Compressive sensing SAR range compression with chirp scaling principle

Compressive sensing(CS) techniques offer a framework for the detection and allocation of sparse signal with a reduced number of measurements.This paper proposes a novel SAR range compression,namely compressive sensing with chirp scaling(CS-CS),achieving the same range resolution as conventional SAR approach,while using fewer range samplings.In order to realize accurate range cell migration correction(RCMC),chirp scaling principle is used to construct reference matrix for compressive sensing recovery.Additionally,error diagrams are designed for measurement of the performance of CS-CS,and some experiments of using real data are performed to deal with the errors caused by three conditions:SNR,sparsity and sampling.     

Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing(CS)shows that it is possible to recover a sparse signal from very limited measurements.Synthetic aperture radar(SAR)imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem.For multi-channel SAR imaging based on CS,each channel requires sufficient samples for separate imaging and the total number of samples could still be large.We propose an imaging algorithm based on distributed compressive sensing(DCS)that reconstructs scenes jointly under multiple channels.Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene,but also exploits the correlation among channels.It requires significantly fewer samples than multi-channel SAR imaging based on CS.If multiple channels offer different sampling rates,DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.     

SCS模型在山区小流域山洪灾害预报预警中的适用性分析

预报预警是山洪灾害防治中重要的非工程措施,是防灾减灾救灾的关键环节。选取分别代表秦巴山地水资源保护区、西南地震作用区、喀斯特地区、黄土高原超渗产流区、东南沿海台风影响区的湖北省丹江口官山河流域、四川省都江堰白沙河流域、贵州省望谟县望谟河流域、陕西省子洲县岔巴沟流域、广东省高州市马贵河流域这5个典型流域,通过构建改进的SCS模型进行历史山洪模拟,探讨该模型在小流域暴雨山洪预报预警中的广泛适用性。结果表明:基于SCS模型构建的小流域暴雨山洪预报方案中官山河、白沙河、望谟河、马贵河方案均为乙级方案,可用于正式发布预报;岔巴沟为丙级方案,可用于参考性预报。SCS模型在5个典型示范区的洪峰流量预报精度普遍在80%左右,高于我国山洪灾害洪峰流量预报平均水平(40%)。总体来说,SCS模型对各类典型流域的山洪模拟效果较好,能够用于山洪灾害预报预警业务。SCS模型结构简单,参数少,特别适用于无资料地区推广应用。     

高坝泄洪诱发场地振动响应分析及传播规律

为研究高坝泄洪诱发场地振动的问题,提出一种基于黏弹性人工边界的有限元数值模拟和水力学模型试验相结合的方法计算场地振动响应。以国内某工程为例,计算不同工况下场地振动响应,分析泄洪量和泄洪调度方式对场地振动的影响,揭示不同岩体条件下振动波的传播规律。结果表明:该工程各种泄洪方式都不会对周边居民的生活造成影响;泄洪量越大,下游场地振动响应越大,当3个溢洪洞泄洪条件一致时,采用2号洞泄洪最不利;同等泄洪条件下,采用4洞均分泄洪的振动响应小于按设计开度泄洪的振动响应;模型范围内放大振动波的主因是断层左右岩体性质的差异,断层的存在进一步加大了放大效应。     

中置式高压电容器柜改进设计

分析了南屯煤矿白马河风井变电所高压电容器柜原设计方案存在的问题,并对其进行了改进设计:选用适用于容性负载的真空断路器;添加放电线圈,并将放电线圈与电容器直接并联,使放电线圈与电容器组成一个完整的放电回路;添加接地开关,接地开关既有机械闭锁又有电气联锁,操作方便,安全性高。实际应用表明,改进设计后的高压电容器柜功能更加完善,更加安全、可靠。     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

遥感图像理想均衡化及图像质量定量评价

大多数原始的遥感影像由于其灰度分布集中在较窄的范围内,影像的细节不够清晰,对比度较低。为了使影像的灰度范围拉开或使灰度均匀分布,从而增大反差,增强影像细节信息,通常采用的方法为直方图均衡化。通过对信息熵定义的阐述,引出直方图均衡化的图像增强算法。通过分析传统直方图均衡化算法中存在的缺陷,进而基于分段映射思想提出一种改进的理想直方图均衡化算法。同时,为了对传统算法和改进算法进行定量化地分析比较,基于同时对比度以及人类视觉对比度分辨率限制和模糊数学的相关思想,分别提出基于加权几何平均数法的合成平均对比度和细节评价参数的定义。最后,采用同时对比度、基于加权几何平均数法的合成平均对比度以及细节评价参数作为定量评价的指标,对所提出的改进算法进行了定量评价。评价结果表明,该改进算法的图像增强效果优于传统的直方图均衡化算法。     

Chemical composition and bond structure of carbon-nitride films deposited by CH4/N2 dielectric barrier discharge

Carbon nitride films have been deposited by dielectric barrier discharge with a CH₄/N₂ gas mixture at different conditions. Fourier Transform Infrared (FTIR) spectroscopy, X-ray photo electron spectroscopy (XPS), Raman spectroscopy, Atomic force microscopy (AFM) and ellipsometry were used to systematically study chemical composition, bond structure and surface morphology of deposited films. Various bonds between carbon, nitrogen, hydrogen, and also oxygen were observed.