Crystal orientation of β-phase isotactic polypropylene induced by magnetic orientation of N,N′-dicyclohexyl-2,6-naphthalenedicarboxamide

Melt crystallization of isotactic polypropylene (iPP), containing crystallites of N,N′-dicyclohexyl-2,6-naphthalenedicarboxamide (DCNDCA) as a nucleating agent of the the β-phase iPP crystal, is carried out under a magnetic field (6 T) to obtain the alignment of the iPP crystal induced by magnetic alignment of DCNDCA. In a previous paper, DCNDCA was reported to undergo magnetic alignment in a liquid suspension. The obtained iPP sample exhibits alignment of the β-phase crystal with the c-axis aligned perpendicular to the magnetic field. The comparison of this alignment of iPP with the reported magnetic alignment of DCNDCA indicates that the β-phase crystal grows epitaxially on the DCNDCA crystal. The (330)_β plane of the iPP crystal lies on the bc-plane of the DCNDCA crystal in which the direction of the c-axis of the iPP coincides with the direction of the b-axis of the DCNDCA crystal.     

精密深孔表面环状硬挤条纹的产生机理与控制

本文建立了不同条件下环状硬挤条纹的生成模型,详细分析了其产生机理.用理论解析和试验验证了所建模型的可靠性,最后提出了控制其产生的方法.     

联合知识与CNN的遥感影像目标检测研究综述

目标检测是遥感影像智能解译的重要内容,是将影像转换为信息的关键环节。基于知识的方法是遥感影像目标检测的传统经典方法,而基于卷积神经网络的深度学习方法则是近年来逐步兴起并迅速大范围应用的主流方法。介绍了基于几何知识、上下文知识、辅助知识、综合知识的方法,以及一阶段、两阶段的卷积神经网络方法,重点论述了联合知识与卷积神经网络的新方法,并对改进遥感影像数据集、调整算法网络框架、实现目标上下文推理等三种具体应用形式进行了详细介绍。对联合知识与卷积神经网络方法的遥感影像目标检测方法进行了展望。     

Additional kernel observer: privilege escalation attack prevention mechanism focusing on system call privilege changes

International Journal of Information Security - Cyberattacks, especially attacks that exploit operating system vulnerabilities, have been increasing in recent years. In particular, if administrator...     

Pervaporation via silicon-based membranes: Correlation and prediction of performance in pervaporation and gas permeation

Pervaporation (PV) is a membrane technology that holds great promise for industrial applications. To better understand the PV mechanism, PV dehydrations of various types of organic solvents (methanol, ethanol, iso-propanol, tert-butanol, and acetone) were performed on five types of organosilica and two types of silicon carbide-based membranes, all with different pore sizes. Water permeance was dependent on the types of organic aqueous solutions, which suggested that organic solvents penetrated the pores and hindered the permeation of water. In addition, water permeance of various types of membranes in PV was well correlated with hydrogen permeance in single-gas permeation. Furthermore, a clear correlation was obtained between the permeance ratio in PV and that in single-gas permeation, which was confirmed via the modified-gas translation model. These correlations make it possible to use single-gas permeation properties to predict PV performance.     

Spatial distribution of silica fillers in phase-separated rubber blends investigated by three-dimensional elemental mapping

The distribution of nano-sized silica in binary rubber blends is characterized by scanning transmission electron microscopy (STEM) tomography combined with energy dispersive X-ray spectrometry (EDX). 3D distribution of silica is visualized by STEM-EDX tomography with the tilt-series of silicon elemental maps, while the phase-separated morphologies of polyisoprene rubber (IR) and styrene-butadiene rubber (SBR) are visualized by STEM-tomography in high-angle-annular-dark field (HAADF) mode. The combination of STEM-EDX and STEM-HAADF tomography enables us to determine the distribution of silica between the two rubber phases quantitatively even with high contents of silica up to 70 phr (weight parts per hundred rubber). It is found that silica is preferentially distributed in the SBR phase, but it is also distributed in the IR phase when the IR fraction in the total rubber components is higher than 40 wt%. The preferential distribution of silica in the SBR phase improves the dispersion of the IR domains. This is the first use of this technique for a multicomponent polymer system, showing the advantage to characterize the complicated multicomponent polymer composite morphologies.     

基于威胁传播采样的复杂信息系统风险评估

互联网时代的信息安全已成为全社会关注的问题之一。信息系统是信息的载体,为有效评估大规模分布式复杂信息系统的风险,构建了一种基于威胁传播采样的复杂信息系统风险评估方法。该方法考虑到威胁在复杂信息系统中传播时,对资产结点的转移状态以及资产结点发出的威胁传播边进行采样来生成威胁传播树（threat propagation trees ,TPT ）,然后通过计算威胁传播树中各资产结点的期望损失以及威胁传播树的概率来对整个复杂信息系统进行风险评估。实验分析表明,基于威胁传播采样的复杂信息系统风险评估方法,在生成威胁传播树时具有高效的时间效率,能够对复杂信息系统进行客观准确的风险评估,且在对复杂信息系统资产结点制定安全防护策略时,能够为安全风险管理者提供较为合理的安全指导建议。     

Development of a flexible autonomous robot without sensors or controllers

Recently, various robots with many degrees of freedom, such as rescue robots and domestic robots, have been developed and used in practical applications. It is difficult to control such robots autonomously in real environments, because in order to control the many degrees of freedom, we have to observe many states, calculate huge amounts of information, and operate many actuators. In this study, we consider a flexible robot without sensors or controllers that can determine the inclination of a slope and climb up the slope. In order to demonstrate the effectiveness of the proposed framework, we have developed a prototype robot and conducted experiments. The result indicates that the robot could determine the inclination and climb up a gentle slope autonomously. Thus, we have realized an autonomous robot that has no explicit sensors or controllers.     

Estimation of time-dependent input from neuronal membrane potential

The set of firing rates of the presynaptic excitatory and inhibitory neurons constitutes the input signal to the postsynaptic neuron. Estimation of the time-varying input rates from intracellularly recorded membrane potential is investigated here. For that purpose, the membrane potential dynamics must be specified. We consider the Ornstein-Uhlenbeck stochastic process, one of the most common single-neuron models, with time-dependent mean and variance. Assuming the slow variation of these two moments, it is possible to formulate the estimation problem by using a state-space model. We develop an algorithm that estimates the paths of the mean and variance of the input current by using the empirical Bayes approach. Then the input firing rates are directly available from the moments. The proposed method is applied to three simulated data examples: constant signal, sinusoidally modulated signal, and constant signal with a jump. For the constant signal, the estimation performance of the method is comparable to that of the traditionally applied maximum likelihood method. Further, the proposed method accurately estimates both continuous and discontinuous time-variable signals. In the case of the signal with a jump, which does not satisfy the assumption of slow variability, the robustness of the method is verified. It can be concluded that the method provides reliable estimates of the total input firing rates, which are not experimentally measurable.     

基于矢量模型的语音线性预测研究

研究语音参数线性预测的并行处理问题。通过把语音源序列的相邻样本分组能够构成一个均方差平稳的语音向量自回归序列,在Hilbert空间中运用正交投影原理导出具有高度并行处理能力的一预测编码策略,由此可推出参数线性预测的并行处理自适应算法。同传统格型算法相比,这种算法的计算复杂度及存贮量有明显改善。最后通过仿真运算检测了算法的性能。