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
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
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
In this paper we applicate the Hungarian algorithm for assignment problem to solve traveling salesman problem. Tree examples of application of algorithm are included.
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
复杂电磁环境下卫星信号往往淹没在背景和噪声中,传统的信号检测算法在没有准确先验知识的情况下性能急剧降低,目前基于深度学习的信号检测算法往往需要依赖专家经验的数据后处理步骤,无法对信号进行端到端检测.针对上述缺陷,提出一种基...复杂电磁环境下卫星信号往往淹没在背景和噪声中,传统的信号检测算法在没有准确先验知识的情况下性能急剧降低,目前基于深度学习的信号检测算法往往需要依赖专家经验的数据后处理步骤,无法对信号进行端到端检测.针对上述缺陷,提出一种基于DETR_S(DEtection with TRansformer on Signal)的卫星信号智能检测方法.DETR_S以编码器-解码器架构为基础,利用Transformer网络全局建模能力捕获频谱信息,采用多头自注意力机制有效改善频谱信息长距离依赖的问题.基于匈牙利算法的预测框匹配模块摒弃了非极大值抑制的数据后处理步骤,将信号检测问题转变为集合预测问题,使模型并行输出检测结果.引入信号重构模块,将频谱重构损失函数加入损失函数中,辅助模型挖掘频谱深层表征,提升信号检测性能.实验结果表明,在仅使用信号频谱幅度信息条件下,DETR_S能够在信噪比等于0dB及以上对卫星信号进行精确检测(>95%),优于典型的目标检测方法.展开更多
基金This project was supported by the National Natural Science Foundation of China (60272024).
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.[42275147].
文摘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.
文摘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.
文摘In this paper we applicate the Hungarian algorithm for assignment problem to solve traveling salesman problem. Tree examples of application of algorithm are included.
文摘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。
文摘复杂电磁环境下卫星信号往往淹没在背景和噪声中,传统的信号检测算法在没有准确先验知识的情况下性能急剧降低,目前基于深度学习的信号检测算法往往需要依赖专家经验的数据后处理步骤,无法对信号进行端到端检测.针对上述缺陷,提出一种基于DETR_S(DEtection with TRansformer on Signal)的卫星信号智能检测方法.DETR_S以编码器-解码器架构为基础,利用Transformer网络全局建模能力捕获频谱信息,采用多头自注意力机制有效改善频谱信息长距离依赖的问题.基于匈牙利算法的预测框匹配模块摒弃了非极大值抑制的数据后处理步骤,将信号检测问题转变为集合预测问题,使模型并行输出检测结果.引入信号重构模块,将频谱重构损失函数加入损失函数中,辅助模型挖掘频谱深层表征,提升信号检测性能.实验结果表明,在仅使用信号频谱幅度信息条件下,DETR_S能够在信噪比等于0dB及以上对卫星信号进行精确检测(>95%),优于典型的目标检测方法.
