In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed...In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.展开更多
A flume experiment was conducted to investigate the restratification of liquefied sediment strata under a wave load with the focus on the interbedded strata of coarse and fine sediments formed in estuarine and coastal...A flume experiment was conducted to investigate the restratification of liquefied sediment strata under a wave load with the focus on the interbedded strata of coarse and fine sediments formed in estuarine and coastal areas.The aim of this research was to study the characteristics and processes of liquefied sediment strata in terms of wave-induced liquefaction.In the experiment,the bottom bed liquefied under the wave action and the liquefied soil moved in the same period with the overlying waves,and the track of the soil particles in the liquefied soil was an ellipse.The sand layer consisting of coarse particles in the upper part,settled into the lower silt layer.The sinking of coarse particles and upward migration of the fine particles of the lower layer induced by liquefied sediment fluctuations are the likely reasons for sedimentation of the sand layer in liquefied silt.展开更多
Accurate measurement of bean particle size is essential for automated grading and quality control in agricultural processing.However,existing image segmentation methods often suffer from low efficiency,over-segmentati...Accurate measurement of bean particle size is essential for automated grading and quality control in agricultural processing.However,existing image segmentation methods often suffer from low efficiency,over-segmentation,and high computational cost.We proposed a distancegradient dual constrained watershed algorithm for precise segmentation and measurement of bean particles.The method integrated distance transform-based seed extraction with gradient-constrained flooding,effectively suppressing noise-induced region fragmentation and improving the separation of adherent particles.An experimental platform was constructed using an industrial camera and an image-processing pipeline to evaluate performance.Compared with the conventional watershed algorithm,the proposed method improves segmentation accuracy by 7.2%and reduces the mean particle size error by 27.8%(0.13 mm,representing a relative error of 2.4%).Validation on three soybean varieties confirmed the robustness and generalizability of the approach.The results indicated that the proposed algorithm provided an efficient and accurate technique for agricultural particle size analysis,offering potential for integration into practical low-cost inspection systems.展开更多
The manipulation of cells and particles suspended in viscoelastic fluids in microchannels has drawn increasing attention,in part due to the ability for single-stream three-dimensional focusing in simple channel geomet...The manipulation of cells and particles suspended in viscoelastic fluids in microchannels has drawn increasing attention,in part due to the ability for single-stream three-dimensional focusing in simple channel geometries.Improvement in the understanding of non-Newtonian effects on particle dynamics has led to expanding exploration of focusing and sorting particles and cells using viscoelastic microfluidics.Multiple factors,such as the driving forces arising from fluid elasticity and inertia,the effect of fluid rheology,the physical properties of particles and cells,and channel geometry,actively interact and compete together to govern the intricate migration behavior of particles and cells in microchannels.Here,we review the viscoelastic fluid physics and the hydrodynamic forces in such flows and identify three pairs of competing forces/effects that collectively govern viscoelastic migration.We discuss migration dynamics,focusing positions,numerical simulations,and recent progress in viscoelastic microfluidic applications as well as the remaining challenges.Finally,we hope that an improved understanding of viscoelastic flows in microfluidics can lead to increased sophistication of microfluidic platforms in clinical diagnostics and biomedical research.展开更多
Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at pro- posing an optimal set of road maintenance so...Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at pro- posing an optimal set of road maintenance solutions, robust meta-heuristic algorithms are used in research. Two main optimization techniques are applied including single-objective and multi-objective optimization. Genetic algorithms （GA）, particle swarm optimization （PSO）, and combination of genetic algorithm and particle swarm optimization （GAPSO） as single-objective techniques are used, while the non-domination sorting genetic algorithm II （NSGAII） and multi-objective particle swarm optimization （MOPSO） which are sufficient for solving computationally complex large-size optimization problems as multi-objective techniques are applied and compared. A real case study from the rural transportation network of Iran is employed to illustrate the sufficiency of the optimum algorithm. The formulation of the optimization model is carried out in such a way that a cost-effective maintenance strategy is reached by preserving the performance level of the road network at a desirable level. So, the objective functions are pavement performance maximization and maintenance cost minimization. It is concluded that multi-objective algorithms including non-domination sorting genetic algorithm II （NSGAII） and multi-objective particle swarm optimization performed better than the single objective algorithms due to the capability to balance between both objectives. And between multi-objective algorithms the NSGAII provides the optimum solution for the road maintenance planning.展开更多
基金Foundation item： Projects（61102106, 61102105） supported by the National Natural Science Foundation of China Project（2013M530148） supported by China Postdoctoral Science Foundation Project（HEUCF120806） supported by the Fundamental Research Funds for the Central Universities of China
文摘In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.
基金The funding for this project was provided by the National Natural Science Foundation of China(No.41976049)the Fundamental Research Funds for the Central Universities(No.202061028).
文摘A flume experiment was conducted to investigate the restratification of liquefied sediment strata under a wave load with the focus on the interbedded strata of coarse and fine sediments formed in estuarine and coastal areas.The aim of this research was to study the characteristics and processes of liquefied sediment strata in terms of wave-induced liquefaction.In the experiment,the bottom bed liquefied under the wave action and the liquefied soil moved in the same period with the overlying waves,and the track of the soil particles in the liquefied soil was an ellipse.The sand layer consisting of coarse particles in the upper part,settled into the lower silt layer.The sinking of coarse particles and upward migration of the fine particles of the lower layer induced by liquefied sediment fluctuations are the likely reasons for sedimentation of the sand layer in liquefied silt.
基金supported by National Natural Science Foundation of China(No.62006092)University Synergy Innovation Program of Anhui Province(No.GXXT-2023-108)Excellent Youth Project of Natural Science Research in Anhui Province(No.2023AH030081).
文摘Accurate measurement of bean particle size is essential for automated grading and quality control in agricultural processing.However,existing image segmentation methods often suffer from low efficiency,over-segmentation,and high computational cost.We proposed a distancegradient dual constrained watershed algorithm for precise segmentation and measurement of bean particles.The method integrated distance transform-based seed extraction with gradient-constrained flooding,effectively suppressing noise-induced region fragmentation and improving the separation of adherent particles.An experimental platform was constructed using an industrial camera and an image-processing pipeline to evaluate performance.Compared with the conventional watershed algorithm,the proposed method improves segmentation accuracy by 7.2%and reduces the mean particle size error by 27.8%(0.13 mm,representing a relative error of 2.4%).Validation on three soybean varieties confirmed the robustness and generalizability of the approach.The results indicated that the proposed algorithm provided an efficient and accurate technique for agricultural particle size analysis,offering potential for integration into practical low-cost inspection systems.
基金We gratefully acknowledge the partial funding support from the Richard and Loan Hill Department of Bioengineering at the University of Illinois at Chicago and the University of Illinois Cancer Center.
文摘The manipulation of cells and particles suspended in viscoelastic fluids in microchannels has drawn increasing attention,in part due to the ability for single-stream three-dimensional focusing in simple channel geometries.Improvement in the understanding of non-Newtonian effects on particle dynamics has led to expanding exploration of focusing and sorting particles and cells using viscoelastic microfluidics.Multiple factors,such as the driving forces arising from fluid elasticity and inertia,the effect of fluid rheology,the physical properties of particles and cells,and channel geometry,actively interact and compete together to govern the intricate migration behavior of particles and cells in microchannels.Here,we review the viscoelastic fluid physics and the hydrodynamic forces in such flows and identify three pairs of competing forces/effects that collectively govern viscoelastic migration.We discuss migration dynamics,focusing positions,numerical simulations,and recent progress in viscoelastic microfluidic applications as well as the remaining challenges.Finally,we hope that an improved understanding of viscoelastic flows in microfluidics can lead to increased sophistication of microfluidic platforms in clinical diagnostics and biomedical research.
文摘Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at pro- posing an optimal set of road maintenance solutions, robust meta-heuristic algorithms are used in research. Two main optimization techniques are applied including single-objective and multi-objective optimization. Genetic algorithms （GA）, particle swarm optimization （PSO）, and combination of genetic algorithm and particle swarm optimization （GAPSO） as single-objective techniques are used, while the non-domination sorting genetic algorithm II （NSGAII） and multi-objective particle swarm optimization （MOPSO） which are sufficient for solving computationally complex large-size optimization problems as multi-objective techniques are applied and compared. A real case study from the rural transportation network of Iran is employed to illustrate the sufficiency of the optimum algorithm. The formulation of the optimization model is carried out in such a way that a cost-effective maintenance strategy is reached by preserving the performance level of the road network at a desirable level. So, the objective functions are pavement performance maximization and maintenance cost minimization. It is concluded that multi-objective algorithms including non-domination sorting genetic algorithm II （NSGAII） and multi-objective particle swarm optimization performed better than the single objective algorithms due to the capability to balance between both objectives. And between multi-objective algorithms the NSGAII provides the optimum solution for the road maintenance planning.
