实时仿真计算机系统的若干问题

本文比较了实时仿真计算机系统中配备A/D、D/A变换器的两种方案。分析了几种检索算法,并且提出了一种新的全并行检索方法——映象检索。文中论述了仿真计算任务的特点及其并行性的开发。本文在分析了AD-10的缺点之后指出,纯异构型仿真专用多处理机的局限性较大,不能充分开发仿真计算任务的并行性。较好的结构形式应是以同构型多处理机为核心。     

坦克数据通讯网中的协议分析

通过对坦克车内局域网1553B协议的分析,并与一般计算机通信标准局域网做对比,指出不能用标准局域网协议和纶构取代1553B协议。参考 ISO开放系统互连的协议模型,对1553B协议进行了分层设计和进一步构想。     

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.     

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.     

Heuristics for the location of inspection stations on a network

This article considers the preventive flow interception problem (FIP) on a network. Given a directed network with known origin‐destination path flows, each generating a certain amount of risk, the preventive FIP consists of optimally locating m facilities on the network in order to maximize the total risk reduction. A greedy search heuristic as well as several variants of an ascent search heuristic and of a tabu search heuristic are presented for the FIP. Computational results indicate that the best versions of the latter heuristics consistently produce optimal or near optimal solutions on test problems. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 287–303, 2000     

局部最大延迟检测抗转发欺骗干扰算法

接收机在进行窄带干扰抑制后,其输出信号的相关峰会发生分裂。使用传统的基于信号到达时间的抗转发欺骗干扰算法,会使得捕获锁定的相关值为真实信号的旁瓣。针对该问题提出一种基于局部相关值最大的时延检测方法,在真实信号相关峰分裂所产生的旁瓣与欺骗信号不重合前提下,通过对主瓣三个码片范围内最大值的搜索,保证捕获不会锁定在旁瓣,再选择局部最大值中的最早码相位点从而避免锁定到转发欺骗干扰。与传统的基于信号到达时间的抗转发欺骗干扰算法相比,在无窄带干扰情况下,所提算法性能相当;在存在窄带干扰情况下,其性能明显优于传统算法。通过数值仿真验证了方法的有效性。     

Network design for time‐constrained delivery

To meet customer demand, delivery companies are offering an increasing number of time‐definite services. In this article, we examine the strategic design of delivery networks which can efficiently provide these services. Because of the high cost of direct connections, we focus on tree‐structured networks. As it may not be possible to identify a tree‐structured network that satisfies all of the delivery guarantees, we allow these guarantees to be violated but seek to minimize the sum of the violations. We establish the complexity of the problem and exploit an empirically identified solution structure to create new neighborhoods which improve solution values over more general neighborhood structures. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

支持多剧情并发执行的仿真克隆中间件机制研究

仿真克隆可以提高分布式离散事件仿真的效率和并行性,方便快捷地对仿真中的多种可能性进行分析、比较和评估.介绍仿真克隆的相关概念,分析HLA环境下封装式仿真克隆中间件的不足,提出并设计一种支持多剧情并发的拦截式HLA仿真克隆中间件,讨论基于该拦截式仿真克隆中间件的HLA仿真成员体系结构,并对联邦成员的时间延迟性能进行测试,测试结果表明该松耦合结构使联邦成员产生的时间延迟是有限且基本稳定的,基于拦截式仿真克隆中间件方法是可行的.     

适应低时延业务需求的分布式可穿戴单兵作战信息系统

针对云计算应用在单兵作战系统场景下业务处理时延高、服务质量无法保障的问题,提出一种基于可穿戴计算的分布式单兵作战信息系统。利用士兵身上的可穿戴智能设备构建本地计算层,在作战地点就近处理计算数据,提供给士兵本地的信息处理与融合能力,并采用广义扩散负载均衡算法平衡各设备负载,降低业务处理时延;同时利用分布式计算的容错能力增强系统的可靠性。仿真结果表明,基于可穿戴设备的分布式本地网络架构能有效地降低作战任务的处理时延,同时增强系统的可靠性。     

调运问题中基于栅格模型的快速路径规划方法

针对调运路径规划这一问题,采用栅格模型表示环境地图,通过设定路径搜索方向权重,剔除不必要的搜索区域,提高了搜索效率.仿真结果表明,该算法能有效地提高路径搜索效率,并能搜索到最优路径.