With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex ele...With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance.Genetic algorithm(GA) and particle swarm optimization(PSO)are used to simulate the intelligent optimization of interruptedsampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.展开更多
Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.Th...Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings.展开更多
To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） alg...To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of QPSO algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.展开更多
A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules ...A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of The simulat two linguistic variables with the degree of certainty and the storage structure of rule base was described. on results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5 %.展开更多
To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location al...To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
文摘With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance.Genetic algorithm(GA) and particle swarm optimization(PSO)are used to simulate the intelligent optimization of interruptedsampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.
基金supported by the National Natural Science Foundation of China(71871219).
文摘Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings.
基金supported by the National Natural Science Foundation of China(60771063).
文摘To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of QPSO algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.
基金Project(50374079) supported bythe National Natural Science Foundation of China project(2002cB312200) supported bythe State Key Fundamental Research and Development Programof China
文摘A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of The simulat two linguistic variables with the degree of certainty and the storage structure of rule base was described. on results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5 %.
基金National Natural Science Foundation of China(62202477)。
文摘To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
