An exact network flow formulation for cell‐based evacuation in urban areas

In the literature two common macroscopic evacuation planning approaches exist: The dynamic network flow approach and the Cell–Transmission–Based approach. Both approaches have advantages and disadvantages. Many efficient solution approaches for the dynamic network flow approach exist so that realistic problem instances can be considered. However, the consideration of (more) realistic aspects (eg, density dependent travel times) results in non‐linear model formulations. The Cell‐Transmission‐Based approach on the other hand considers realistic traffic phenomena like shock waves and traffic congestion, but this approach leads to long computational times for realistic problem instances. In this article, we combine the advantages of both approaches: We consider a Cell‐Transmission‐Based Evacuation Planning Model (CTEPM) and present a network flow formulation that is equivalent to the cell‐based model. Thus, the computational costs of the CTEPM are enormously reduced due to the reformulation and the detailed representation of the traffic flow dynamics is maintained. We investigate the impacts of various evacuation scenario parameters on the evacuation performance and on the computational times in a computational study including 90 realistic instances.     

Sensor placement in active multistatic sonar networks

The idea of deploying noncollocated sources and receivers in multistatic sonar networks (MSNs) has emerged as a promising area of opportunity in sonar systems. This article is one of the first to address point coverage problems in MSNs, where a number of points of interest have to be monitored in order to protect them from hostile underwater assets. We consider discrete “definite range” sensors as well as various diffuse sensor models. We make several new contributions. By showing that the convex hull spanned by the targets is guaranteed to contain optimal sensor positions, we are able to limit the solution space. Under a definite range sensor model, we are able to exclude even more suboptimal solutions. We then formulate a nonlinear program and an integer nonlinear program to express the sensor placement problem. To address the nonconvex single‐source placement problem, we develop the Divide Best Sector (DiBS) algorithm, which quickly provides an optimal source position assuming fixed receivers. Starting with a basic implementation of DiBS, we show how incorporating advanced sector splitting methods and termination conditions further improve the algorithm. We also discuss two ways to use DiBS to find multiple source positions by placing sensors iteratively or simultaneously. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 287–304, 2017     

基于警报关联的威胁行为检测技术综述

基于警报关联的网络威胁行为检测技术因其与网络上大量部署的安全产品耦合,且能充分挖掘异常事件之间的关联关系以提供场景还原证据,正成为复杂威胁行为检测的研究热点。从威胁行为和网络安全环境的特点出发,引出威胁行为检测的应用需求和分类,介绍基于警报关联的威胁行为检测的基本概念和系统模型;重点论述作为模型核心的警报关联方法,并分类介绍了各类典型算法的基本原理和特点,包括基于因果逻辑的方法、基于场景的方法、基于相似性的方法和基于数据挖掘的方法;并结合实例介绍了威胁行为检测系统的三种典型结构,即集中式结构、层次式结构和分布式结构;基于当前研究现状,提出了对未来研究趋势的一些认识。     

稀疏卷积计算高效数据加载与输出缓存策略

针对现有神经网络加速器在处理稀疏神经网络时存在的数据加载效率低、乘加资源利用率低、输出缓存寻址逻辑复杂等问题,提出了稀疏卷积计算高效数据加载与输出缓存策略。将属于同一输入通道的非零输入特征图像数据和非零权重进行全对全乘累加运算,降低了非零数据配对难度,提高了乘加资源利用率;通过采用输入驻留计算,以及密集型循环加载特征图像数据,大幅减少了数据片外调取次数;优化了输出缓存设计,解决了现有方案中存在的输出缓存地址访问争用、存储拥塞等问题。实验表明,与采用类似架构的细粒度脉动加速器相比,在处理单元面积上减少了21.45%;在数据加载速度方面平均提高了117.71%在平均乘法器利用率方面提高了11.25%,达到89%。     

联合生成对抗网络和检测网络的SAR图像目标检测

针对合成孔径雷达图像目标检测中存在的样本获取困难且数量有限问题,提出了联合生成对抗网络和检测网络的学习模型。利用原始训练集对特别设计的超快区域卷积神经网络进行预训练;再通过基于注意力机制的深度学习生成对抗网络生成高质量合成样本,并输入检测网络进行预测;依据预测信息和概率等价类属标签分配策略为新生样本提供注释信息,并以一定占比对原始训练集进行扩充;利用扩充数据集对检测网络进行再训练。多组仿真实验证明,所提框架能够有效提升网络检测效率和性能。     

The SSA-BP-based potential threat prediction for aerial target considering commander emotion

The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system. However, the traditional threat prediction methods mostly ignore the effect of commander's emotion. They only predict a target's present threat from the target's features itself, which leads to their poor ability in a complex situation. To aerial targets, this paper proposes a method for its potential threat prediction considering commander emotion (PTP-CE) that uses the Bi-directional LSTM (BiLSTM) network and the backpropagation neural network (BP) optimized by the sparrow search algorithm (SSA). Furthermore, we use the BiLSTM to predict the target's future state from real-time series data, and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model. Therefore, the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion. The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction, regardless of commander's emotional effect.     

美国网络安全战略管理体系分析

文章从美国政府颁布的政策文件、法律法规和各机构部门的职能三个方面,对美国网络安全战略管理体系进行了介绍和分析。     

Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

An artificial neural network (ANN) constitutive model is developed for high strength armor steel tempered at 500 °C, 600 °C and 650 °C based on high strain rate data generated from split Hopkinson pressure bar (SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnson–Cook (J–C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stress–strain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500–650 °C), strains (0.05–0.2) and strain rates (1000–5500/s) are employed to formulate J–C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). R and AARE for the J–C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.     

末端防御作战中火控雷达组网系统应用研究

火控雷达组网是应对未来末端防御作战挑战的有效途径。提出了基于指挥信息系统的火控雷达组网方式,并分析了该方式的优势。继而基于末端防御战斗部署一般要求和火控雷达组网系统对空侦察配系的特殊要求,经仿真计算得出和分析了火控雷达组网系统的典型对空侦察配系。最后详细论述了火控雷达组网系统的指挥控制要点。研究结果对于促进末端防御部队形成以信息系统为核心的体系作战能力具有重要意义。     

动态拓扑图的卫星网络可视化方法

针对高度动态变化的卫星网络,提出一种基于动态拓扑图的可视化方法。动态拓扑图可视化的难点在于如何保持动态可视化过程中的稳定性,从而使用户容易地感知到网络中所发生的拓扑变化。根据卫星网络的动态变化特点,构建连续的动态拓扑图模型;设计一种保持布局稳定性的策略,并基于力引导思想提出一种动态拓扑图布局算法;以Iridium系统为典型实例,验证本文的可视化方法的合理性和可行性。实验表明,该方法能够以清晰的可视化图像支持用户对卫星网络动态拓扑的感知和理解。