A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited resources.However,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging task.To resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction optimizationmethod.Itmainly includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood information.Specifically,the OPBS consists of two parts:the selection of blocks and the optimization of prediction blocks.We combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video sequence.In addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on performance.Therefore,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as possible.To maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current block.The proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction performance.Experimental results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.

基于高维多目标优化的多无人机协同航迹规划

随着无人机应用领域的增多,多无人机协同航迹规划问题变得愈发重要.然而,现存的多无人机协同航迹规划问题大多将多个目标加权转换为单目标问题进行优化,为减少多目标加权的主观性,本文提出一种基于高维多目标优化的多无人机协同航迹规划模型,此模型可以同时优化多无人机航迹距离代价、多无人机航迹威胁代价、多无人机航迹能耗代价,以及多无人机协同性能.同时,为提高高维多目标优化算法在解决此模型时的性能,提出一种基于个体评估交叉策略的NSGA-Ⅲ算法(NSGAⅢ-ICO),可以根据算法运行代数综合评估个体优劣并指导种群交叉操作.仿真结果证明,此模型可以有效地提供多无人机协同航迹,且通过与其他高维多目标优化算法的比较,可以证明此改进算法可以有效地提高多无人机协同航迹规划的多种性能.

Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.

Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

The trajectory planning of multiple unmanned aerial vehicles(UAVs)is the core of efficient UAV mission execution.Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods.However,multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment.Therefore,a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance,trajectory time,trajectory threat,and trajectory coordination distance costs of UAVs.The NSGA-III algorithm,which overcomes the problems of traditional trajectory planning,is used to solve the model.This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm.Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm,thereby addressing different actual needs.

Dyeing fine denier polypropylene fibers with phenylazo-β-naphthol-containing sulfonamide disperse dyes

A series of phenylazo-β-naphthol-containing sulfonamide disperse dyes were prepared from C.I.Acid Orange 7 by successive reactions of chlorination and amination,and their chemical structures were characterized by FTIR,1H NMR,and mass spectrometry.The dyes were applied to coloring of knitted fabrics from fine denier polypropylene fibers by exhaust dyeing and their optimal dyeing conditions,such as dyebath pH,dyeing temperature,dyeing time,and dye concentration were investigated in detail.Then,dye exhaustion,color strength,and color fastnesses of the dyes on the fibers were assessed and summarized.In view of dye exhaustion and color strength of the sulfonamide dyes on fine denier PP fabrics,90℃ was selected as the best dyeing temperature at dye concentration below or equal to 3.0%owf.For achieving higher color strength,130℃ was the better choice when the dye concentration was above 3.0%owf.The sulfonamide dyes,especially secondary sulfonamide dyes,exhibited superior dye exhaustion and color fastnesses to washing,sublimation,and rubbing on fine denier PP fabrics in comparison to C.I.Solvent Yellow 14 bearing the same chromophore but without sulfonamide group.