With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data ...With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to computing.Distributed computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this process.This study analyzes the characteristics of batch processing of remote sensing big data.This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to heterogeneously distributed computing environments.This strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism.Moreover,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks.The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm.We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy.Compared with the processing before optimization,the makespan of the proposed method was shortened by at least 20%.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.展开更多
A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood d...A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest (HF) algorithm and Hungarian tree (HT) algorithm, and their combination with the naYve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.展开更多
In order to overcome the shortcoming of the classical Hungarian algorithm that it can only solve the problems where the total cost is the sum of that of each job, an improved Hungarian algorithm is proposed and used t...In order to overcome the shortcoming of the classical Hungarian algorithm that it can only solve the problems where the total cost is the sum of that of each job, an improved Hungarian algorithm is proposed and used to solve the assignment problem of serial-parallel systems. First of all, by replacing parallel jobs with virtual jobs, the proposed algorithm converts the serial-parallel system into a pure serial system, where the classical Hungarian algorithm can be used to generate a temporal assignment plan via optimization. Afterwards, the assignment plan is validated by checking whether the virtual jobs can be realized by real jobs through local searching. If the assignment plan is not valid, the converted system will be adapted by adjusting the parameters of virtual jobs, and then be optimized again. Through iterative searching, the valid optimal assignment plan can eventually be obtained.To evaluate the proposed algorithm, the valid optimal assignment plan is applied to labor allocation of a manufacturing system which is a typical serial-parallel system.展开更多
In this paper, we propose an algorithm for solving multi-objective assignment problem (MOAP) through Hungarian Algorithm, and this approach emphasizes on optimal solution of each objective function by minimizing the r...In this paper, we propose an algorithm for solving multi-objective assignment problem (MOAP) through Hungarian Algorithm, and this approach emphasizes on optimal solution of each objective function by minimizing the resource. To illustrate the algorithm a numerical example (Sec. 4;Table 1) is presented.展开更多
SORT(Simple Online and Realtime Tracking)算法作为一种经典的基于检测的多目标跟踪算法,凭借简单高效、实时性的特点受到广泛关注。本文对SORT目标检测算法进行研究,首先,剖析了算法的核心原理,包括卡尔曼滤波器、匈牙利算法和IOU等...SORT(Simple Online and Realtime Tracking)算法作为一种经典的基于检测的多目标跟踪算法,凭借简单高效、实时性的特点受到广泛关注。本文对SORT目标检测算法进行研究,首先,剖析了算法的核心原理,包括卡尔曼滤波器、匈牙利算法和IOU等关键组件;其次,梳理了算法的完整流程,并进行SORT算法的性能分析与实验验证;最后,梳理了SORT算法的改进思路与优化策略。研究表明,SORT算法在平稳运动目标跟踪场景中表现优异,在目标遮挡、快速运动等复杂场景中仍需改进。未来可通过融合多特征和优化数据关联策略等方式,进一步提升其鲁棒性和准确性。展开更多
针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在...针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在目标转弯时的跟踪精度。同时,利用检测算法提供的速度信息,在轨迹更新时对物体速度进行校正,以改善在目标速度突变时的跟踪效果。此外,采用基于置信度的两阶段匹配策略,以降低低置信度检测框对跟踪结果的影响。在nuScenes验证集上对所提出的三维目标检测与跟踪算法进行了性能评估,并通过消融实验验证了算法中各模块的有效性。实验结果表明,基于CTRA模型的点云多目标跟踪算法在跟踪精度上优于基于简单模型的算法,在目标转弯和速度突变场景下的跟踪效果显著提升,且跟踪过程中身份切换次数大幅减少。展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.[42275147].
摘要With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to computing.Distributed computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this process.This study analyzes the characteristics of batch processing of remote sensing big data.This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to heterogeneously distributed computing environments.This strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism.Moreover,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks.The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm.We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy.Compared with the processing before optimization,the makespan of the proposed method was shortened by at least 20%.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.
基金This project was supported by the National Natural Science Foundation of China (60272024).
摘要A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest (HF) algorithm and Hungarian tree (HT) algorithm, and their combination with the naYve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.
摘要In order to overcome the shortcoming of the classical Hungarian algorithm that it can only solve the problems where the total cost is the sum of that of each job, an improved Hungarian algorithm is proposed and used to solve the assignment problem of serial-parallel systems. First of all, by replacing parallel jobs with virtual jobs, the proposed algorithm converts the serial-parallel system into a pure serial system, where the classical Hungarian algorithm can be used to generate a temporal assignment plan via optimization. Afterwards, the assignment plan is validated by checking whether the virtual jobs can be realized by real jobs through local searching. If the assignment plan is not valid, the converted system will be adapted by adjusting the parameters of virtual jobs, and then be optimized again. Through iterative searching, the valid optimal assignment plan can eventually be obtained.To evaluate the proposed algorithm, the valid optimal assignment plan is applied to labor allocation of a manufacturing system which is a typical serial-parallel system.
摘要In this paper, we propose an algorithm for solving multi-objective assignment problem (MOAP) through Hungarian Algorithm, and this approach emphasizes on optimal solution of each objective function by minimizing the resource. To illustrate the algorithm a numerical example (Sec. 4;Table 1) is presented.
摘要SORT(Simple Online and Realtime Tracking)算法作为一种经典的基于检测的多目标跟踪算法,凭借简单高效、实时性的特点受到广泛关注。本文对SORT目标检测算法进行研究,首先,剖析了算法的核心原理,包括卡尔曼滤波器、匈牙利算法和IOU等关键组件;其次,梳理了算法的完整流程,并进行SORT算法的性能分析与实验验证;最后,梳理了SORT算法的改进思路与优化策略。研究表明,SORT算法在平稳运动目标跟踪场景中表现优异,在目标遮挡、快速运动等复杂场景中仍需改进。未来可通过融合多特征和优化数据关联策略等方式,进一步提升其鲁棒性和准确性。
摘要针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在目标转弯时的跟踪精度。同时,利用检测算法提供的速度信息,在轨迹更新时对物体速度进行校正,以改善在目标速度突变时的跟踪效果。此外,采用基于置信度的两阶段匹配策略,以降低低置信度检测框对跟踪结果的影响。在nuScenes验证集上对所提出的三维目标检测与跟踪算法进行了性能评估,并通过消融实验验证了算法中各模块的有效性。实验结果表明,基于CTRA模型的点云多目标跟踪算法在跟踪精度上优于基于简单模型的算法,在目标转弯和速度突变场景下的跟踪效果显著提升,且跟踪过程中身份切换次数大幅减少。