基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

Nonreciprocal properties of 1D magnetized plasma photonic crystals with the Fibonacci sequence

In this paper, the nonreciprocal properties of a novel kind of 1D magnetized plasma photonic crystals(MPPCs) with the Fibonacci sequence are investigated. The isolation of the proposed 1D MPPCs is also used to analyze the nonreciprocal properties. Compared to the conventional 1D MPPCs with periodic structure, the nonreciprocal performance can be significantly improved.The effects of several parameters of the proposed 1D MPPCs on the nonreciprocal properties are studied by the transfer matrix method, which includes the incident angle, order of the Fibonacci sequence, plasma frequency, plasma cyclotron frequency and plasma filling factor. The obtained results show that the nonreciprocal propagation properties can be improved by increasing the values of the plasma cyclotron frequency and incident angle, but they will worsen by blindly increasing the order of the Fibonacci sequence, plasma frequency and filling factor of plasma.The peaks of transmittance also are obviously reduced. In addition, the value of isolation will increase with increasing the incident angle, order of Fibonacci sequence, plasma frequency and plasma filling factor. However, when the plasma cyclotron frequency is increased, the value of isolation will be increased at lower frequencies, but is almost unchanged at higher frequencies.     

Multicriteria decision-making using Archimedean aggregation operators in Pythagorean hesitant fuzzy environment

In multicriteria decision-making (MCDM), the existing aggregation operators are mostly based on algebraic t-conorm and t-norm. But, Archimedean t-conorms and t-norms are the generalized forms of t-conorms and t-norms which include algebraic, Einstein, Hamacher, Frank, and other types of t-conorms and t-norms. From that view point, in this paper the concepts of Archimedean t-conorm and t-norm are introduced to aggregate Pythagorean hesitant fuzzy information. Some new operational laws for Pythagorean hesitant fuzzy numbers based on Archimedean t-conorm and t-norm have been proposed. Using those operational laws, Archimedean t-conorm and t-norm-based Pythagorean hesitant fuzzy weighted averaging operator and weighted geometric operator are developed. Some of their desirable properties have also been investigated. Afterwards, these operators are applied to solve MCDM problems in Pythagorean hesitant fuzzy environment. The developed Archimedean aggregation operators are also applicable in Pythagorean fuzzy contexts also. To demonstrate the validity, practicality, and effectiveness of the proposed method, a practical problem is considered, solved, and compared with other existing method.     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.     

Investigation of non-isothermal crystallization,dynamic mechanical and dielectric properties of poly(ether-ketone) matrix composites

ABSTRACT

Poly(ether-ketone)/hexagonal boron nitride (h-BN) composites reinforced with micrometer-sized h-BN particles were investigated. The composites exhibited glass transition temperature (T_g) and thermal stability over 160°C and 560°C, respectively. The melting point and peak crystallization temperatures of the composites decreased up to 17°C and 12°C, respectively. The linear CTE of the composites decreased both below and above the T_g. The storage modulus increased with increasing h-BN content at all temperatures (50–250°C). The composites possessed excellent dielectric properties with insignificant dispersion with increasing frequency. Thus, resultant composites are promising candidates for the printed circuit boards/electronic substrates.     

Three-dimensional graphene-carbon nanotube reinforced ceramics and computer simulation

Graphene and carbon nanotube (CNT) have been the striking reinforcements for ceramics as a function of their unique structures together with exceptional multi-performance. However, the reinforcing efficiency could only be fully confirmed if they were homogenously dispersed throughout the whole ceramic matrix. The poor dispersion properties of graphene and CNT restricted the further development of advanced carbon nanomaterials reinforced ceramic. Very recently, the hybridization of graphene and CNT was proposed to reach full utilization of graphene and CNT through preventing the aggregations in ceramic. Herein, this review discussed the preparation techniques, composition, microstructures and properties of hybrid graphene-CNT (HGC) reinforced ceramic, highlighting the hybridization strategies of graphene and CNT. Additionally, the computer simulation was employed to advance the development of three–dimensional HGC reinforced ceramics.     

Constitutive model and failure criterion for orthotropic ceramic matrix composites under macroscopic plane stress

To predict the nonlinear stress-strain behavior and the rupture strength of orthotropic ceramic matrix composites (CMCs) under macroscopic plane stress, a concise damage-based mechanical theory including a new constitutive model and two kinds of failure criteria was developed in the framework of continuum damage mechanics (CDM). The damage constitutive model was established using strain partitioning and damage decoupling methods. Meanwhile, the failure criteria were formulated in terms of damage energy release rate (DERR) in order to correlate the failure property of CMCs with damage driving forces, and the maximum DERR criterion and the interactive DERR criterion were suggested simultaneously. For the sake of model evaluation, the theory was applied to a typical CMC with damageable and nonlinear behavior, that is, 2D-C/SiC. The damage evolution law, strain response and rupture strength under incremental cyclic tension along both on-axis and off-axis directions were completely investigated. Comparison between theoretical predictions and experimental data illustrates that the newly developed mechanical theory is potential to give reasonable and accurate results of both stress-strain response and failure property for orthotropic CMCs.