Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data....Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.展开更多
A class of hybrid algorithms of real-time simulation based on evaluation of non-integerstep right-hand side function are presented in this paper. And some results of the convergence and stability of the algorithms are...A class of hybrid algorithms of real-time simulation based on evaluation of non-integerstep right-hand side function are presented in this paper. And some results of the convergence and stability of the algorithms are given. Using the class of algorithms, evaluation for the right-hand side function is needed once in every integration-step. Moreover, comparing with the other methods with the same amount of work, their numerical stability regions are larger and the method errors are smaller, and the numerical experiments show that the algorithms are very effective.展开更多
In this paper a class of real-time parallel modified Rosenbrock methods of numerical simulation is constructed for stiff dynamic systems on a multiprocessor system, and convergence and numerical stability of these met...In this paper a class of real-time parallel modified Rosenbrock methods of numerical simulation is constructed for stiff dynamic systems on a multiprocessor system, and convergence and numerical stability of these methods are discussed. A-stable real-time parallel formula of two-stage third-order and A(α)-stable real-time parallel formula with o ≈ 89.96° of three-stage fourth-order are particularly given. The numerical simulation experiments in parallel environment show that the class of algorithms is efficient and applicable, with greater speedup.展开更多
The quantum critical regime marks a zone in the phase diagram where quantum fluctuation around the critical point plays a significant role at finite temperatures.While it is of great physical interest,simulation of th...The quantum critical regime marks a zone in the phase diagram where quantum fluctuation around the critical point plays a significant role at finite temperatures.While it is of great physical interest,simulation of the quantum critical regime can be difficult on a classical computer due to its intrinsic complexity.Herein,we propose a variational approach,which minimizes the variational free energy,to simulate and locate the quantum critical regime on a quantum computer.The variational quantum algorithm adopts an ansatz by performing an unitary operator on a product of a single-qubit mixed state,in which the entropy can be analytically obtained from the initial state,and thus the free energy can be accessed conveniently.With numeral simulation,using the one-dimensional Kitaev model as a demonstration we show that the quantum critical regime can be identified by accurately evaluating the temperature crossover line.Moreover,the dependencies of both the correlation length and the phase coherence time with temperature are evaluated for the thermal states.Our work suggests a practical way as well as a first step for investigating quantum critical systems at finite temperatures on quantum devices with few qubits.展开更多
This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aim...This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.展开更多
Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental p...Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental preparations of Gibbs states and excited states of Heisenberg X X and X X Z models by using a 5-qubit programmable superconducting processor.In the experiments,we apply a hybrid quantum–classical algorithm to generate finite temperature states with classical probability models and variational quantum circuits.We reveal that the Hamiltonians can be fully diagonalized with optimized quantum circuits,which enable us to prepare excited states at arbitrary energy density.We demonstrate that the approach has a self-verifying feature and can estimate fundamental thermal observables with a small statistical error.Based on numerical results,we further show that the time complexity of our approach scales polynomially in the number of qubits,revealing its potential in solving large-scale problems.展开更多
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig...In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.展开更多
In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation...In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.展开更多
Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember ...Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN).展开更多
Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index ...Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.展开更多
An efficient importance sampling algorithm is presented to analyze reliability of complex structural system with multiple failure modes and fuzzy-random uncertainties in basic variables and failure modes. In order to ...An efficient importance sampling algorithm is presented to analyze reliability of complex structural system with multiple failure modes and fuzzy-random uncertainties in basic variables and failure modes. In order to improve the sampling efficiency, the simulated annealing algorithm is adopted to optimize the density center of the importance sampling for each failure mode, and results that the more significant contribution the points make to fuzzy failure probability, the higher occurrence possibility the points are sampled. For the system with multiple fuzzy failure modes, a weighted and mixed importance sampling function is constructed. The contribution of each fuzzy failure mode to the system failure probability is represented by the appropriate factors, and the efficiency of sampling is improved furthermore. The variances and the coefficients of variation are derived for the failure probability estimations. Two examples are introduced to illustrate the rationality of the present method. Comparing with the direct Monte-Carlo method, the improved efficiency and the precision of the method are verified by the examples.展开更多
The present study proposes a stochastic simulation scheme to model reactive boundaries through a position jump process which can be readily implemented into the Inhomogeneous Stochastic Simulation Algorithm by modifyi...The present study proposes a stochastic simulation scheme to model reactive boundaries through a position jump process which can be readily implemented into the Inhomogeneous Stochastic Simulation Algorithm by modifying the propensity of the diffusive jump over the reactive boundary. As compared to the literature, the present approach does not require any correction factors for the propensity. Also, the current expression relaxes the constraint on the compartment size allowing the problem to be solved with a coarser grid and therefore saves considerable computational cost. The modified algorithm is then applied to simulate three reaction-diffusion systems with reactive boundaries.展开更多
Traditional parafoil homing usually uses a point as object. As the mobility of parafoil is limited by its glide ratio and wind, in some cases when the parafoil scatter area is large, or the glide ratio of parafoil is ...Traditional parafoil homing usually uses a point as object. As the mobility of parafoil is limited by its glide ratio and wind, in some cases when the parafoil scatter area is large, or the glide ratio of parafoil is small, the deviation of its landing point to object point will be arduous to control. Accordingly, during these situations, when parafoil is used in recovery of spacecraft or satellite, the landing area of parafoil can be set as a rectangle, and the object of parafoil can be set as a line segment. The thesis of this work is designing an algorithm for parafoil homing using line segment as object. The algorithm of wind velocity and direction calculation in different flying segments was also investigated. The algorithm designed navigates the parafoil to land into the predestined area and largely reduce the probability of recovery loads falling to unwanted area to damage houses and people.展开更多
The concepts of information fusion and the basic principles of neural networks are introduced. Neural net-works were introduced as a way of building an information fusion model in a coal mine monitoring system. This a...The concepts of information fusion and the basic principles of neural networks are introduced. Neural net-works were introduced as a way of building an information fusion model in a coal mine monitoring system. This assures the accurate transmission of the multi-sensor information that comes from the coal mine monitoring systems. The in-formation fusion mode was analyzed. An algorithm was designed based on this analysis and some simulation results were given. Finally,conclusions that could provide auxiliary decision making information to the coal mine dispatching officers were presented.展开更多
To improve the precision of the filling pressure curve of differential pressure casting controlled with PID controller,the model of differential pressure casting process is established and two pressure-difference cont...To improve the precision of the filling pressure curve of differential pressure casting controlled with PID controller,the model of differential pressure casting process is established and two pressure-difference control systems using PID algorithm and Dahlin algorithm are separately designed in MATLAB. The scheduled pressure curves controlled with PID algorithm and Dahlin algorithm,respectively,are comparatively simulated in MATLAB. The simulated pressure curves obtained show that the control precision with Dahlin algorithm is higher than that with PID algorithm in the differential pressure casting process,and it was further verified by production practice.展开更多
The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various ...The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.展开更多
The improved weighted-least-square model was used for state simulation of water distribution networks. And DFP algorithm was applied to get the model solution. In order to fit DFP algorithm,the initial model was trans...The improved weighted-least-square model was used for state simulation of water distribution networks. And DFP algorithm was applied to get the model solution. In order to fit DFP algorithm,the initial model was transformed into a non-constrained optimization problem using mass conservation. Then,through one dimensional optimization and scale matrix establishment,the feasible direction of iteration was obtained,and the values of state variables could be calculated. After several iterations,the optimal estimates of state variables were worked out and state simulation of water distribution networks was achieved as a result. A program of DFP algorithm is developed with Delphi 7 for verification. By running on a designed network,which is composed of 55 nodes,94 pipes and 40 loops,it is proved that DFP algorithm can quickly get the convergence. After 36 iterations,the root mean square of all nodal head errors is reduced by 90.84% from 5.57 to 0.51 m,and the maximum error is only 1.30 m. Compared to Marquardt algorithm,the procedure of DFP algorithm is more stable,and the initial values have less influences on calculation accuracy. Therefore,DFP algorithm can be used for real-time simulation of water distribution networks.展开更多
基金This work is supported by the NationalNatural Science Foundation of China(No.52075350)the Major Science and Technology Projects of Sichuan Province(No.2022ZDZX0001)the Special City-University Strategic Cooperation Project of Sichuan University and Zigong Municipality(No.2021CDZG-3).
文摘Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.
文摘A class of hybrid algorithms of real-time simulation based on evaluation of non-integerstep right-hand side function are presented in this paper. And some results of the convergence and stability of the algorithms are given. Using the class of algorithms, evaluation for the right-hand side function is needed once in every integration-step. Moreover, comparing with the other methods with the same amount of work, their numerical stability regions are larger and the method errors are smaller, and the numerical experiments show that the algorithms are very effective.
基金This project was supported by the National Natural Science Foundation of China (No. 19871080).
文摘In this paper a class of real-time parallel modified Rosenbrock methods of numerical simulation is constructed for stiff dynamic systems on a multiprocessor system, and convergence and numerical stability of these methods are discussed. A-stable real-time parallel formula of two-stage third-order and A(α)-stable real-time parallel formula with o ≈ 89.96° of three-stage fourth-order are particularly given. The numerical simulation experiments in parallel environment show that the class of algorithms is efficient and applicable, with greater speedup.
基金supported by the National Natural Science Foundation of China(Grant No.12005065)the Guangdong Basic and Applied Basic Research Fund(Grant No.2021A1515010317)。
文摘The quantum critical regime marks a zone in the phase diagram where quantum fluctuation around the critical point plays a significant role at finite temperatures.While it is of great physical interest,simulation of the quantum critical regime can be difficult on a classical computer due to its intrinsic complexity.Herein,we propose a variational approach,which minimizes the variational free energy,to simulate and locate the quantum critical regime on a quantum computer.The variational quantum algorithm adopts an ansatz by performing an unitary operator on a product of a single-qubit mixed state,in which the entropy can be analytically obtained from the initial state,and thus the free energy can be accessed conveniently.With numeral simulation,using the one-dimensional Kitaev model as a demonstration we show that the quantum critical regime can be identified by accurately evaluating the temperature crossover line.Moreover,the dependencies of both the correlation length and the phase coherence time with temperature are evaluated for the thermal states.Our work suggests a practical way as well as a first step for investigating quantum critical systems at finite temperatures on quantum devices with few qubits.
基金the Shandong Province Key Research and Development Program under Grant No.2021SFGC0601.
文摘This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.
基金Project supported by the State Key Development Program for Basic Research of China(Grant No.2017YFA0304300)the National Natural Science Foundation of China(Grant Nos.11934018,11747601,and 11975294)+4 种基金Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB28000000)Scientific Instrument Developing Project of Chinese Academy of Sciences(Grant No.YJKYYQ20200041)Beijing Natural Science Foundation(Grant No.Z200009)the Key-Area Research and Development Program of Guangdong Province,China(Grant No.2020B0303030001)Chinese Academy of Sciences(Grant No.QYZDB-SSW-SYS032)。
文摘Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental preparations of Gibbs states and excited states of Heisenberg X X and X X Z models by using a 5-qubit programmable superconducting processor.In the experiments,we apply a hybrid quantum–classical algorithm to generate finite temperature states with classical probability models and variational quantum circuits.We reveal that the Hamiltonians can be fully diagonalized with optimized quantum circuits,which enable us to prepare excited states at arbitrary energy density.We demonstrate that the approach has a self-verifying feature and can estimate fundamental thermal observables with a small statistical error.Based on numerical results,we further show that the time complexity of our approach scales polynomially in the number of qubits,revealing its potential in solving large-scale problems.
基金funded by the National Natural Science Foundation of China(42174131)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03).
文摘In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.
文摘In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.
文摘Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN).
基金Project(61273187)supported by the National Natural Science Foundation of ChinaProject(61321003)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China
文摘Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.
基金This project is supported by National Natural Science Foundation of China (No.10572117)Aerospace Science Foundation of China(No.N3CH0502,No.N5CH0001)Provincial Natural Science Foundation of Shanxi, China(No.N3CS0501).
文摘An efficient importance sampling algorithm is presented to analyze reliability of complex structural system with multiple failure modes and fuzzy-random uncertainties in basic variables and failure modes. In order to improve the sampling efficiency, the simulated annealing algorithm is adopted to optimize the density center of the importance sampling for each failure mode, and results that the more significant contribution the points make to fuzzy failure probability, the higher occurrence possibility the points are sampled. For the system with multiple fuzzy failure modes, a weighted and mixed importance sampling function is constructed. The contribution of each fuzzy failure mode to the system failure probability is represented by the appropriate factors, and the efficiency of sampling is improved furthermore. The variances and the coefficients of variation are derived for the failure probability estimations. Two examples are introduced to illustrate the rationality of the present method. Comparing with the direct Monte-Carlo method, the improved efficiency and the precision of the method are verified by the examples.
文摘The present study proposes a stochastic simulation scheme to model reactive boundaries through a position jump process which can be readily implemented into the Inhomogeneous Stochastic Simulation Algorithm by modifying the propensity of the diffusive jump over the reactive boundary. As compared to the literature, the present approach does not require any correction factors for the propensity. Also, the current expression relaxes the constraint on the compartment size allowing the problem to be solved with a coarser grid and therefore saves considerable computational cost. The modified algorithm is then applied to simulate three reaction-diffusion systems with reactive boundaries.
基金Project(61503077)supported by the National Natural Science Foundation of ChinaProject(BK20130628)supported by the Jiangsu Natural Science Foundation,China
文摘Traditional parafoil homing usually uses a point as object. As the mobility of parafoil is limited by its glide ratio and wind, in some cases when the parafoil scatter area is large, or the glide ratio of parafoil is small, the deviation of its landing point to object point will be arduous to control. Accordingly, during these situations, when parafoil is used in recovery of spacecraft or satellite, the landing area of parafoil can be set as a rectangle, and the object of parafoil can be set as a line segment. The thesis of this work is designing an algorithm for parafoil homing using line segment as object. The algorithm of wind velocity and direction calculation in different flying segments was also investigated. The algorithm designed navigates the parafoil to land into the predestined area and largely reduce the probability of recovery loads falling to unwanted area to damage houses and people.
基金project BK2001073 supported by Jiangsu Province Natural Science Foundation
文摘The concepts of information fusion and the basic principles of neural networks are introduced. Neural net-works were introduced as a way of building an information fusion model in a coal mine monitoring system. This assures the accurate transmission of the multi-sensor information that comes from the coal mine monitoring systems. The in-formation fusion mode was analyzed. An algorithm was designed based on this analysis and some simulation results were given. Finally,conclusions that could provide auxiliary decision making information to the coal mine dispatching officers were presented.
文摘To improve the precision of the filling pressure curve of differential pressure casting controlled with PID controller,the model of differential pressure casting process is established and two pressure-difference control systems using PID algorithm and Dahlin algorithm are separately designed in MATLAB. The scheduled pressure curves controlled with PID algorithm and Dahlin algorithm,respectively,are comparatively simulated in MATLAB. The simulated pressure curves obtained show that the control precision with Dahlin algorithm is higher than that with PID algorithm in the differential pressure casting process,and it was further verified by production practice.
基金in part supported by the Key Research and Development Project of Hubei Province(Nos.2020BAB1141,2023BAB094)the Key Project of Science and Technology Research ProgramofHubei Educational Committee(No.D20211402)+1 种基金the Teaching Research Project of Hubei University of Technology(No.XIAO2018001)the Project of Xiangyang Industrial Research Institute of Hubei University of Technology(No.XYYJ2022C04).
文摘The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.
基金Project(IRT0853) supported by Changjiang Scholars and Innovative Research Team in UniversityProject(DB03086) supported by Talents Fund of Xi’an University of Architecture and TechnologyProject(50978213) supported by National Natural Science Foundation
文摘The improved weighted-least-square model was used for state simulation of water distribution networks. And DFP algorithm was applied to get the model solution. In order to fit DFP algorithm,the initial model was transformed into a non-constrained optimization problem using mass conservation. Then,through one dimensional optimization and scale matrix establishment,the feasible direction of iteration was obtained,and the values of state variables could be calculated. After several iterations,the optimal estimates of state variables were worked out and state simulation of water distribution networks was achieved as a result. A program of DFP algorithm is developed with Delphi 7 for verification. By running on a designed network,which is composed of 55 nodes,94 pipes and 40 loops,it is proved that DFP algorithm can quickly get the convergence. After 36 iterations,the root mean square of all nodal head errors is reduced by 90.84% from 5.57 to 0.51 m,and the maximum error is only 1.30 m. Compared to Marquardt algorithm,the procedure of DFP algorithm is more stable,and the initial values have less influences on calculation accuracy. Therefore,DFP algorithm can be used for real-time simulation of water distribution networks.