China (Grant No.52221003)。
文摘The spatial optimization of best management practices(BMPs) plays a critical role in precise watershed pollution control. However, the effectiveness of BMPs exhibits a complex nonlinear dependence on both configuration unit scale and rainfall intensity, often leading to widespread spatiotemporal mismatches during implementation. To fill this gap, this study proposes a new framework:(a) delineating configuration units based on the implementation scale differences between structural and nonstructural BMPs;(b) incorporating BMP reduction thresholds to enable dynamic adjustment of design scales according to inflow loads;and(c) developing a staged allocation strategy tailored to varying rainfall scenarios. The framework is exemplified by an agricultural catchment in the southeastern Liaohe watershed, China. The results showed that the framework could improve the assessment accuracy and cost-effectiveness of pollution control. Specifically, neglecting BMP reduction thresholds resulted in a 51.35% underestimation of treatment costs. Incorporating these thresholds and dynamically adjusting BMP design scales reduced treatment costs by 62.70%. Furthermore, the framework facilitated more precise localization of structural BMPs(1 km^(2)) and improved optimization efficiency by 95.91%. The proposed staged allocation strategy ensured water quality compliance under varying rainfall intensities. Structural BMPs primarily addressed pollution from light to moderate rainfall in the initial stage, while nonstructural BMPs targeted heavy rainfall pollution in the subsequent stage. The proposed framework may enhance the spatiotemporal adaptability of BMP configuration to respond to the threats posed by climate change and human activities. It can also be extended to other agriculture-dominated watersheds.
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R442)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.
基金supported by the National Natural Science Foundation of China(Grant No.11571181)by the Natural Science Foundation of Jiangsu Province(Grant No.BK20171454).
文摘In this paper,we propose and analyze two second-order accurate finite difference schemes for the one-dimensional heat equation with concentrated capacity on a computa-tional domain=[a,b].We first transform the target equation into the standard heat equation on the domain excluding the singular point equipped with an inner interface matching(IIM)condition on the singular point x=ξ∈(a,b),then adopt Taylor’s ex-pansion to approximate the IIM condition at the singular point and apply second-order finite difference method to approximate the standard heat equation at the nonsingular points.This discrete procedure allows us to choose different grid sizes to partition the two sub-domains[a,ξ]and[ξ,b],which ensures that x=ξ is a grid point,and hence the pro-posed schemes can be generalized to the heat equation with more than one concentrated capacities.We prove that the two proposed schemes are uniquely solvable.And through in-depth analysis of the local truncation errors,we rigorously prove that the two schemes are second-order accurate both in temporal and spatial directions in the maximum norm without any constraint on the grid ratio.Numerical experiments are carried out to verify our theoretical conclusions.
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supported by the Excellence Foundation of BUAA for Ph D.and the National Natural Science Foundation of China(No.91641123).
文摘To efficiently and fully utilize aircraft carrier resources,an optimization model is presented to deal with parameter matching between aircraft and carrier in the process of aircraft catapult launch.Based on carrier aircraft longitudinal dynamic equations and theorem of kinetic energy in catapult launch course,the work characteristics of different forces are learned and a theory model of parameter matching is deduced.In view of the uncertainty of the model parameters of the theory model and the low matching accuracy of the approximate model,an optimization model of parameter matching is introduced in line with the structure of theory model and the approximate model and is generated by the proposed immune genetic algorithm.Compared with the original genetic algorithm and immune algorithm,the proposed algorithm has better calculation accuracy and convergence.The calculation results show that the optimization model occupies certain application value of engineering estimation from the comparison with the relevant literature data and has higher precision than the approximate models.The validity of the proposed approach is verified with numerical case study on a carrier based aircraft.
基金supported by the National Natural Science Foundation of China(61703114,61673126,61703217,U1701261)the Science and Technology Plan Project of Guangdong(2014B090907010,2015B010131014)
文摘Particle accelerators are devices used for research in scientific problems such as high energy and nuclear physics.In a particle accelerator, the shape of particle beam envelope is changed dynamically along the forward direction. Thus, this reference direction can be considered as an auxiliary "time" beam axis. In this paper, the optimal beam matching control problem for a low energy transport system in a charged particle accelerator is considered. The beam matching procedure is formulated as a finite "time" dynamic optimization problem, in which the Kapchinsky-Vladimirsky(K-V) coupled envelope equations model beam dynamics. The aim is to drive any arbitrary initial beam state to a prescribed target state, as well as to track reference trajectory as closely as possible, through the control of the lens focusing strengths in the beam matching channel. We first apply the control parameterization method to optimize lens focusing strengths, and then combine this with the time-scaling transformation technique to further optimize the drift and lens length in the beam matching channel. The exact gradients of the cost function with respect to the decision parameters are computed explicitly through the state sensitivity-based analysis method. Finally, numerical simulations are illustrated to verify the effectiveness of the proposed approach.
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.
文摘Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.
文摘Most previous land-surface model calibration studies have defined globalranges for their parameters to search for optimal parameter sets. Little work has been conducted tostudy the impacts of realistic versus global ranges as well as model complexities on the calibrationand uncertainty estimates. The primary purpose of this paper is to investigate these impacts byemploying Bayesian Stochastic Inversion (BSI) to the Chameleon Surface Model (CHASM). The CHASM wasdesigned to explore the general aspects of land-surface energy balance representation within acommon modeling framework that can be run from a simple energy balance formulation to a complexmosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem,importance sampling, and very fast simulated annealing. The model forcing data and surface flux datawere collected at seven sites representing a wide range of climate and vegetation conditions. Foreach site, four experiments were performed with simple and complex CHASM formulations as well asrealistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parametersets were used for each run. The results show that the use of global and realistic ranges givessimilar simulations for both modes for most sites, but the global ranges tend to produce someunreasonable optimal parameter values. Comparison of simple and complex modes shows that the simplemode has more parameters with unreasonable optimal values. Use of parameter ranges and modelcomplexities have significant impacts on frequency distribution of parameters, marginal posteriorprobability density functions, and estimates of uncertainty of simulated sensible and latent heatfluxes. Comparison between model complexity and parameter ranges shows that the former has moresignificant impacts on parameter and uncertainty estimations.
基金Project (No. 20276063) supported by the National Natural Sci-ence Foundation of China
文摘The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.
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 High Technology Resesarch Development Project of China (863)
文摘Energy transmission efficiency in the magnetic pulse generators varies with saturated time of magnetic switch. An optimal matching time exists and depends on the compression ratio, under which, the energy transmission efficiency can reach approximate 100%. The equation of required magnetic core volume is obtained by taken into account the optimal matching mode. It indicates that a great reduction on the volume is feasible under the optimal matching mode. The circuit simulation code-PSPICE is also introduced to simulate a 3-stage magnetic pulse compressor, and the results are in accordance with those of equivalent circuit analyses.
基金provided by grants from National Natural Science Foundation of China (Grant Nos. 40905050and 40830955)the Chinese Academy of Sciences (CASGrant No. KZCX3-SW-230)
文摘Due to uncertainties in initial conditions and parameters, the stability and uncertainty of grassland ecosystem simulations using ecosystem models are issues of concern. Our objective is to determine the types and patterns of initial and parameter perturbations that yield the greatest instability and uncertainty in simulated grassland ecosystems using theoretical models. We used a nonlinear optimization approach, i.e., a conditional nonlinear optimal perturbation related to initial and parameter perturbations （CNOP） approach, in our work. Numerical results indicated that the CNOP showed a special and nonlinear optimal pattern when the initial state variables and multiple parameters were considered simultaneously. A visibly different complex optimal pattern characterizing the CNOPs was obtained by choosing different combinations of initial state variables and multiple parameters in different physical processes. We propose that the grassland modeled ecosystem caused by the CNOP-type perturbation is unstable and exhibits two aspects： abrupt change and the time needed for the abrupt change from a grassland equilibrium state to a desert equilibrium state when the initial state variables and multiple parameters are considered simultaneously. We compared these findings with results affected by the CNOPs obtained by considering only uncertainties in initial state variables and in a single parameter. The numerical results imply that the nonlinear optimal pattern of initial perturbations and parameter perturbations, especially for more parameters or when special parameters are involved, plays a key role in determining stabilities and uncertainties associated with a simulated or predicted grassland ecosystem.
基金Project(2010CB328101) supported by the National Basic Research Program of ChinaProject(2009AA01Z401) supported by the National High Technology Research and Development Program of China+4 种基金Projects(60803032,90818023) supported by the National Natural Science Foundation of ChinaProjects(09510701300,09JC1414200,09DZ1120403) supported by the Shanghai Science and Technology Commission,China"Shu Guang" Project(10SG23) supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation,ChinaProject(09QA1405800) supported by Shanghai Science and Technology Commission Rising-Star Program,ChinaProject(NCET-10-0598) supported by Program for New Century Excellent Talents in Chinese University
文摘A novel layered method was proposed to solve the problem of Web services composition.In this method,services composition problem was formally transformed into the optimal matching problem of every layer,then optimal matching problem was modeled based on the hypergraph theory,and solved by computing the minimal transversals of the hypergraph.Meanwhile,two optimization algorithms were designed to discard some useless states at the intermediary steps of the composition algorithm.The effectiveness of the composition method was tested by a set of experiments,in addition,an example regarding the travel services composition was also given.The experimental results show that this method not only can automatically generate composition tree whose leaf nodes correspond to services composition solutions,but also has better performance on execution time and solution quality by adopting two proposed optimization algorithms.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11074282 and 11474320
文摘We propose a method to determine the optimal power of the microwave resonance transition that simultaneously improves the signal-to-noise ratio and reduces line width based on saturation broadening theory and experiment. Saturation broadening spectra of the ground state hyperfine transition of trapped 199Hg＋ ions are measured and analyzed. The value of the optimal microwave power is obtained by using the proposed method and is verified. Rabi oscillations decay spectra of trapped 199Hg＋ ions are observed and the optimal microwave irradiation time for the maximum transition signal intensity is determined. This work will help to improve the short-term frequency stability of the mercury ion microwave frequency standard.
基金supported by the National Natural Science Foundation of China(Grant Nos.61174021 and 61104155)the Fundamental Research Funds for theCentral Universities,China(Grant Nos.JUDCF13037 and JUSRP51322B)+1 种基金the Programme of Introducing Talents of Discipline to Universities,China(GrantNo.B12018)the Jiangsu Innovation Program for Graduates,China(Grant No.CXZZ13-0740)
文摘This paper aims to improve the performance of a class of distributed parameter systems for the optimal switching of actuators and controllers based on event-driven control. It is assumed that in the available multiple actuators, only one actuator can receive the control signal and be activated over an unfixed time interval, and the other actuators keep dormant. After incorporating a state observer into the event generator, the event-driven control loop and the minimum inter-event time are ultimately bounded. Based on the event-driven state feedback control, the time intervals of unfixed length can be obtained. The optimal switching policy is based on finite horizon linear quadratic optimal control at the beginning of each time subinterval. A simulation example demonstrate the effectiveness of the proposed policy.
