一种基于多目标微粒群优化的虚拟网络映射方法

针对多个虚拟网络同时映射时资源统一优化分配的问题,提出了一种基于多目标微粒群优化的虚拟网络映射方法(MSC-VNE),提高底层网络资源利用率及全局负载均衡性能。建立了虚拟网络映射的多目标优化模型,将单个虚拟网络映射作为一个子群,并采用多子群协作优化的方法在子群映射时通过相互信息交换进行协同进化,最终达到全局资源的优化分配。仿真结果表明,与典型成果相比,提出的方法有效地提高了底层网络资源利用率和虚拟网络构建成功率。     

聚碳硅烷表面张力的测定及其对纤维异形度的影响机理

采用改进的滴重法测定了先驱体聚碳硅烷(PCS)的表面张力,并探讨了表面张力对三叶形聚碳硅烷原丝异形度的影响机理。通过对纤维截面形貌的扫描电镜分析,发现表面张力对三叶形PCS纤维异形截面的形成有着较大的影响,它和粘滞阻力相互竞争的共同作用决定了三叶形PCS纤维异形度的大小。研究表明,先驱体PCS的表面张力与温度基本上呈线性关系,温度越高,PCS的表面张力就越小。     

网络学习模式识别移动代理因素提取方法研究

从分析网络学习因素的角度出发,提出了利用数据融合理论和使用移动代理技术解决网络学习模式识别的问题。详细分析了网络学习模式中的主要因素,针对网络学习模式识别提出了使用移动代理(MA)进行因素提取的方法。同时运用数据融合技术中分布式缠绕判决模型进行描述,从而使有效地获取网络学习者的学习状态变为可能。也对网络学习因素提取MA的执行环境进行了具体阐述。设计了因素提取MA体系结构,分析了因素提取的执行过程,采用ATP层代理传输协议作为移动代理系统的应用层协议,负责学习模式识别的因素提取。     

集对分析法在作战仿真结果分析中的应用研究

提出了集对分析法的概念,详细讨论了集对分析法对多指标问题的评估步骤、方法及特点.文章最后运用该方法对一个分队作战仿真实例进行了综合分析评估,证明了集对分析法简单、实用、有效.     

反解法在再入机动弹道设计中的应用研究

简要分析了一般的再入机动弹道设计方法,即正解法.针对正解法对模型依赖性强和对初值非常敏感的问题,提出了一种基于误差补偿的机动弹道设计方法——反解法.该方法从弹道终端出发反解弹道,将计算结果误差转化为机动点参数误差,再通过关机点或中制导的调整进行误差补偿,从而有利于命中精度和打击效果的实现.仿真结果表明,该方法计算简便,对工程应用有一定的参考价值.     

舰空导弹系统可靠性维修性对训练的影响

以舰空导弹武器系统远洋训练为研究背景,分析了舰空导弹武器系统可靠性、维修性对训练的影响.提出了可靠性、维修性对训练影响的具体指标,通过建立模型和仿真计算,在理论和应用上分析了航空导弹武器系统可靠性、维修性对训练的影响,得到了仿真应用结果并基于可靠性、维修性提出了对舰空导弹系统训练使用的建议.     

基于信息增益比和SGAN的信息系统评估模型

信息系统的安全对于个人、企业甚至国家都至关重要,因此,对信息系统安全的评估也极为严格.以军工单位的信息系统为例,标准过于冗余,且样本数量较少,使得神经网络难以达到预期的评估效果.针对这些问题,建立了"信息增益比+SGAN"的分类评估模型,通过计算信息增益比得到各项指标对信息系统安全划分的重要度,减少无关紧要的冗余指标;用重要指标量化后的样本训练SGAN,得到一个强有力的分类判别网络,来对信息系统的安全进行评估分类,同时输出样本"真伪概率"和"下类似比系数"作为专家是否要人为对该系统进行评估的参考标准.     

交通场景行人穿越检测方法进展研究

随着自动驾驶和计算机视觉技术的飞速发展,人们对交通安全的重视程度越来越高。其中,交通场景中行人穿越检测问题是研究的热点与难点之一。对行人穿越检测这一领域进行了综合评述,并对未来研究进行了展望。按照行人试图穿越、正在穿越及穿越预测三个阶段进行分析,首先分析了基于行人姿态穿越意图的研究进展(试图穿越),包括人体姿态、数据驱动和高斯动力学模型三个方面;其次讨论了基于轨迹跟踪的穿越行为检测方法(正在穿越);然后概括了基于轨迹预测的行人穿越检测方法(穿越预测);最后总结了现阶段比较新颖的多传感融合、车路协同、车联网等研究角度,并对未来该领域需重点发展的方向提出建议。     

Effects of total pressures and equivalence ratios on kerosene/air rotating detonation engines using a paralleling CE/SE method

In this paper, the kerosene/air rotating detonation engines(RDE) are numerically investigated, and the emphasis is laid on the effects of total pressures and equivalence ratios on the operation characteristics of RDE including the initiation, instabilities, and propulsive performance. A hybrid MPI + OpenMP parallel computing model is applied and it is proved to be able to obtain a more effective parallel performance on high performance computing(HPC) systems. A series of cases with the total pressure of 1 MPa, 1.5 MPa, 2 MPa, and the equivalence ratio of 0.9, 1, 1.4 are simulated. On one hand, the total pressure shows a significant impact on the instabilities of rotating detonation waves. The instability phenomenon is observed in cases with low total pressure (1 MPa) and weakened with the increase of the total pressure. The total pressure has a small impact on the detonation wave velocity and the specific impulse. On the other hand, the equivalence ratio shows a negligible influence on the instabilities, while it affects the ignition process and accounts for the detonation velocity deficit. It is more difficult to initiate rotating detonation waves directly in the lean fuel operation condition. Little difference was observed in the thrust with different equivalence ratios of 0.9, 1, and 1.4. The highest specific impulse was obtained in the lean fuel cases, which is around 2700 s. The findings could provide insights into the understanding of the operation characteristics of kerosene/air RDE.     

Multi-scale traffic vehicle detection based on faster R-CNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection. However, the environment of vehicle detection is particularly sophisticated in practical processes. It is compara-tively difficult to detect vehicles of various scales in traffic scene images, because the vehicles partially obscured by green belts, roadblocks or other vehicles, as well as influence of some low illumination weather. In this paper, we present a model based on Faster R-CNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes. First, we proposed a Retinex-based image adaptive correction algorithm (RIAC) to enhance the traffic images in the dataset to reduce the influence of shadow and illumination, and improve the image quality. Second, in order to improve the feature expression of the backbone network, we conducted Neural Architecture Search (NAS) on the backbone network used for feature extraction of Faster R-CNN to generate the optimal cross-layer connection to extract multi-layer features more effectively. Third, we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets, and improve the robustness of the model for challenging targets such as small scale and severe occlusion. In the imple-mentation of the model, K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model. Our model has been trained and tested on the UN-DETRAC dataset, and the obtained results indicate that our method has art-of-state detection performance.