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.     

{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.     

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。     

基于最近邻与神经网络融合模型的软测量建模方法

软测量建模能够有效地解决生产过程中在线分析仪表测量滞后大、价格昂贵、维护保养复杂等问题。目前，基于数据驱动的神经网络是软测量建模的主要工具之一。而在建模数据的采集过程中，主导变量的采集相对辅助变量要困难得多，由此产生了大量缺失标签的数据。但传统的软测量建模方法却忽视了这些无标签数据，只利用少量的有标签数据建模，从而影响了模型的预测精度。为了解决标签缺失的问题，采用最近邻算法对无标签数据进行伪标记，同时设计了由卷积操作与门限循环单元神经网络（GRU）结合的网络结构来进一步利用无标签数据，提取不同时刻数据中的动态特征，提高神经网络的预测精度。最后将该方法应用于丙烯精馏塔塔顶丙烷浓度的预测，实验结果表明该模型能有效处理非线性动态系统的标签缺失问题，具有更高的预测精度。     

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.     

自然邻点插值在渗流场/应力场构造中的应用

计算渗流场或初始地应力场的网格一般要比实际结构分析的网格大，因此结构分析时需要将大网格中的渗流场或初始地应力场转换到小的结构分析网格中。若有大网格信息，就可以利用有限元形函数进行转换。但有时只有大网格结点坐标和相应的物理量，此时一般采用加权平均法进行转换，这种转换的精度不是太高。自然邻点插值法是基于给定结点的Voronoi图，通过自然邻点的坐标值构造插值函数，因此特别适合只有结点坐标和相应的物理量，而要将其应用于另一种网格模型的情况。给出了两种自然邻点插值的构造方式及其具体实现步骤。算例表明采用自然邻点插值构造场的精度很高。     

无网格自然邻接点法及其在岩土工程数值模拟中的应用

基于Laplace插值函数提出了一种类似于无单元伽辽金法的无网格方法——无网格自然邻接点法。该方法克服了自然单元法需要全域三角形网格以及无单元伽辽金法难以准确施加位移边界条件和材料不连续条件、形函数的计算复杂、权函数的选择困难等缺点,适合于考虑多种材料、多步施工过程等复杂岩土工程的自动数值模拟。详细讨论了这种无网格自然邻接点法的分析过程和基本理论,给出其在杆、梁、节理单元和材料不连续面等方面的处理办法,并用一些标准算例和实际的地下工程算例对本文方法的效率、精度和可靠性进行了验证。     

基于多尺度子带能量集特征的膛口波识别方法

面向实际复杂环境下的枪声探测应用,提出一种基于多尺度子带能量集特征的膛口波识别方法。采用一组低频子带滤波器增强膛口波信号,通过膛口波检测和波峰搜索确定候选膛口波的起点位置,并基于该起点以嵌套方式截取候选膛口波及其各子带分量的多尺度数据片段,用各片段数据的短时能量和能量比构成多尺度子带能量集特征输入支持向量机和k近邻分类器,进行膛口波和非膛口波识别。对372段外场实录枪声数据进行数值实验,结果表明：所提方法对膛口波识别的查全率、查准率均高于93%,加权调和平均高于0.95;两种分类器下的识别结果无明显差别,但所用特征维数和计算耗时却远低于常用的离散小波方法,更接近实际应用需求。     

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。