Simulation optimization is a rapidly growing research field,fueled by advances in computational technology.These advances have made it possible to solve complex stochastic optimization problems through simulation.Whil...Simulation optimization is a rapidly growing research field,fueled by advances in computational technology.These advances have made it possible to solve complex stochastic optimization problems through simulation.While most published articles focus on single-objective optimization,multi-objective optimization is gaining prominence,allowing the approach of real-world problems that present multiple,conflicting objectives.In this context,the objective of this article was to conduct a systematic literature review to identify articles that present solution methods for Multi-Objective Simulation Optimization(MOSO)problems.The focus was on practical optimization applications in conjunction with Discrete Event Simulation(DES)models,aiming to identify the main aspects of the problems addressed,the methods used,and research opportunities,contributing to future projects.By exploring the characteristics of MOSO problems associated with DES and the applied solution methods,this article innovatively presents a guide to help professionals improve their decision-making processes and assist researchers in developing new research.展开更多
After-sale service plays an essential role in the electronics retail industry,where providers must supply the required repair parts to consumers during the product warranty period.The rapid evolution of electronic pro...After-sale service plays an essential role in the electronics retail industry,where providers must supply the required repair parts to consumers during the product warranty period.The rapid evolution of electronic products prevents part suppliers from maintaining continuous production,making it impossible to supply spare parts consistently during the warranty periods and requiring the providers to purchase all necessary spare parts on Last Time Buy(LTB).The uncertainty of customer demand in spare parts brings out difficulties to maintain optimal spare parts inventory.In this paper,we address the challenge of forecasting spare parts demand and optimizing the purchase volumes of spare parts during the regular monthly replenishment period and LTB.First,the problem is well defined and formulated based on the dynamic economic lotsize model.Second,a transfer function model is constructed between historical demand values and product sales,aiming to identify the length of warranty period and forecast the spare part demands.In addition,the linear Model Predictive Control(MPC)scheme is adopted to optimize the purchase volumes of spare part considering the inaccuracy in the demand forecasts.A real-world case considering different categories of spare parts consumption is studied.The results demonstrate that our proposed algorithm outperforms other algorithms in terms of forecasting accuracy and the inventory cost.展开更多
Complex System Modeling and Simulation,an academic journal sponsored by Tsinghua University,published by Tsinghua University Press(TUP)and hosted in IEEE Xplore,is published quarterly.This journal aims at presenting t...Complex System Modeling and Simulation,an academic journal sponsored by Tsinghua University,published by Tsinghua University Press(TUP)and hosted in IEEE Xplore,is published quarterly.This journal aims at presenting the up-to-date scientific achievements with high creativity and great significance in the fields of complex system modeling,simulation,optimization and control.Contributions all over the world are welcome.展开更多
Hydrogen production from wind-solar generation is of great importance for consuming renewable energy and it is meeting industrial hydrogen demand.In this paper,the modelling of the off-grid hydrogen production system ...Hydrogen production from wind-solar generation is of great importance for consuming renewable energy and it is meeting industrial hydrogen demand.In this paper,the modelling of the off-grid hydrogen production system from wind-solar generation and the simulation of its operating characteristics are investigated.Firstly,the network architecture and hierarchical control architecture of the off-grid hydrogen generation system are designed with the goal of efficiently utilising wind-solar generation output.Then,the components of the off-grid hydrogen generation system are characterised and modelled.Finally,the operating characteristics of the hydrogen production system under three operating conditions,such as hydrogen system startup,wind power fluctuation,and electrolyzer partial failure are simulated and analyzed,revealing the ability of the alkaline electrolytic water hydrogen production system to respond to the fluctuation of wind and solar power.展开更多
This paper proposes a novel 5D hyperchaotic memristive system based on the Sprott-C system configuration,which greatly improves the complexity of the system to be used for secure communication and signal processing.A ...This paper proposes a novel 5D hyperchaotic memristive system based on the Sprott-C system configuration,which greatly improves the complexity of the system to be used for secure communication and signal processing.A critical aspect of this research work is the introduction of a flux-controlled memristor that exhibits chaotic behavior and dynamic responses of the system.To this respect,detailed mathematical modeling and numerical simulations about the stability of the system’s equilibria,bifurcations,and hyperchaotic dynamics were conducted and showed a very wide variety of behaviors of great potential in cryptographic applications and secure data transmission.Then,the flexibility and efficiency of the real-time operating environment were demonstrated,and the system was actually implemented on a field-programmable gate array(FPGA)hardware platform.A prototype that confirms the theoretical framework was presented,providing new insights for chaotic systems with practical significance.Finally,we conducted National Institute of Standards and Technology(NIST)testing on the proposed 5D hyperchaotic memristive system,and the results showed that the system has good randomness.展开更多
In the field of fault diagnosis for rolling bearings under variable working conditions,significant progress has been made using methods based on unsupervised domain adaptation(UDA).However,most existing UDA methods pr...In the field of fault diagnosis for rolling bearings under variable working conditions,significant progress has been made using methods based on unsupervised domain adaptation(UDA).However,most existing UDA methods primarily achieve identification by directly aligning the distributions of the source and target domains,often overlooking the relevance of samples between different domains,which may result in incomplete extraction of deep features and alignment of feature distributions.Therefore,this study proposes a novel domain adaptation network based on Gaussian prior distributions,aiming at solving the challenges of cross working conditions bearing fault diagnosis.The method consists of a feature mining module and an adversarial domain adaptation module.The former effectively extracts deep features by stacking multiple residual networks(Resnet),while the latter employs an indirect latent alignment strategy,using Gaussian prior distributions in the latent feature space to indirectly align the feature distributions of the source and target domains,achieving more precise feature alignment.In addition,an adaptive factor is introduced to dynamically assess the method’s transfer and discriminative capabilities.Experimental data from two bearing systems validate that the method can effectively transfer source domain knowledge to the target domain,confirming its effectiveness.展开更多
In real production,machines are operated by workers,and the constraints of worker flexibility should be considered.The flexible job shop scheduling problem with both machine and worker resources(DRCFJSP)has become a r...In real production,machines are operated by workers,and the constraints of worker flexibility should be considered.The flexible job shop scheduling problem with both machine and worker resources(DRCFJSP)has become a research hotspot in recent years.In this paper,DRCFJSP with the objective of minimizing the makespan is studied,and it should solve three sub-problems:machine allocation,worker allocation,and operations sequencing.To solve DRCFJSP,a novel hybrid algorithm(CEAM-CP)of cooperative evolutionary algorithm with multiple populations(CEAM)and constraint programming(CP)is proposed.Specifically,the CEAM-CP algorithm is comprised of two main stages.In the first stage,CEAM is used based on three-layer encoding and full active decoding.Moreover,CEAM has three populations,each of which corresponds to one layer encoding and determines one sub-problem.Moreover,each population evolves cooperatively by multiple cross operations.To further improve the solution quality obtained by CEAM,CP is adopted in the second stage.Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations,CP,and CEAM-CP.Most importantly,the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances.展开更多
Affected by the limited interchange spacing,the operational risk of vehicles in expressway small-spacing interchanges(SSIs)is more complex compared to other interchanges.In this study,unmanned aerial vehicle(UAV)measu...Affected by the limited interchange spacing,the operational risk of vehicles in expressway small-spacing interchanges(SSIs)is more complex compared to other interchanges.In this study,unmanned aerial vehicle(UAV)measurements were integrated with joint simulation data to explore the risk characteristics of SSIs with the help of traffic conflict theory.Seven traffic flow parameters,including mainline traffic volume,were selected to evaluate their impact on traffic conflicts.The distribution of four traffic conflict indicators,such as time to collision(TTC),was analyzed,and their severity was categorized using cumulative frequency analysis and minibatch K-means clustering.By varying the spacing,the study scrutinized trends in traffic conflicts,emphasizing the influence of various traffic flow parameters,distinctions in conflict indicators,and the ratio of severe conflicts to total conflicts.Additionally,an analysis of the spatial distribution of severe conflicts was conducted.The results suggested that traffic conflicts in SSIs are influenced by multiple factors,with mainline and entry traffic volumes being the most significant.Heavy vehicle proportions and entry ramp speeds had notable effects under certain spacing conditions.Considerable variations were observed in conflict indicators across different spacings,with the maximum conflict speed being the most affected by spacing,while TTC was the least.As spacing increased,the proportion of severe conflicts decreased,with severe TTC dropping from 18%to 10%.High-density conflict zones were identified near merging points in the second and third lanes.With larger spacing,the conflict zone range narrowed while the density of conflict points intensified.展开更多
In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objec...In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objective to minimize the makespan.A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA.In the evolution of DHICCA,population individuals are endowed with heterogeneous identities according to their quality,including superior individuals,ordinary individuals,and inferior individuals,which serve local exploitation,global exploration,and diversified restart,respectively.Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials,identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way.This is important to use limited population resources to solve complex optimization problems.Specifically,exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood,destruction-construction,and gene targeting.Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy.In addition,restart is performed on inferior individuals to introduce new evolutionary individuals to the population.After the cooperative co-evolution,all individuals with different identities are merged as a population again,and their identities are dynamically adjusted by new evaluation.The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms.The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving the DLSFSP.展开更多
The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,ma...The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,machine preventive maintenance is required to prevent machine breakdowns.Therefore,this paper studies the DPFSP with preventive maintenance(PM/DPFSP)aiming at minimizing the total flowtime.For solving the problem,a discrete gray wolf optimization algorithm with restart mechanism(DGWO_RM)is proposed.In the initialization phase,a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution.Next,four local search strategies are proposed for further enhancing the exploitation capability.Furthermore,a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution,thereby ensuring a broader exploration of potential solutions.Finally,comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM.The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.展开更多
Recently,deeplearning based fingerprint localization has attracted significant interest due to its simplicity in implementation and effectiveness in complex multipath environments,especially for the Internet of Things...Recently,deeplearning based fingerprint localization has attracted significant interest due to its simplicity in implementation and effectiveness in complex multipath environments,especially for the Internet of Things(loT)devices in multiple-input multiple-output(MiMO)-orthogonal frequency-division multiplexing(OFDM)system.However,the huge amount of training data collection has become a challenge,which increases the labor burden of fingerprint localization heavily and hinders its large-scale implementation.In this paper,we propose a novel fingerprint localization system,termed as SiamResNet,which can be trained only on the radio map by contrastive self-supervised learning without the need for any other additional data.To be more specific,we first model the fingerprint localization problem as a dictionary look-up task.Subsequently,a channel fingerprint capturing the multipath angle and delay of wireless propagation is introduced,which exhibits excellent uniqueness,stability,and distinguishability.Meanwhile,we propose the corresponding data augmentation strategy to ensure data diversity when generating the training data from the radio map.Thus,the cost of data collection for training can be significantly reduced.Lastly,the Siamese architecture based SiamResNet is applied for location estimation,which can comprehensively extract the features of fingerprints and accurately compare the similarity of any fingerprint to the radio map in the representation space.The performance of the proposed localization method is validated through extensive simulations with a ray-tracing channel model,which demonstrates promising localization accuracy for our SiamResNet with reduced training costs.展开更多
Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these met...Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these methods use attention mechanisms and their variants,which have room for further improvement in the speed of solving MOCO problems.In this paper,following the idea of decomposition strategy and neural combinatorial optimization,a novel fast-solving model for MOCO based on retention is proposed.A brand new calculation of retention is proposed,causal masking and exponential decay are deprecated in retention,so that our model could better solve MOCO problems.During model training,a parallel computation of retention is applied,allowing for fast parallel training.When using the model to solve MOCO problems,a recurrent computation of retention is applied,enabling quicker problem-solving.In order to make our model more practical and flexible,a preference-based retention decoder is proposed,which allows generating approximate Pareto solutions for any trade-off preferences directly.An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO.Experimental results show that,while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.展开更多
Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufact...Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion time.To address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is proposed.In NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution process.The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations.In addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is adopted.Multiple local search operators based on ideal point are proposed to find a better local solution.To improve the global exploration ability of the algorithm,a dual population restart mechanism is adopted.Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.展开更多
This paper introduces a novel color image encryption algorithm based on a five-dimensional continuous memristor hyperchaotic system(5D-MHS),combined with a two-dimensional Salomon map and an optimized Arnold transform...This paper introduces a novel color image encryption algorithm based on a five-dimensional continuous memristor hyperchaotic system(5D-MHS),combined with a two-dimensional Salomon map and an optimized Arnold transform.Firstly,convert the test image to a 2D pixel matrix then processed in blocks,and each block of the pixel matrix is permuted with chaotic sequences generated by 5D-MHS and 2D Salomon map.Then,the permuted image is permuted for three rounds with the optimized Arnold algorithm.Finally,one of the chaotic sequences generated by 5D-MHS is employed to diffuse the permuted image to obtain the final ciphertext image.In this paper,several pseudo-random sequences are generated and mixed in the permutation stage to achieve higher security.The algorithm achieves a key space of 2472,the information entropy of the ciphertext image for the color image is 7.9998,number of pixels change rate(NPCR)and unified average changing intensity(UACI)reached 99.6131%and 33.4361%,respectively,and the correlation between pixels is close to 0.The simulation results show that the encryption algorithm is efficient and the key system is secure.展开更多
The present paper attempts to design an adaptive multi-model predictive control strategy for strongly nonlinear or switched systems with various operating points.The proposed control system guarantees the feasibility ...The present paper attempts to design an adaptive multi-model predictive control strategy for strongly nonlinear or switched systems with various operating points.The proposed control system guarantees the feasibility and the asymptotic stability of the closed-loop system,considering various challenges such as inherent uncertainties in the local models constituting the model bank,limited prediction/control horizons,and set point changes.To this end,four fundamental challenges in this area,namely guaranteeing feasibility throughout the region assigned to each subspace,ensuring asymptotic stability in each subspace considering the inherent uncertainties of the local models,guaranteeing feasibility and asymptotic stability during changes in the set point and switching between the subspaces,are addressed.By introducing transferring mode concept,this paper presents a novel method for guaranteeing the feasibility and stability of the switched systems without the need for increasing the prediction/control horizons or decreasing the size of the feasibility region.The proposed control structure uses a supervisor algorithm along with a soft-switching technique.The supervisor algorithm is responsible for determining the suitable local model/controller pair,determining the operational mode of the control system,managing the soft switching,and specifying the control objectives in accordance with the defined set point.The efficiency of the proposed control strategy is demonstrated by simulating a Continuous Stirred Tank Reactor(CSTR)as the controlled system.Based on the results,the proposed controller is able to guarantee the feasibility and stability of highly nonlinear and switched systems in a wide operating region under set point changes and uncertainties in the local models.展开更多
This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy consumption.First,the Bi-HFSP_CS is formali...This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy consumption.First,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical model.Second,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_CS.Then,fourteen local search operators are employed to search for better solutions.Two different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process.Finally,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different problems.The experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned problems.This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.展开更多
As the global economy develops and people's awareness of environmental protection increases,the efficient scheduling of production lines in workshops has received more and more attention.However,there is very litt...As the global economy develops and people's awareness of environmental protection increases,the efficient scheduling of production lines in workshops has received more and more attention.However,there is very little research focusing on distributed scheduling for heterogeneous factories.This study addresses a multi-objective distributed heterogeneous permutation flow shop scheduling problem with sequence-dependent setup times(DHPFSP-SDST).The objective is to optimize the trade-off between the maximum completion time(Makespan)and total energy consumption.First,to describe the concerned problems,we establish a mathematical model.Second,we use the artificial bee colony(ABC)algorithm to optimize the two objectives,incorporating five local search strategies tailored to the problem characteristics to enhance the algorithm's performance.Third,to improve the convergence speed of the algorithm,a Q-learning based strategy is designed to select the appropriated local search operator during iterations.Finally,based on experiments conducted on 72 instances,statistical analysis and discussions show that the Q-learning based ABC algorithm can effectively solve the problems better than its peers.展开更多
The lack of data is constraining research on the bistatic ballistic target(BT).In the bistatic radar system,the angle between the target axis and the radar line of sight(RLOS)is generally large,which means that the sp...The lack of data is constraining research on the bistatic ballistic target(BT).In the bistatic radar system,the angle between the target axis and the radar line of sight(RLOS)is generally large,which means that the specular scattering center(SSC)is likely to be present with the target's micro-motion.This results in a brief flash in the time-frequency representation(TFR)but has been less frequently reported.We propose a micro-motion echo simulation method with SSC by calculating its position vector and modeling its visible coefficient.Furthermore,the model with four estimated parameters is utilized to optimize the simulated TFR.The simulation results intuitively and numerically agree well with the TFR obtained by electromagnetic calculation.展开更多
The human brain is composed of a large number of neurons that work together to process the generation,transmission,reception,and processing of information.The topological structure of the human brain has small-world c...The human brain is composed of a large number of neurons that work together to process the generation,transmission,reception,and processing of information.The topological structure of the human brain has small-world characteristics,and the synchronization and neuron firing are influenced by the electromagnetic field.In this paper,we use four-stable discrete memristors to simulate the external electromagnetic field,and construct a memristive small-world neural network(MSNN)model based on Rulkov neurons,and conduct numerical simulations.We have found that the MSNN exhibits multiple coexisting behaviors of synchronous,asynchronous,and chimeric states under different initial conditions of the discrete memristors.At the same time,changing the strength of electromagnetic induction can affect the synchronization performance of the MSNN.Finally,we find that increasing the electromagnetic induction strength can enhance the neuron firing action potential.展开更多
Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse fields.One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it ...Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse fields.One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it is a difficult and challenging task in numerical computation.In recent years,Intelligent Optimization Algorithms(IOAs)have shown to be particularly effective in solving NEs.This paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of NEs.This paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation techniques.Then,solving NEs with IOAs is reviewed,followed by the benchmark functions and the performance comparison of several state-of-the-art algorithms.Finally,this paper points out the challenges and some possible open issues for solving NEs.展开更多
文摘Simulation optimization is a rapidly growing research field,fueled by advances in computational technology.These advances have made it possible to solve complex stochastic optimization problems through simulation.While most published articles focus on single-objective optimization,multi-objective optimization is gaining prominence,allowing the approach of real-world problems that present multiple,conflicting objectives.In this context,the objective of this article was to conduct a systematic literature review to identify articles that present solution methods for Multi-Objective Simulation Optimization(MOSO)problems.The focus was on practical optimization applications in conjunction with Discrete Event Simulation(DES)models,aiming to identify the main aspects of the problems addressed,the methods used,and research opportunities,contributing to future projects.By exploring the characteristics of MOSO problems associated with DES and the applied solution methods,this article innovatively presents a guide to help professionals improve their decision-making processes and assist researchers in developing new research.
基金supported by the National Key R&D Program of China(No.2021YFB3300400)National Natural Science Foundation of China(No.62203049).
文摘After-sale service plays an essential role in the electronics retail industry,where providers must supply the required repair parts to consumers during the product warranty period.The rapid evolution of electronic products prevents part suppliers from maintaining continuous production,making it impossible to supply spare parts consistently during the warranty periods and requiring the providers to purchase all necessary spare parts on Last Time Buy(LTB).The uncertainty of customer demand in spare parts brings out difficulties to maintain optimal spare parts inventory.In this paper,we address the challenge of forecasting spare parts demand and optimizing the purchase volumes of spare parts during the regular monthly replenishment period and LTB.First,the problem is well defined and formulated based on the dynamic economic lotsize model.Second,a transfer function model is constructed between historical demand values and product sales,aiming to identify the length of warranty period and forecast the spare part demands.In addition,the linear Model Predictive Control(MPC)scheme is adopted to optimize the purchase volumes of spare part considering the inaccuracy in the demand forecasts.A real-world case considering different categories of spare parts consumption is studied.The results demonstrate that our proposed algorithm outperforms other algorithms in terms of forecasting accuracy and the inventory cost.
文摘Complex System Modeling and Simulation,an academic journal sponsored by Tsinghua University,published by Tsinghua University Press(TUP)and hosted in IEEE Xplore,is published quarterly.This journal aims at presenting the up-to-date scientific achievements with high creativity and great significance in the fields of complex system modeling,simulation,optimization and control.Contributions all over the world are welcome.
基金supported by the National Key Research and Development Program of China under the project"Key Technology and Demonstration of Hybrid Alkaline-ProtonExchange Membrane Hydrogen Generation System at 10 MW Scale"(No.2023 YFB4004600).
文摘Hydrogen production from wind-solar generation is of great importance for consuming renewable energy and it is meeting industrial hydrogen demand.In this paper,the modelling of the off-grid hydrogen production system from wind-solar generation and the simulation of its operating characteristics are investigated.Firstly,the network architecture and hierarchical control architecture of the off-grid hydrogen generation system are designed with the goal of efficiently utilising wind-solar generation output.Then,the components of the off-grid hydrogen generation system are characterised and modelled.Finally,the operating characteristics of the hydrogen production system under three operating conditions,such as hydrogen system startup,wind power fluctuation,and electrolyzer partial failure are simulated and analyzed,revealing the ability of the alkaline electrolytic water hydrogen production system to respond to the fluctuation of wind and solar power.
基金supported by the Scientific Research Fund of Hunan Provincial Education Department(No.24A0248)Hefei Minglong Electronic Technology Co.,Ltd.(Nos.2024ZKHX293,2024ZKHX294,and 2024ZKHX295).
文摘This paper proposes a novel 5D hyperchaotic memristive system based on the Sprott-C system configuration,which greatly improves the complexity of the system to be used for secure communication and signal processing.A critical aspect of this research work is the introduction of a flux-controlled memristor that exhibits chaotic behavior and dynamic responses of the system.To this respect,detailed mathematical modeling and numerical simulations about the stability of the system’s equilibria,bifurcations,and hyperchaotic dynamics were conducted and showed a very wide variety of behaviors of great potential in cryptographic applications and secure data transmission.Then,the flexibility and efficiency of the real-time operating environment were demonstrated,and the system was actually implemented on a field-programmable gate array(FPGA)hardware platform.A prototype that confirms the theoretical framework was presented,providing new insights for chaotic systems with practical significance.Finally,we conducted National Institute of Standards and Technology(NIST)testing on the proposed 5D hyperchaotic memristive system,and the results showed that the system has good randomness.
文摘In the field of fault diagnosis for rolling bearings under variable working conditions,significant progress has been made using methods based on unsupervised domain adaptation(UDA).However,most existing UDA methods primarily achieve identification by directly aligning the distributions of the source and target domains,often overlooking the relevance of samples between different domains,which may result in incomplete extraction of deep features and alignment of feature distributions.Therefore,this study proposes a novel domain adaptation network based on Gaussian prior distributions,aiming at solving the challenges of cross working conditions bearing fault diagnosis.The method consists of a feature mining module and an adversarial domain adaptation module.The former effectively extracts deep features by stacking multiple residual networks(Resnet),while the latter employs an indirect latent alignment strategy,using Gaussian prior distributions in the latent feature space to indirectly align the feature distributions of the source and target domains,achieving more precise feature alignment.In addition,an adaptive factor is introduced to dynamically assess the method’s transfer and discriminative capabilities.Experimental data from two bearing systems validate that the method can effectively transfer source domain knowledge to the target domain,confirming its effectiveness.
基金supported by the Funds for the National Natural Science Foundation of China(Nos.52205529 and 62303204)Natural Science Foundation of Shandong Province(Nos.ZR2021QE195 and ZR2021QF036)+2 种基金Youth Innovation Team Program of Shandong Higher Education Institution(No.2023KJ206)Guangyue。Youth Scholar Innovation Talent Program support received from Liaocheng University(No.LCUGYTD2022-03)Foundation of Young Talent of Lifting engineering for Science and Technology in Shandong,China(No.SDAST2024QTA074).
文摘In real production,machines are operated by workers,and the constraints of worker flexibility should be considered.The flexible job shop scheduling problem with both machine and worker resources(DRCFJSP)has become a research hotspot in recent years.In this paper,DRCFJSP with the objective of minimizing the makespan is studied,and it should solve three sub-problems:machine allocation,worker allocation,and operations sequencing.To solve DRCFJSP,a novel hybrid algorithm(CEAM-CP)of cooperative evolutionary algorithm with multiple populations(CEAM)and constraint programming(CP)is proposed.Specifically,the CEAM-CP algorithm is comprised of two main stages.In the first stage,CEAM is used based on three-layer encoding and full active decoding.Moreover,CEAM has three populations,each of which corresponds to one layer encoding and determines one sub-problem.Moreover,each population evolves cooperatively by multiple cross operations.To further improve the solution quality obtained by CEAM,CP is adopted in the second stage.Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations,CP,and CEAM-CP.Most importantly,the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances.
基金supported in part by the National Natural Science Foundation of China(No.52172340).
文摘Affected by the limited interchange spacing,the operational risk of vehicles in expressway small-spacing interchanges(SSIs)is more complex compared to other interchanges.In this study,unmanned aerial vehicle(UAV)measurements were integrated with joint simulation data to explore the risk characteristics of SSIs with the help of traffic conflict theory.Seven traffic flow parameters,including mainline traffic volume,were selected to evaluate their impact on traffic conflicts.The distribution of four traffic conflict indicators,such as time to collision(TTC),was analyzed,and their severity was categorized using cumulative frequency analysis and minibatch K-means clustering.By varying the spacing,the study scrutinized trends in traffic conflicts,emphasizing the influence of various traffic flow parameters,distinctions in conflict indicators,and the ratio of severe conflicts to total conflicts.Additionally,an analysis of the spatial distribution of severe conflicts was conducted.The results suggested that traffic conflicts in SSIs are influenced by multiple factors,with mainline and entry traffic volumes being the most significant.Heavy vehicle proportions and entry ramp speeds had notable effects under certain spacing conditions.Considerable variations were observed in conflict indicators across different spacings,with the maximum conflict speed being the most affected by spacing,while TTC was the least.As spacing increased,the proportion of severe conflicts decreased,with severe TTC dropping from 18%to 10%.High-density conflict zones were identified near merging points in the second and third lanes.With larger spacing,the conflict zone range narrowed while the density of conflict points intensified.
基金supported by the National Natural Science Foundation of China(No.62003258)Natural Science Foundation of Hebei Province(No.F2024204007)Projection of State Key Laboratory for Manufacturing Systems Engineering of Xi’an Jiaotong University(No.sklms 2023002).
文摘In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objective to minimize the makespan.A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA.In the evolution of DHICCA,population individuals are endowed with heterogeneous identities according to their quality,including superior individuals,ordinary individuals,and inferior individuals,which serve local exploitation,global exploration,and diversified restart,respectively.Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials,identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way.This is important to use limited population resources to solve complex optimization problems.Specifically,exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood,destruction-construction,and gene targeting.Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy.In addition,restart is performed on inferior individuals to introduce new evolutionary individuals to the population.After the cooperative co-evolution,all individuals with different identities are merged as a population again,and their identities are dynamically adjusted by new evaluation.The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms.The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving the DLSFSP.
基金supported by the National Natural Science Foundation of China(Nos.62473186 and 62273221)Natural Science Foundation of Shandong Province(No.ZR2024MF017)Discipline with Strong Characteristics of Liaocheng University Intelligent Science and Technology(No.319462208).
文摘The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,machine preventive maintenance is required to prevent machine breakdowns.Therefore,this paper studies the DPFSP with preventive maintenance(PM/DPFSP)aiming at minimizing the total flowtime.For solving the problem,a discrete gray wolf optimization algorithm with restart mechanism(DGWO_RM)is proposed.In the initialization phase,a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution.Next,four local search strategies are proposed for further enhancing the exploitation capability.Furthermore,a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution,thereby ensuring a broader exploration of potential solutions.Finally,comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM.The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.
文摘Recently,deeplearning based fingerprint localization has attracted significant interest due to its simplicity in implementation and effectiveness in complex multipath environments,especially for the Internet of Things(loT)devices in multiple-input multiple-output(MiMO)-orthogonal frequency-division multiplexing(OFDM)system.However,the huge amount of training data collection has become a challenge,which increases the labor burden of fingerprint localization heavily and hinders its large-scale implementation.In this paper,we propose a novel fingerprint localization system,termed as SiamResNet,which can be trained only on the radio map by contrastive self-supervised learning without the need for any other additional data.To be more specific,we first model the fingerprint localization problem as a dictionary look-up task.Subsequently,a channel fingerprint capturing the multipath angle and delay of wireless propagation is introduced,which exhibits excellent uniqueness,stability,and distinguishability.Meanwhile,we propose the corresponding data augmentation strategy to ensure data diversity when generating the training data from the radio map.Thus,the cost of data collection for training can be significantly reduced.Lastly,the Siamese architecture based SiamResNet is applied for location estimation,which can comprehensively extract the features of fingerprints and accurately compare the similarity of any fingerprint to the radio map in the representation space.The performance of the proposed localization method is validated through extensive simulations with a ray-tracing channel model,which demonstrates promising localization accuracy for our SiamResNet with reduced training costs.
基金supported by the National Natural Science Foundation of China(No.62102002).
文摘Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these methods use attention mechanisms and their variants,which have room for further improvement in the speed of solving MOCO problems.In this paper,following the idea of decomposition strategy and neural combinatorial optimization,a novel fast-solving model for MOCO based on retention is proposed.A brand new calculation of retention is proposed,causal masking and exponential decay are deprecated in retention,so that our model could better solve MOCO problems.During model training,a parallel computation of retention is applied,allowing for fast parallel training.When using the model to solve MOCO problems,a recurrent computation of retention is applied,enabling quicker problem-solving.In order to make our model more practical and flexible,a preference-based retention decoder is proposed,which allows generating approximate Pareto solutions for any trade-off preferences directly.An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO.Experimental results show that,while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.
基金supported by the National Natural Science Foundation of China(No.62076095)National Key Research and Development Program(No.2022YFB4602104).
文摘Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion time.To address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is proposed.In NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution process.The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations.In addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is adopted.Multiple local search operators based on ideal point are proposed to find a better local solution.To improve the global exploration ability of the algorithm,a dual population restart mechanism is adopted.Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region,China(Nos.2023D01C187 and 2022D01E33)National Natural Science Foundation of China(No.52267010)Tianshan Talent Training Program(Nos.2023TSYCCX0037 and 2023TSYCQNTJ0020).
文摘This paper introduces a novel color image encryption algorithm based on a five-dimensional continuous memristor hyperchaotic system(5D-MHS),combined with a two-dimensional Salomon map and an optimized Arnold transform.Firstly,convert the test image to a 2D pixel matrix then processed in blocks,and each block of the pixel matrix is permuted with chaotic sequences generated by 5D-MHS and 2D Salomon map.Then,the permuted image is permuted for three rounds with the optimized Arnold algorithm.Finally,one of the chaotic sequences generated by 5D-MHS is employed to diffuse the permuted image to obtain the final ciphertext image.In this paper,several pseudo-random sequences are generated and mixed in the permutation stage to achieve higher security.The algorithm achieves a key space of 2472,the information entropy of the ciphertext image for the color image is 7.9998,number of pixels change rate(NPCR)and unified average changing intensity(UACI)reached 99.6131%and 33.4361%,respectively,and the correlation between pixels is close to 0.The simulation results show that the encryption algorithm is efficient and the key system is secure.
文摘The present paper attempts to design an adaptive multi-model predictive control strategy for strongly nonlinear or switched systems with various operating points.The proposed control system guarantees the feasibility and the asymptotic stability of the closed-loop system,considering various challenges such as inherent uncertainties in the local models constituting the model bank,limited prediction/control horizons,and set point changes.To this end,four fundamental challenges in this area,namely guaranteeing feasibility throughout the region assigned to each subspace,ensuring asymptotic stability in each subspace considering the inherent uncertainties of the local models,guaranteeing feasibility and asymptotic stability during changes in the set point and switching between the subspaces,are addressed.By introducing transferring mode concept,this paper presents a novel method for guaranteeing the feasibility and stability of the switched systems without the need for increasing the prediction/control horizons or decreasing the size of the feasibility region.The proposed control structure uses a supervisor algorithm along with a soft-switching technique.The supervisor algorithm is responsible for determining the suitable local model/controller pair,determining the operational mode of the control system,managing the soft switching,and specifying the control objectives in accordance with the defined set point.The efficiency of the proposed control strategy is demonstrated by simulating a Continuous Stirred Tank Reactor(CSTR)as the controlled system.Based on the results,the proposed controller is able to guarantee the feasibility and stability of highly nonlinear and switched systems in a wide operating region under set point changes and uncertainties in the local models.
基金supported by the National Natural Science Foundation of China(No.62173356)Science and Technology Development Fund(FDCT),Macao SAR(No.0019/2021/A)+2 种基金Zhuhai Industry-University-Research Project with Hongkong and Macao(No.ZH22017002210014PWC)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011531)Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(No.22JR10KA007).
文摘This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy consumption.First,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical model.Second,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_CS.Then,fourteen local search operators are employed to search for better solutions.Two different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process.Finally,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different problems.The experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned problems.This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.
基金supported by the Science and Technology Development Fund(FDCT),Macao SAR(No.0019/2021/A)National Natural Science Foundation of China(No.62173356)+2 种基金Zhuhai Industry-University-Research Project with Hongkong and Macao(No.ZH22017002210014PWC)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011531)Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(No.22JR10KA007).
文摘As the global economy develops and people's awareness of environmental protection increases,the efficient scheduling of production lines in workshops has received more and more attention.However,there is very little research focusing on distributed scheduling for heterogeneous factories.This study addresses a multi-objective distributed heterogeneous permutation flow shop scheduling problem with sequence-dependent setup times(DHPFSP-SDST).The objective is to optimize the trade-off between the maximum completion time(Makespan)and total energy consumption.First,to describe the concerned problems,we establish a mathematical model.Second,we use the artificial bee colony(ABC)algorithm to optimize the two objectives,incorporating five local search strategies tailored to the problem characteristics to enhance the algorithm's performance.Third,to improve the convergence speed of the algorithm,a Q-learning based strategy is designed to select the appropriated local search operator during iterations.Finally,based on experiments conducted on 72 instances,statistical analysis and discussions show that the Q-learning based ABC algorithm can effectively solve the problems better than its peers.
基金supported by the National Natural Science Foundation of China(Nos.62071475 and 62401580).
文摘The lack of data is constraining research on the bistatic ballistic target(BT).In the bistatic radar system,the angle between the target axis and the radar line of sight(RLOS)is generally large,which means that the specular scattering center(SSC)is likely to be present with the target's micro-motion.This results in a brief flash in the time-frequency representation(TFR)but has been less frequently reported.We propose a micro-motion echo simulation method with SSC by calculating its position vector and modeling its visible coefficient.Furthermore,the model with four estimated parameters is utilized to optimize the simulated TFR.The simulation results intuitively and numerically agree well with the TFR obtained by electromagnetic calculation.
基金supported by the Key projects of Hunan Provincial Department of Education(No.23A0133)National Natural Science Foundation of China(No.62171401).
文摘The human brain is composed of a large number of neurons that work together to process the generation,transmission,reception,and processing of information.The topological structure of the human brain has small-world characteristics,and the synchronization and neuron firing are influenced by the electromagnetic field.In this paper,we use four-stable discrete memristors to simulate the external electromagnetic field,and construct a memristive small-world neural network(MSNN)model based on Rulkov neurons,and conduct numerical simulations.We have found that the MSNN exhibits multiple coexisting behaviors of synchronous,asynchronous,and chimeric states under different initial conditions of the discrete memristors.At the same time,changing the strength of electromagnetic induction can affect the synchronization performance of the MSNN.Finally,we find that increasing the electromagnetic induction strength can enhance the neuron firing action potential.
基金supported by the National Natural Science Foundation of China(No.62076225)the Natural Science Foundation of Guangxi Province(No.2020JJA170038)the High-Level Talents Research Project of Beibu Gulf(No.2020KYQD06).
文摘Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse fields.One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it is a difficult and challenging task in numerical computation.In recent years,Intelligent Optimization Algorithms(IOAs)have shown to be particularly effective in solving NEs.This paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of NEs.This paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation techniques.Then,solving NEs with IOAs is reviewed,followed by the benchmark functions and the performance comparison of several state-of-the-art algorithms.Finally,this paper points out the challenges and some possible open issues for solving NEs.
