基于深度置信网络的机载外挂振动预计方法

针对机载外挂挂飞振动环境与飞行高度、马赫数的非线性关系,提出了一种基于深度置信网络(DBN)的振动预计方法。从仿真的角度根据GJB 150.16A中的经验公式产生7 470组数据,分别建立线性回归、多项式回归和DBN振动预计模型,并优化网络隐含层节点数,对比分析了三种方法的方均根误差、平均绝对误差和平均相对误差。利用工程案例验证了DBN预计方法的可行性和准确性。结果表明:DBN模型能够充分表征外挂在挂飞过程中振动与影响因素的非线性关系,整体预计效果优于线性回归和多项式回归,且预计平均相对误差在2.24 dB左右,误差减小50%以上。该方法能够为机载外挂振动环境的预计提供一种新的思路,支撑航空武器装备的环境适应性分析、设计与验证。     

辽宁省大学生宗教信仰现状分析

宗教信仰对大学生的成长有着重要的影响。通过对辽宁省大学生宗教信仰的现状进行广泛深入的调查,对大学生宗教信仰的表现及特征进行了理性的分析,探讨了大学生宗教信仰的引导与教育的对策。     

一种多码率QC-LDPC码译码结构设计与实现

为了满足在一个系统中使用多码率LDPC(Low Density Parity Check)码字的需求,设计了一个7Kbit长度多码率LDPC码的译码器,分析了各种码率之间校验矩阵的相似性,提出了复合译码结构中变量节点运算单元、校验节点运算单元以及迭代存储器单元的复用方案.通过在变量节点运算单元以及校验节点运算单元输入端增加若干选通开关,就可以使这些运算单元适于多码率的处理.通过管脚的选择,此译码器支持非规则0.4码率、非规则0.6码率以及非规则0.8码率3种工作译码模式,并用Altera公司的FPGA进行了实现.综合结果表明,所提出的复合结构在不损伤单码率译码性能的前提下,仅用略多于0.8码率LDPC码单独译码的硬件资源实现了3种码率码字的译码.      

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

An information-volume-based distance measure for decision-making

D-S evidence theory, as a general framework for reasoning with uncertainty, allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy measure. However, the mass assignments given by unknown information sources are disordered. How to measure the difference between the mass assignments has aroused people’s interest. In this paper, inspired by the information volume, a novel distance-based measure is proposed to measure the difference between mass assignments. The method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass assignments. At the same time, it is verified that the measure not only meets the properties of distance, but also proves the superiority of the proposed Information Volume Distance (IVD) through simulation experiments. Meanwhile, in the process of information fusion, the reliability of each source could be quantified through IVD. Therefore, based on IVD, a new multi-source information algorithm is proposed to solve the problem of multi-source information fusion. Moreover, algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.