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
In this paper we applicate the Hungarian algorithm for assignment problem to solve traveling salesman problem. Tree examples of application of algorithm are included.
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
传统蜂窝网络采用固定的频谱资源分配方式,导致频谱利用率低且无法满足用户高速和高质量通信需求。针对这种局限性提出了一种将分布式天线系统(Distributed Antenna System,DAS)与终端直通(Device to Device,D2D)通信技术相结合的策略...传统蜂窝网络采用固定的频谱资源分配方式,导致频谱利用率低且无法满足用户高速和高质量通信需求。针对这种局限性提出了一种将分布式天线系统(Distributed Antenna System,DAS)与终端直通(Device to Device,D2D)通信技术相结合的策略。首先,该方案提出协同上下行链路(Uplink and Downlink Collaboration,UADC)的资源分配算法,并构建了以最大化系统总效率为目标的非线性规划问题。然后,将该问题分为最佳功率选择和信道分配2个子问题,同时利用匈牙利算法为D2D对选择最佳信道。最后,在MATLAB仿真平台进行实验。仿真结果表明,相比于仅复用上行链路的和仅使用DAS系统的方法,所提方案的频谱效率更高。展开更多
为了提高车联网中高清地图下载业务的吞吐量和降低车队行驶业务的传输时延,提出一种基于进化策略算法和匈牙利算法(Evolutionary Strategy Algorithm and Hungarian Algorithm,ES-HA)的网络切片资源分配策略。构建增强型移动带宽(Enhanc...为了提高车联网中高清地图下载业务的吞吐量和降低车队行驶业务的传输时延,提出一种基于进化策略算法和匈牙利算法(Evolutionary Strategy Algorithm and Hungarian Algorithm,ES-HA)的网络切片资源分配策略。构建增强型移动带宽(Enhanced Mobile Broadband,eMBB)切片和高可靠低时延(Ultra Reliable&Low Latency Communication,uRLLC)切片,根据eMBB用户和uRLLC用户功率之间的函数关系求得最佳功率,采用ES算法获得两种用户的最佳带宽,并使用HA实现最佳信道匹配。仿真结果表明,与基于集群的资源块共享和功率分配(Cluster-based Resource Block Sharing and Power Allocation,CROWN)算法、基于基准算法的资源分配策略在总吞吐量、传输任务时延、链路容量及最小吞吐量方面进行对比,该策略在满足车到基础设施(Vehicle to Infrastructure,V2I)链路用户高容量需求的同时,能够提高下载业务的吞吐量和降低车队行驶业务的传输时延。展开更多
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.
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.
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.
文摘传统蜂窝网络采用固定的频谱资源分配方式,导致频谱利用率低且无法满足用户高速和高质量通信需求。针对这种局限性提出了一种将分布式天线系统(Distributed Antenna System,DAS)与终端直通(Device to Device,D2D)通信技术相结合的策略。首先,该方案提出协同上下行链路(Uplink and Downlink Collaboration,UADC)的资源分配算法,并构建了以最大化系统总效率为目标的非线性规划问题。然后,将该问题分为最佳功率选择和信道分配2个子问题,同时利用匈牙利算法为D2D对选择最佳信道。最后,在MATLAB仿真平台进行实验。仿真结果表明,相比于仅复用上行链路的和仅使用DAS系统的方法,所提方案的频谱效率更高。
文摘为了提高车联网中高清地图下载业务的吞吐量和降低车队行驶业务的传输时延,提出一种基于进化策略算法和匈牙利算法(Evolutionary Strategy Algorithm and Hungarian Algorithm,ES-HA)的网络切片资源分配策略。构建增强型移动带宽(Enhanced Mobile Broadband,eMBB)切片和高可靠低时延(Ultra Reliable&Low Latency Communication,uRLLC)切片,根据eMBB用户和uRLLC用户功率之间的函数关系求得最佳功率,采用ES算法获得两种用户的最佳带宽,并使用HA实现最佳信道匹配。仿真结果表明,与基于集群的资源块共享和功率分配(Cluster-based Resource Block Sharing and Power Allocation,CROWN)算法、基于基准算法的资源分配策略在总吞吐量、传输任务时延、链路容量及最小吞吐量方面进行对比,该策略在满足车到基础设施(Vehicle to Infrastructure,V2I)链路用户高容量需求的同时,能够提高下载业务的吞吐量和降低车队行驶业务的传输时延。
