一种跨公司航天软件缺陷预测方法

为提高航天软件测试的效率和质量，针对同公司航天软件数量少、研制周期长的特点，提出了一种跨公司航天软件缺陷预测方法。从航天软件背景信息复杂、规模大、功能独立等特征出发，提出基于静态分类缺陷预测的模型构建思想。引入迁移学习方法，利用最近邻分类器和数据引力模型，对训练数据的分布特征进行修正，提高训练数据与目标数据的相似性；为提高模型的泛化能力以适应目标数据的多样性，提出在训练数据中加入少量目标数据用于模型训练。将该方法在实际工程中进行应用，实验结果表明，与已有软件缺陷预测方法相比，该方法在保持较低误报率（不高于0.3）的情况下可有效提高召回率（接近0.6），整体可信度得到有效增强（G- measure超过0.6），方法稳定度高，泛化能力较强；本方法在实际工程中对测试规模影响可控，测试效率得到提高。     

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.     

正则化图形模糊聚类及鲁棒分割算法

针对现有图形模糊聚类算法合理性差和抗噪能力弱的问题，提出嵌入对称正则项的图形模糊聚类鲁棒算法。将样本聚类所对应的中立度与拒分度相结合构造对称正则项，嵌入现有图形模糊聚类所对应的目标函数；同时，利用像素邻域所对应的均值信息辅助当前像素聚类并构造了空间信息约束正则项，采用拉格朗日乘子法获得正则化图形模糊聚类鲁棒分割算法。不同噪声干扰图像分割结果表明，所建议的分割算法是有效的，相比现有的鲁棒模糊聚类分割算法具有更强的抑制噪声能力。     

基于光谱分类模型的保险杠物证无损研究

采用中红外光谱结合化学计量学的方法对车用保险杠碎片进行鉴别，分别对52个车用保险杠碎片样本的全波段光谱数据、指纹区光谱数据和主成分分析降维后的光谱数据建立Fisher判别分析和K近邻算法2种分类模型，并对分类结果进行比较。结果表明，主成分分析提取特征变量后构建的分类模型，分类的准确率更高，对聚丙烯（PP）、PP/滑石粉、PP/滑石粉/碳酸钙（CaCO₃）3种类型的样本分类准确率达到92.3 %，对PP/滑石粉类型中的10种品牌样本分类准确率达到88.9 %，分类结果理想；在构建的2种分类模型中，Fisher判别分析模型的分类率远高于K近邻算法模型，分析认为K近邻算法模型受到样本不均衡的影响；中红外光谱结合化学计量学可以实现对车用保险杠碎片的准确区分，且满足快速、无损的检验要求。     

{X,Y}-Cyclacene Graphs with Next Nearest Neighbor Interactions

Eigensolutions of {X( = C,B,N),Y( = C,B,N)}-cyclacene graphs with next nearest neighbor (nnn) interactions have been obtained in analytical forms by adapting n-fold rotational symmetry followed by two-fold rotational symmetry (or a plane of symmetry). Expressions of eigensolution indicate the subspectral relationship among such cyclacenes with an even number of hexagonal rings e.g., eigenvalues of {X,Y}-di-cyclacene are found in the eigenspectra of all such even cyclacenes. Total π-electron energies and highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO–LUMO) gaps are calculated using the analytical expressions obtained and are found to vary negligibly with the variation of nnn interactions in such cyclacenes. Total π-electron energy is found to increase due to increase in restriction intensity of nnn interactions, whereas the HOMO–LUMO gap of polyacenecs having the even number of hexagonal rings and with one electron at each site (atom) decreases with increase in the restriction intensity since such systems contain degenerate half-filled HOMO (bonding or nonbonding) that are much more vulnerable for perturbations imposed through nnn interactions.     

Data-oriented multi-index Hashing

The multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes. However, it is based on the dataset codes uniform distribution assumption, and will lower efficiency in dealing with non-uniformly distributed codes. In this paper, we propose a data-oriented multi-index hashing method. We first compute the correlations between bits and learn adaptive projection vector for each binary substring. Then, instead of using substrings as direct indices into hash tables, we project them with corresponding projection vectors to generate new indices. With adaptive projection, the indices in each hash table are nearly uniformly distributed. Besides, we put forward an entropy based measurement to evaluate the distribution of data items in each hash table. Experiments conducted on reference large scale datasets show that compared to the MIH the time performance of our method can be 36.9％～87.4％ better .     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.     

基于核典型相关分析融合的XLPE电缆局部放电模式识别

提出使用核典型相关分析方法提取XLPE电缆接头局部放电信号PRPD图谱特征信息,并使用K最近邻分类算法实现不同绝缘缺陷模式的高准确率识别。利用YJV-26/35 k V型电缆及其附件设计了4种典型绝缘缺陷,使用脉冲电流检测获取局部放电样本信息,绘制了PRPD图谱并应用于样本数据,研究不同特征向量下的识别效果,在适合维数最终获得较高识别正确率。相对于传统电力设备模式识别方法,不但可以有效反映信号非线性特征,并可以将多种特征进行有效融合,消除冗余特征。     

基于局部切空间排列和K-最近邻分类器的转子故障诊断方法

为了解决大型机械设备故障数据难以准确快速提取的问题,提出了一种基于局部切空间排列(LTSA)和K-最近邻分类器的转子故障诊断模型。首先基于转子的振动信号构造一个高维多征兆矩阵,利用LTSA提取高维矩阵的低维特征向量,映射在可视空间里;然后将提取的低维特征向量输入K-最近邻分类器进行故障模式识别。试验和数据降维仿真过程表明,该模型的准确度和快速性均优于LTSA和神经网络以及LTSA和支持向量机组成的故障诊断模型。