基于双向耦合扩散的自适应图像插值

提出了一种自适应的双向耦合扩散算法:沿着等照度线(边缘)的梯度方向实施反向扩散以增强边缘,而相反地沿切线方向实施正向扩散以去除人工锯齿。为了消除这2个相反的扩散力彼此之间的冲突,将此算法分裂为一种耦合的格式;同时,为了保持图像特征,利用图像的方向导数局部地调整非线性扩散系数。最后将上述算法应用于图像插值。实验结果显示,相比于传统的图像插值算法和一些相关的偏微分方程模型,此图像插值算法可以显著地提高被插值图像的视觉质量。     

Infrared image simulation of ground maneuver target and scene

Infrared scene simulation has extensive applications in military and civil fields. Based on a certain experimental environment, object-oriented graphics rendering engine (OGRE) is utilized to simulate a real three-dimensional infrared complex scene. First, the target radiation of each part is calculated based on our experimental data. Then through the analysis of the radiation characteristics of targets and related material, an infrared texture library is established and the 3ds Max software is applied to establish an infrared radiation model. Finally, a real complex infrared scene is created by using the OGRE engine image rendering technology and graphic processing unit (GPU) programmable pipeline technology. The results show that the simulation images are very similar to real images and are good supplements to real data.     

一种混合策略的Pareto演化规划

提出一种多目标演化算法--混合策略Pareto演化规划(Mixed Strategies Pareto Evolutionary Programming,MSPEP).借鉴强度Pareto Ⅱ演化算法的个体比较技术,通过计算个体位序的Pareto强度值进行比较排序,混合策略变异机制用于指导算法有效搜索过程.标准测试函数的实验结果验证算法的通用性和有效性.算法搜索的解集能快速逼近Pareto最优前沿.     

Research of the ATR system based on the 3-D models and L-M BP neural network

Automatic target recognition （ATR） is an important issue for military applications, the topic of the ATR system belongs to the field of pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recog- nize and classify the typical targets in the battle field. The invariant features of Hu invariant moments and roundness were selected to be the inputs of the neural network because they have the invari- ances of rotation, translation and scaling. The pictures of the targets are generated by the 3-D mod- els to improve the recognition rate because it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can be implement ed in the ATR system and it has a high recognition rate and can be applied in real time.     

基于多层模糊综合评判的入侵检测系统报警验证

提出了一种基于多层模糊综合评判的报警验证方法，此方法不但可以有效地减少误报，还可以对不同情况下的无关报警进行滤除和标记等不同的操作，很好地处理验证过程中的各种不确定因素。所得的报警相关度参数有利于制定细致、灵活的报警处理策略，从而对报警进行更深入的自动化处理。     

基于双向模糊扩散的保持特征的图像锐化

本文基于模糊集合理论,提出了一个双向模糊扩散框架.这个框架沿着等照度线(边缘)的梯度方向实施模糊的反向扩散;而相反地沿切线方向实施确定的正向扩散.为了保持图像特征的自然过渡,利用图像的局部结构张量的特征值检测图像的不同特征,并进行相应的锐化处理.     

基于模糊综合评判的入侵检测报警信息处理

提出一种基于模糊综合评判的方法来处理入侵检测系统的报警信息、关联报警事件,并引入有监督的确信度学习方法,通过确信度来对报警信息进行进一步的过滤．通过对这些技术手段的综合使用,力求降低误报率和重复报警,逐步减轻网络管理员的工作强度．这种模糊评判所实现的事件关联有助于发现入侵者的行为序列,为事件威胁分析和入侵响应决策打下了基础,并有利于将不同安全产品集成在一起,实现网络系统的立体防御．