The blocky LPSO particles were modulated by single-directional and multi-directional forging,and the effect of blocky LPSO particles on the anisotropy of mechanical properties of Mg-8.5Gd-2.5Y-1.5Zn-0.5Zr alloy forged...The blocky LPSO particles were modulated by single-directional and multi-directional forging,and the effect of blocky LPSO particles on the anisotropy of mechanical properties of Mg-8.5Gd-2.5Y-1.5Zn-0.5Zr alloy forged parts was investigated.In the present work,3D processing maps are established,and the forming domain that is both stable and power efficient is in the temperature range from 430 to 500℃ and strain rate range from 0.001 to 0.06 s^(-1),which is used to guide the single-directional forging(SDF)and multi-directional forging(MDF)experiments.The tensile mechanical properties reveal that the blocky LPSO particles have an influence on the mechanical anisotropy,especially in terms of the elongation anisotropy.The blocky LPSO particles after the MDF process have a more regular shape and smaller size and are homogeneously distributed,which is responsible for the low anisotropy of the elongation.In addition,the age-hardening capability of the MDF part is higher than that of the SDF part.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in oper...Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this...During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.展开更多
Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haz...Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haze,remains very scarce.This study used single-particle aerosol mass spectrometry to investigate IM-containing particles in Chengdu,one of the megacities in the SCB,during summer and winter before and after implemen-tation of the Three-year Action Plan to Win the Blue-Sky Defense War(BSDW).We found that IM-containing particles accounted for 1.2%–12.0%of all detected particles,and they highly mixed with carbonaceous com-ponents,secondary inorganic species,and organic nitrogen.From before to after the BSDW,the proportion of IM-containing particles decreased by 1.8%in summer,but increased by 9.6%in winter.Ammonium/amines and carbonyl compounds were closely related to IM-containing particles;the highest proportion of IM-containing particles occurred in particles mixed with amines and carbonyls.The number fraction of IM-containing particles in all seasons was higher at night than during daytime.The potential source areas of IM-containing particles showed notable narrowing after the BSDW,and the high-value areas were found distributed closer to Chengdu and its surrounding areas.In the winter before the BSDW,most IM-containing particles(>70%)were mixed with organic carbon(OC)particles,and the contributions of OC and mixed organic–elemental carbon(OC-EC)particles increased with aggravation of pollution,whereas OC-EC and Metal particles played a more crucial role in the winter after the BSDW.展开更多
In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the...In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
基金supports of the National Key Research and Development Program of China(Grant No.2021YFB3501005)the National Natural Science Foundation of China(Grant No.52071208)the Key R&D program of Shanxi Province(International Cooperation)(Grant No.201903D421036).
文摘The blocky LPSO particles were modulated by single-directional and multi-directional forging,and the effect of blocky LPSO particles on the anisotropy of mechanical properties of Mg-8.5Gd-2.5Y-1.5Zn-0.5Zr alloy forged parts was investigated.In the present work,3D processing maps are established,and the forming domain that is both stable and power efficient is in the temperature range from 430 to 500℃ and strain rate range from 0.001 to 0.06 s^(-1),which is used to guide the single-directional forging(SDF)and multi-directional forging(MDF)experiments.The tensile mechanical properties reveal that the blocky LPSO particles have an influence on the mechanical anisotropy,especially in terms of the elongation anisotropy.The blocky LPSO particles after the MDF process have a more regular shape and smaller size and are homogeneously distributed,which is responsible for the low anisotropy of the elongation.In addition,the age-hardening capability of the MDF part is higher than that of the SDF part.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金supported by the National Natural Science Foundation of China(Grant No.52475543)Natural Science Foundation of Henan(Grant No.252300421101)+1 种基金Henan Province University Science and Technology Innovation Talent Support Plan(Grant No.24HASTIT048)Science and Technology Innovation Team Project of Zhengzhou University of Light Industry(Grant No.23XNKJTD0101).
文摘Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
基金supported by the National Natural Science Foundation of China(No.52175414)the Natural Science Foundation of Jiangsu Province of China(No.BK20220134)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.NE2023002)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(No.KYCX24_0559)。
文摘During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.
基金supported by Sichuan Science and Technology Program(No.2024NSFSC0060)the National Natural Science Foundation of China(No.U23A2030)the Basic Research Cultivation Support Plan of Southwest Jiaotong University(No.2682023ZTPY016).
文摘Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haze,remains very scarce.This study used single-particle aerosol mass spectrometry to investigate IM-containing particles in Chengdu,one of the megacities in the SCB,during summer and winter before and after implemen-tation of the Three-year Action Plan to Win the Blue-Sky Defense War(BSDW).We found that IM-containing particles accounted for 1.2%–12.0%of all detected particles,and they highly mixed with carbonaceous com-ponents,secondary inorganic species,and organic nitrogen.From before to after the BSDW,the proportion of IM-containing particles decreased by 1.8%in summer,but increased by 9.6%in winter.Ammonium/amines and carbonyl compounds were closely related to IM-containing particles;the highest proportion of IM-containing particles occurred in particles mixed with amines and carbonyls.The number fraction of IM-containing particles in all seasons was higher at night than during daytime.The potential source areas of IM-containing particles showed notable narrowing after the BSDW,and the high-value areas were found distributed closer to Chengdu and its surrounding areas.In the winter before the BSDW,most IM-containing particles(>70%)were mixed with organic carbon(OC)particles,and the contributions of OC and mixed organic–elemental carbon(OC-EC)particles increased with aggravation of pollution,whereas OC-EC and Metal particles played a more crucial role in the winter after the BSDW.
基金Supported by the Guangxi Special Program for Technological Innovation Guidance(No.GuiKeAC25069006).
文摘In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
