Reforming the bras tendus? The Congolese National Police reforms, 2003–2016

The security sector reform (SSR) programme in the Democratic Republic of the Congo (DRC) has failed, according to a former high-level member of the United Nations (UN) mission in the DRC, as a large section of the country remains outside government control, and the security institutions of the state continue to constitute one of the predominant sources of insecurity for the local population in a number of ways. Based on several field studies, this article critically scrutinises the SSR of the Congolese National Police (Police nationale congolaise; PNC) and the efforts to reform it between 2004 and 2016. It further attempts to explain why so little progress was made in the SSR of the PNC, despite extensive involvement from donors. The article shows that the instrumental and traditional approach to SSR is partly to blame, because in this case it failed to address the root problems and initiate the needed fundamental reform and reconstruction of the police force. It also shows that reforming local security institutions becomes even more difficult when the local authorities do not support the effort.     

基于神经网络的语音识别系统

本文研究神经网络的多层感知器模型在语音识别中的应用。为达到实时识别的效果,笔者设计了TMS32010数字信号处理板,它与PC/XT机构成主-从系统,由PC/XT机管理数据,TMS32010进行高速运算,实验中,考察了神经网络对数字语音的识别能力,识别率达到95％以上,结果表明,用神经网络进行语音识别是一种很有前途的方法。     

轻量级接入点协议LWAPP的应用及其安全性分析

轻量级接入点协议(Lightweight Access Point Protocol,LWAPP)是一个新的无线局域网协议,该协议被设计用来传输和控制无线接入点和无线局域网控制器之间的通信。LWAPP把一些传统的无线接入点的一些功能移到了无线控制器中,以实现对无线接入点的统一控制和集中管理。简单讲述了LWAPP的功能和作用,同时分析了LWAPP的安全性,列举了一些该协议可能会受到的攻击类型,并提出了一些解决方案。     

3D laser scanning strategy based on cascaded deep neural network

A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s. The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.     

Knowledge-oriented modeling for influencing factors of battle damage in military industrial logistics: An integrated method

Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge. However, one of key challenges in designing the simulation system could be how to reasonably determine simulation model input and build a bridge to link battle damage model and battle damage laws knowledge. In this paper, we propose a novel knowledge-oriented modeling method for influencing factors of battle damage in military industrial logistics, integrating conceptual analysis, conceptual modeling, quantitative modeling and simulation implementation. We conceptualize influencing factors of battle damage by using the principle of hierarchical decomposition, thus classifying the related battle damage knowledge logically. Then, we construct the conceptual model of influencing factors of battle damage by using Entity-Relationship approach, thus describing their interactions reasonably. Subsequently, we extract the important influencing factors by using social network analysis, thus evaluating their importance quantitatively and further clarifying the elements of simulation. Finally, we develop an agent-based military industry logistics simulation system by taking the modeling results on influencing factors of battle damage as simulation model input, and obtain simulation model output, i.e., new battle damage laws knowledge, thus verifying feasibility and effectiveness of the proposed method. The results show that this method can be used to support human decision-making and action.     

军用飞机采购价格的RBF神经网络预测

分析了现代军用飞机采购价格估算中存在的问题.应用基于k-均值聚类算法的RBF神经网络建立了军用飞机采购价格预测模型,并采用该模型对某型军用飞机采购价格进行了预测.与多元线性回归和BP神经网络的预测结果对比,建立的新型军用飞机采购价格预测模型具有更高的预测精度,为军用飞机采购价格预测提供了一种新的有效方法.     

基于代理群的军事自组网拓扑搜索算法

分析现有网络管理模式的基础上,针对军事移动自组网的特点,提出了一种基于动态代理群的网络管理模式,并实现了动态代理群网络管理模式的网络拓扑结构搜索算法,最后对该拓扑搜索算法的有效性、正确性及复杂度进行分析论证.实验证明该算法可以很好地解决节点频繁移动带来的需要实时反映网络拓扑结构的问题.     

网络中心战条件下舰艇编队武器系统可靠度分析

利用可靠度模型对网络化条件下舰艇编队跨平台组织武器通道的可靠度进行了分析。分析表明,在网络中心战条件下舰艇编队武器系统的可靠度有了大幅度的提升。     

一种控制系统的综合故障诊断方法

提出了一种控制系统的综合故障诊断方法 ,用以解决复杂系统的描述困难与诊断准确性问题。通过将控制系统故障检测与诊断分解为一个多层次的过程 ,综合应用因果网络方法与强跟踪滤波器方法 ,实现了两种方法优缺点的互补。仿真表明 ,该方法解决复杂控制系统故障检测与诊断问题是有效的     

基于BP神经网络的防空导弹火力单元阵地评价模型

建立了防空导弹阵地评价的指标体系。对应用人工神经网络解决防空导弹阵地评价的方法问题进行了探讨 ,并且给出了神经网络的评价模型及算法。实例证明该方法是可行的。