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911.
将组合电路故障模拟的一些加速技术推广到时序电路故障模拟中,提出并实现了一个功能块级的基于测试码并行的同步时序电路故障模拟方法,对部分ISCAS89 Benchmark电路的模拟结果表明,该故障模拟方法有较好的性能. 相似文献
912.
文中介绍了IRM-PC机上多功能仿真软件GASP/PC的开发,叙述了GASP/PC仿真语言的形成以及对于连续、离散和复合仿真模型的GASP/PC语言的结构与仿真方法。 相似文献
913.
为充分发掘利用海量卫星网络数据,提高决策效率,加强空间频轨资源获取与储备的分析手段,尤其是对地球静止轨道资源的协调获取问题,提出基于机器学习算法的卫星网络态势评估策略。通过对卫星网络协调因素进行特征分析,选择卷积神经网络(Convolution Neural Network, CNN)为目标算法模型,并建立算法模型的训练数据集及Label规则,采用分裂信息增益度量方法对数据进行降维处理,建立CNN评估模型,并进行了验证分析。结果表明,CNN模型对卫星网络协调态势评估问题测试的正确率高达80%以上,具有较高的评估效能。随着数据量的增多,CNN评估效果逐步提升,是一种在卫星网络协调态势分析、资源储备的有效评估方法。 相似文献
914.
为保证新一代移动无线网络能够根据实时覆盖情况动态地调节小区天线参数,需要实现高效且准确的无线覆盖预测。传统的求解方法通过精确的场强预测判断天线参数的优劣,虽然精度很高但需要大量的计算资源,无法满足5G和后5G移动网络通过实时覆盖预测进行射频参数动态调整的实际需求。现采用基于深度神经网络的算法对给定天线参数的覆盖效果进行预测,以取代对目标区域的精确场强预测。数值结果表明:该方法能够在保持计算准确性的同时显著减少计算量,为5G动态网络规划提供基础性参考数据。 相似文献
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916.
917.
子母弹作为一种面杀伤武器,在遂行封锁敌机场作战任务时可发挥有效作用。本文以子母弹主要技战术指标为研究对象,首先给出基于最小起飞窗口的跑道封锁判断方法和基于蒙特卡洛仿真的机场跑道封锁概率估算模型,然后研究了母弹圆概率误差、子炸弹抛撒半径、数量以及毁伤半径等主要弹药参数对机场跑道封锁率曲线的影响。结果表明:跑道封锁率对母弹圆概率误差和抛撒半径的变化较为敏感,通过优化上述参数可以显著降低子母弹耗弹量,提高封锁效率;相比之下,单枚母弹内子炸弹数量和单枚子炸弹毁伤半径在一定范围内变动时对机场封锁率影响较小。本仿真研究的结果可为封锁机场作战时弹药选取、耗弹量测算提供科学依据,并对子母弹后续型号改进提供有效建议。 相似文献
918.
《防务技术》2020,16(1):96-106
The numerical simulation of a blast wave of a multilayer composite charge is investigated. A calculation model of the near-field explosion and far-field propagation of the shock wave of a composite charge is established using the AUTODYN finite element program. Results of the near-field and far-field calculations of the shock wave respectively converge at cell sizes of 0.25–0.5 cm and 1–3 cm. The Euler––flux-corrected transport solver is found to be suitable for the far-field calculation after mapping. A numerical simulation is conducted to study the formation, propagation, and interaction of the shock wave of the composite charge for different initiation modes. It is found that the initiation mode obviously affects the shock-wave waveform and pressure distribution of the composite charge. Additionally, it is found that the area of the overpressure distribution is greatest for internal and external simultaneous initiation, and the peak pressure of the shock wave exponentially decays, fitting the calculation formula of the peak overpressure attenuation under different initiation modes, which is obtained and verified by experiment. The difference between numerical and experimental results is less than 10%, and the peak overpressure of both internal and external initiation is 56.12% higher than that of central single-point initiation. 相似文献
919.
《防务技术》2020,16(2):362-373
An increase in the use of the gun barrel will cause wear of the inner wall, which reduces the muzzle velocity and the spin rate of the projectile. The off-bore flight attitude and trajectory of the projectile also change, affecting the shooting power and the accuracy. Exterior ballistic data of a high-speed spinning projectile are required to study the performance change. Therefore, based on the barrel's accelerated life test, the whole process of projectile shooting is reproduced using numerical simulation technology, and key information on the ballistic performance change at each shooting stage are acquired. Studies have shown that in the later stages of barrel shooting, the accuracy of shooting has not decreased significantly. However, it is found that the angle of attack of the projectile increases as the wear of the barrel increases. The maximum angle of attack reaches 0.106 rad when the number of shots reaches 4300. Meanwhile, elliptical bullet hole has appeared on the target at this shooting stage. Through combining external ballistic theory with simulation results, the primary reason of this phenomenon is found to be a significant decrease in the muzzle spin rate of the projectile. At the end of the barrel life, the projectile muzzle spin rate is 57.5% lower than that of a barrel without wear. 相似文献
920.
《防务技术》2020,16(6):1116-1129
Object detection models based on convolutional neural networks (CNN) have achieved state-of-the-art performance by heavily rely on large-scale training samples. They are insufficient when used in specific applications, such as the detection of military objects, as in these instances, a large number of samples is hard to obtain. In order to solve this problem, this paper proposes the use of Gabor-CNN for object detection based on a small number of samples. First of all, a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor is constructed, and the optimal Gabor convolution kernel group is obtained by means of training and screening, which is convolved with the input image to obtain feature information of objects with strong auxiliary function. Then, the k-means clustering algorithm is adopted to construct several different sizes of anchor boxes, which improves the quality of the regional proposals. We call this regional proposal process the Gabor-assisted Region Proposal Network (Gabor-assisted RPN). Finally, the Deeply-Utilized Feature Pyramid Network (DU-FPN) method is proposed to strengthen the feature expression of objects in the image. A bottom-up and a top-down feature pyramid is constructed in ResNet-50 and feature information of objects is deeply utilized through the transverse connection and integration of features at various scales. Experimental results show that the method proposed in this paper achieves better results than the state-of-art contrast models on data sets with small samples in terms of accuracy and recall rate, and thus has a strong application prospect. 相似文献