论新型智能消防系统在大空间建筑中的应用

结合大空间建筑的消防特点,对防火墙与防火卷帘、普通闭式系统、雨淋系统等传统大空间建筑消防设施存在的问题进行了详细的分析,并对大空间建筑新型智能型消防灭火系统的优越性进行了探讨,提出应用建议。     

基于相参特性的雷达信号分选中“增批”问题研究

一部雷达可以在不同时间段内大范围跨波段工作,基于现行信号分选方法,常出现"增批"问题。利用信号相参特性,基于雷达本振初相不变性原理,提出求和曲线斜率判别法,对同一辐射源和不同辐射源信号进行分选仿真,并进行了误差分析。仿真结果表明,方法能够解决信号分选过程中的"增批"问题,当信噪比高于5 dB,并且测频误差在小于200 kHz时,分选准确率可以达到98%以上。     

基于非参估计的局部滑窗双参CFAR目标检测

针对特定杂波概率模型不能有效的描述SAR图像背景杂波这一问题,提出了一种基于非参估计的局部滑窗双参CFAR目标检测算法。该方法首先用非参估计方法逼近SAR图像局部背景,完成对局部背景的精确建模;在此基础上,理论推导了局部双参CFAR检测算法的阈值,设计了阈值求解的数值算法。对典型目标图像进行实验,结果表明,该方法检测速度较快、精度较高。     

关于新时期反恐情报分析模型的建构

反恐情报分析是反恐情报工作的重中之重,建构反恐情报分析模型不仅是新时期公安大情报发展的必由之路,同时也为新时期反恐斗争拓宽了解决途径。     

污水中重金属离子浓度软测量的BP算法

在重金属离子检测仪器及其工作原理研究的基础上,对污水中重金属离子浓度测量方法进行了分析研究,分析了采用软测量技术的可行性和必要性,针对水质测量具有非线性、大时变和多滞后的特点,提出了基于BP神经网络的污水中重金属离子浓度软测量建模方法,建立了软测量模型,并通过仿真分析和实验验证了该方法的实用性.     

基于遗传算法的跳频通信侦察技术

为了识别跳频序列,提出了采用遗传算法并结合跳频信号特点来完成跳频序列的分析与重组,从而实现跳频信号的侦察。在采用传统遗传算法的基础上,针对序列特点提出了5种改进手段,从而大大提高了算法的效率和可用性。最后利用采样的语音信号,对分析与重组过程进行模拟来验证算法的可行性。     

基于磁强计测量的滚转弹药姿态估计

为了实时获得滚转弹药的飞行姿态信息,提出了一种速率陀螺与磁强计组合的姿态测量方案。该方案采用磁强计获得大地磁场强度在弹体三轴的投影及其变化率,结合刚体转动运动模型,利用最优估计技术获得了滚转弹药姿态信息。与单点测量方法相比,最优估计方法综合了测量信息序列,不会出现反三角函数双值失控现象,并可获得更高精度。仿真表明:陀螺无漂移时,俯仰角、偏航角的解算精度小于0.1°;采用低成本陀螺含漂移时,姿态角的解算精度小于0.4°。     

Fuzzy recognition of missile borne multi-line array infrared detection based on size calculating

In order to improve the infrared detection and discrimination ability of the smart munition to the dy-namic armor target under the complex background, the multi-line array infrared detection system is established based on the combination of the single unit infrared detector. The surface dimension features of ground armored targets are identified by size calculating solution algorithm. The signal response value and the value of size calculating are identified by the method of fuzzy recognition to make the fuzzy classification judgment for armored target. According to the characteristics of the target signal, a custom threshold de-noising function is proposed to solve the problem of signal preprocessing. The multi-line array infrared detection can complete the scanning detection in a large area in a short time with the characteristics of smart munition in the steady-state scanning stage. The method solves the disadvan-tages of wide scanning interval and low detection probability of single unit infrared detection. By reducing the scanning interval, the number of random rendezvous in the infrared feature area of the upper surface is increased, the accuracy of the size calculating is guaranteed. The experiments results show that in the fuzzy recognition method, the size calculating is introduced as the feature operator, which can improve the recognition ability of the ground armor target with different shape size.     

Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades.During that period,they have increased in number,complexity,and agility,while their design and development have been processes well kept under secrecy.As a consequence,limited data(sets)regarding these incidents are available.Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations(such as attack,target identification and selection,and collateral damage),still methodologies and models are needed in order to plan,execute,and assess them in a responsibly and legally compliant way.Based on these facts,it is the aim of this article to propose a model that i))estimates and classifies the effects of cyber operations,and ii)assesses proportionality in order to support targeting decisions in cyber operations.In order to do that,a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical-military experts.The proposed model was evaluated on two cyber operations use cases in a focus group with four technical-military experts.Both the design and the results of the evaluation are revealed in this article.     

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes.