针对BDLS(Blockchain version of DLS)共识算法在含有大量节点且具有层次结构的系统中共识效率低下的问题,提出一种基于BDLS的区块链共识改进算法HBDLS(Hierarchical BDLS)。首先,根据实际应用中节点的属性将节点分为两个层次,每个高层...针对BDLS(Blockchain version of DLS)共识算法在含有大量节点且具有层次结构的系统中共识效率低下的问题,提出一种基于BDLS的区块链共识改进算法HBDLS(Hierarchical BDLS)。首先,根据实际应用中节点的属性将节点分为两个层次,每个高层节点分别管理一个低层节点簇;其次,将所有低层节点进行分簇共识,并将共识结果汇报至相应的高层节点;最后,所有高层节点对低层的共识结果再次共识,通过高层共识的数据将被写入区块链。理论分析和仿真实验结果表明,在36个节点且单个区块包含4500个交易的情况下,HBDLS的吞吐量相较于BDLS算法提高了21%;在44个节点且单个区块包含3000个交易的情况下,HBDLS的吞吐量相较于BDLS算法提高了约52%;在44个节点且单个区块包含1个交易的情况下,HBDLS的共识时延相较于BDLS算法下降了26%。实验结果表明,在节点数多且交易量大的系统中,HBDLS能够大幅提高系统的共识效率。展开更多
作战任务和平台资源的合理匹配是战役作战准备阶段的主要内容。考虑平台资源能力在作战过程中的损耗,在问题建模的过程中引入了资源能力的损耗系数,使得所建模型更加符合实际作战。提出了基于动态列表调度(dynamic list scheduling,DLS...作战任务和平台资源的合理匹配是战役作战准备阶段的主要内容。考虑平台资源能力在作战过程中的损耗,在问题建模的过程中引入了资源能力的损耗系数,使得所建模型更加符合实际作战。提出了基于动态列表调度(dynamic list scheduling,DLS)和遗传算法(genetic algorithm,GA)的模型求解方法,使用DLS选择处理的任务,使用GA为选定任务分配平台资源,给出了该方法具体的设计思路和流程。最后结合联合作战的战役算例,验证了所提方法的优越性和适用性。展开更多
The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We...The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.展开更多
文摘针对BDLS(Blockchain version of DLS)共识算法在含有大量节点且具有层次结构的系统中共识效率低下的问题,提出一种基于BDLS的区块链共识改进算法HBDLS(Hierarchical BDLS)。首先,根据实际应用中节点的属性将节点分为两个层次,每个高层节点分别管理一个低层节点簇;其次,将所有低层节点进行分簇共识,并将共识结果汇报至相应的高层节点;最后,所有高层节点对低层的共识结果再次共识,通过高层共识的数据将被写入区块链。理论分析和仿真实验结果表明,在36个节点且单个区块包含4500个交易的情况下,HBDLS的吞吐量相较于BDLS算法提高了21%;在44个节点且单个区块包含3000个交易的情况下,HBDLS的吞吐量相较于BDLS算法提高了约52%;在44个节点且单个区块包含1个交易的情况下,HBDLS的共识时延相较于BDLS算法下降了26%。实验结果表明,在节点数多且交易量大的系统中,HBDLS能够大幅提高系统的共识效率。
文摘作战任务和平台资源的合理匹配是战役作战准备阶段的主要内容。考虑平台资源能力在作战过程中的损耗,在问题建模的过程中引入了资源能力的损耗系数,使得所建模型更加符合实际作战。提出了基于动态列表调度(dynamic list scheduling,DLS)和遗传算法(genetic algorithm,GA)的模型求解方法,使用DLS选择处理的任务,使用GA为选定任务分配平台资源,给出了该方法具体的设计思路和流程。最后结合联合作战的战役算例,验证了所提方法的优越性和适用性。
文摘The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.
