Isomers of C20: an energy profile III

Semiempirical calculations, at the PM3 level provided within the Winmopac v2.0 software package, are used to geometrically optimize and determine the absolute energies (heats of formation) of a variety of C(20) isomers that are predicted to exist in and around the ring and cage isomers. Using the optimized Cartesian coordinates for the ring and the cage isomers, a saddle-point calculation was performed. The resulting energy profile, consisting of a series of peaks and valleys, is used as a starting point for the identification and location of fifteen additional isomers of C(20) that are predicted to be energetically stable, both via geometry optimizations and force constant analysis. These additional isomers were subsequently determined to lie adjacent to one another on the potential surface and establish a step-wise transformation between the ring and the cage. Transition-state optimization of the Cartesian coordinates at the saddle point between adjacent isomers was performed to quantify the energy of the transition state. The step-wise process from one isomer to another, which extends out over the three-dimensional surface, is predicted to require approximately 15% less energy than that of the direct, two-dimensional transformation predicted in the bowl-cage profile. However, the net atomic rearrangement for the step-wise process is about four times greater than that of the direct process. Although less in energy, the amount of atomic rearrangement in the step-wise process would make the occurrence of such a route prohibitive. Utilizing the direct distance separating the three primary isomers (ring, bowl, cage), the method of triangulation is performed to quantitatively position other C(20) structures on the potential surface, relative to the ring, bowl, and cage isomers.     

Solid state extrusion of thermoplastic elastomers 4

A solid state extrusion technique is applied as to produce oriented block copoly(ether ester) under various physical conditions. The morphology of the extruded samples is characterized in relation to the extrusion parameters and hard segment compositions of the polymer, using thermal analysis and X-ray methods. The lateral dimensions of the crystalline domains are found to be approximately 150 Å depending on the extrusion conditions. The statistics of the long range periodicity of the structure along the extrusion direction is in agreement with a one-dimensional two phase model, the crystalline portion of which does not vary much in thickness (35 – 45 Å). The unexpected increase in the long period and the thermal shrinkage suggest the existence of strained interlamellar amorphous chains (tie molecules). The observed variations in tensile properties are interpreted under the assumption that both the number of such tie molecules and their fully extended lengths are determined by the hard segment composition and the extrusion conditions. It is also argued that the increase in the glass transition temperature is not only a function of the composition of hard segments in the amorphous phase but also of the number of strained tie molecules.Herrn Dr. Dr. h. c. H. Hellmann zum 70. Geburtstag gewidmet.Part 3 cf. lit [11]     

Calculation of harmonic force constants,vibrational frequencies and integrated intensities of some organic molecules using the MINDO—Forces method

The harmonic force constants, vibrational frequencies and integrated intensity ratios of CH₂, H₂O, CH₂O, C₂H₂, CO₂, HCN, CH₃, CH₄, and C₂H₄ have been calculated using the MINDO—FORCES program and the Pulay method for the calculation of the molecular force constants. The results obtained are in general quite satisfactory when compared with available literature values. The results are, however, not as satisfactory in case of molecules containing heteroatoms, due to the neglect of some dipolar repulsion integrals for the heteroatoms by the MINDO/3 method. Calculated integrated intensities for CH₃ and C₂H₄ agree well with experimental results. The calculated integrated intensities for other molecules are obtained for the first time and no comparison with published data is therefore possible.Part of the M.Sc. Thesis of K. H. A. 1978.     

Reconstructing small perturbations of an obstacle for acoustic waves from boundary measurements on the perturbed shape itself

We derive relationships between the shape deformation of an impenetrable obstacle and boundary measurements of scattering fields on the perturbed shape itself. Our derivation is rigorous by using a systematic way, based on layer potential techniques and the field expansion (FE) method (formal derivation). We extend these techniques to derive asymptotic expansions of the Dirichlet-to-Neumann (DNO) and Neumann-to-Dirichlet (NDO) operators in terms of the small perturbations of the obstacle as well as relationships between the shape deformation of an obstacle and boundary measurements of DNO or NDO on the perturbed shape itself. All relationships lead us to very effective algorithms for determining lower order Fourier coefficients of the shape perturbation of the obstacle.     

双层伞状开孔的微测辐射热计红外吸收分析

建立了具有伞状吸收层结构的微测辐射热计探测单元的红外吸收模型。基于光学导纳矩阵法和阻抗匹配理论,采用三维电磁仿真软件CST,对伞状结构不同开孔尺寸和形状下模型的红外吸收特性进行了分析。结果表明,双层伞状开孔微测辐射热计光学性能与伞状结构开孔大小有密切的关系。该伞状微测辐射热计中引入开孔后,在保持较高的红外吸收特性的基础上,减少了探测单元热容,从而提升了器件响应速度。最终所得探测单元在8~14μm红外波段内的平均吸收率为85%,满足超大规模小像元非致冷红外焦平面探测器的设计要求。     

基于残差神经网络的无人机射频信号识别

针对民用小型无人机对低空领域安全造成威胁及无人机视觉上识别难的问题,提出了一种无人机射频信号识别的方法,使用深度学习技术学习无人机与控制器之间的射频信号特征来识别无人机。首先将射频信号数据集中的无人机信号进行预处理操作,接着使用残差神经网络进行模型训练,最后使用训练好的网络模型对无人机信号进行识别验证。实验结果表明,该方法识别无人机是否存在的准确率达到99.8%,识别无人机型号的准确率达到91.1%,识别无人机运行模式的准确率达到70.3%,且该方法具备较强的鲁棒性和环境抗干扰能力,性能明显优于基准方法。     

引入自上向下特征融合的小目标检测算法

针对原始SSD(Single Shot Multibox Detector)算法未充分利用各特征层之间关系导致浅层特征层缺乏小目标语义信息的问题,为了提高对小目标的检测能力,提出了一种结合PANet多尺度特征融合网络和自上向下特征融合路径的TTB-SSD(Top to Bottom SSD)改进算法。首先,使用PANet多尺度特征融合网络对特征进行反复提取,从而获得丰富的多尺度语义信息;然后,使用一种深层特征融合模块将浅层特征层的空间信息传递到深层特征层,进而更准确地对小目标进行定位;最后,为了增强浅层特征层的语义信息,构造了自上向下的特征融合路径,从而强化浅层对小目标检测的准确率。实验结果表明,在PASCAL VOC2007测试集检测的mAP(Mean Average Precision)值达到80.5%,对目标的mAP较原始SSD提高了5.7%,证明了该算法对小目标检测的有效性。     

基于自适应对比度增强的红外小目标检测网络

红外探测系统需要尽早发现目标以便及时拦截,但是红外图像上的小目标检测是一个挑战十足的任务。为了提高检测准确率,提出一种基于自适应对比度增强的红外小目标检测方法。为了利用自注意力机制和卷积各自的优势,设计了一个高效的特征提取网络和一个面向小目标的检测头。同时为了解决实际应用中出现的弱目标,在检测子网络前添加了一个图像预处理子网络,该模块可以自适应地调节图像对比度。在红外空中小目标数据集上的实验表明,提出的方法能达到93.76%的检测精度,与经典的检测方法相比,能够更好地平衡检测精度和召回率,证明了方法的巨大应用潜力。     

双重JPEG压缩图像篡改区域检测与定位

对JPEG(joint photographic experts group)图像实施篡改往往会产生双重JPEG(double JPEG,DJPE) 压缩痕迹,分析该痕迹有助于揭示图像压缩历史并实现篡改区域定位。现有算法在图像尺寸较小和质量因子(quality factor,QF) 较低的时候性能不佳,对两个QF的组合情况存在限制。本文提出了一种端到端的混合QF双重JPEG压缩图像取证网络,命名为DJPEGNet。首先,使用预处理层从图像头文件中提取表征压缩历史信息的量化表 (quantization table,Qtable) 特征,将图像从空域转换至DCT(discrete cosine transform)域构造统计直方图特征。然后,将两个特征输入到由深度可分离卷积和残差结构堆叠而成的主体结构,输出二分类结果。最后,使用滑动窗口算法自动定位篡改区域并绘制概率分布图。实验结果表明,在使用不同Qtable集生成的小尺寸数据集上,DJPEGNet所有指标均优于现有最先进的算法,其中ACC提高了1.78%,TPR提升了2.00%,TNR提升了1.60%。     

基于超像素级卷积神经网络的多聚焦图像融合算法

该文提出了基于超像素级卷积神经网络(sp-CNN)的多聚焦图像融合算法。该方法首先对源图像进行多尺度超像素分割,将获取的超像素输入sp-CNN,并对输出的初始分类映射图进行连通域操作得到初始决策图;然后根据多幅初始决策图的异同获得不确定区域,并利用空间频率对其再分类,得到阶段决策图;最后利用形态学对阶段决策图进行后处理,并根据所得的最终决策图融合图像。该文算法直接利用超像素分割块进行图像融合,其相较以往利用重叠块的融合算法可达到降低时间复杂度的目的,同时可获得较好的融合效果。