Two years of operational use of Subpixel Automatic Navigation of AVHRR scheme: accuracy assessment and validation

Automated techniques for satellite imagery navigation and co-location are especially required for environmental monitoring activities intensively using satellite data. In this work are presented the results obtained after 2 years of operational use of the Subpixel Automatic Navigation of AVHRR (SANA) scheme. An automatic method for accuracy assessment of satellite navigation techniques, which permits a preliminary evaluation of their performances, dealing with a large collection of test images is also proposed. The navigation accuracy assessment, performed by using a selection of small islands as reference points, is discussed.Results achieved over more than 400 Advanced Very-High-Resolution Radiometer (AVHRR) scenes confirm that the SANA scheme is a very accurate one (computed mean navigation error is generally about one AVHRR pixel). Furthermore, because of its high processing speed, it can be considered a suitable tool for intensive satellite data processing in multitemporal analyses, especially required for environmental studies as well as for operational monitoring purposes.     

莺-琼盆地的独特埋藏史

通过沉积速率线性外推剥蚀厚度恢复、脱压实和异常高压以及古水深校正的单井埋藏史和连井埋藏剖面及构造沉降分析,发现莺-琼盆地是个多旋回沉积盆地,其沉积物埋藏过程可分为早期缓慢浅埋和晚期快速深埋两大阶段.这种独特的埋藏历史不仅是储层孔隙增生与保存的主要控制因素之一,而且极大地影响了烃源岩于酪根热解反应路径、烃类的运移和聚集,异常高压的发育以及所发生的复杂的水(气)—岩相互作用.涸11-4油田位于南海北部湾盆地涸西南凹陷,距广西北海市103km,离涸10-3油田17km.油田于1993年9月19日投产,目前开发形势较好,产量、压力稳定.在开发实践中我们体会到,要提高油田开发水平,搞好油藏监测是非常重要的.1钻井和完井过程中的油藏监测     

一种降低电离层梯度影响的Hatch滤波算法

目前,地基增强系统（Ground-Based Augment System,GBAS）中的主要问题是：尽管使用载波平滑降低了伪距噪,利用差分技术消除了用户和基准站间的大部分共同误差,从而减少了差分改正值中的误差。但是电离层时间梯度和空间梯度于这两个过程中产生了额外的误差,阻碍了差分改正值质量的进一步提高。基于对电离层时间和空间梯度引起的额外误差的分析,并考虑平滑伪距噪声,文章提出了一种新的Hatch滤波方法,该滤波器的时间常数是随卫星仰角、电离层时间和空间梯度以及用户与参考站之间的距离而实时变化的。该方法降低了差分改正值误差,提高了定位精度。并且通过差分定位仿真并对比传统的单频Hatch滤波方法,发现使用该方法后,减小了差分校正值的误差。     

基于二维Otsu阈值修正的图像分割新方法

首先采用对二维直方图斜分割和查表的方法,解决了传统二维Otsu方法分割图像计算耗时,难以实时实现的缺点。其次基于灰度统计的思想,针对二维Otsu法处理小目标图像难以实现正确分割的缺点,提出了一种在实现过程中采用迭代的阈值修正新方法。最后设计了一种新型滤波器对分割后的图像进行滤波降噪处理。实验结果表明,阈值修正后的二维Otsu改进算法对小目标图像分割效果明显,而且新型滤波器对滤除散布在目标与背景中的噪声非常有效。将阈值修正法和新型滤波器结合使用,不仅快速,而且准确,取得了良好的分割效果。     

基于去调频技术的斜视聚束SAR成像方法

针对具有方位谱混叠现象的斜视聚束式合成孔径雷达(SAR)成像问题,本文研究了一种基于去调频技术的成像方法.文中首先分析了斜视聚束SAR中的方位谱混叠现象,从理论上找出了斜视角影响方位粗聚焦的根本原因.基于分析结果,引入了回波信号非线性相位校正预处理法,彻底消除了斜视角的影响,从而得到正确的粗聚焦结果.然后基于斜视成像模型,推导了预处理回波信号的方位粗聚焦和精聚焦处理过程.在精聚焦的方位滤波处理中,针对非线性相位校正预处理导致的场景聚焦深度限制问题,提出了一种改进的非线性调频变标(NLCS)算法,实现了回波信号的高精度方位压缩.仿真实验结果证明了文中理论分析的正确性和所提方法的有效性.     

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.     

天车称重系统解析

以河北钢铁集团唐山钢铁有限责任公司一钢轧厂240t天车称重系统为例,介绍天车称重系统的硬件结构、工作原理、传输方式、仪表功能、称重传感器原理、钢丝绳补偿、信号传输、称重仪表等,结合在生产中的实际运行状况,总结出在生产维护中的常见问题,并提出具体的解决方法。     

平行双关节坐标测量机的精度设计

平行双关节坐标测量机是一种新型非正交坐标测量设备,具有结构简单,精度高,便携等优点。文章首先介绍了平行双关节坐标测量机的测量原理,然后分析了影响仪器测量精度的误差源及误差源的可能修正精度,计算了仪器测量总误差。根据仪器设计精度进行了误差分配,并对误差分配进行了仿真计算,总结了仪器误差分布规律。本文的研究为指导测量机的结构设计和标定提供了依据,为实现平行双关节坐标测量机的测量精度指标提供了扎实的理论基础。     

FY-3气象卫星MERSI数据快速预处理的IDL实现

作为新型卫星数据源,FY\|3/MERSI(风云三号中分辨率光谱成像仪)影像的快速预处理方法与模块目前较少见。采用基于三角网的几何校正算法,根据研究区Shapefile文件和FY\|3/MERSI自带定位数据提取处理区域,实现对应区域全部20个通道几何校正,并利用后向映射重采样输出各通道图像纠正后像元点DN值,随后进行辐射定标、太阳高度角订正等预处理。该系列过程用IDL编写相关程序实现了用户界面化交互操作。运行结果表明:设计的算法流程占用内存小,计算速度快。对于一个面积约45万km²的区域,所有通道处理耗时仅450 s,且校正后影像质量和精度均较好。相较于相同区域借助ENVI软件下地理位置查找表法(GLT)仅实现单通道几何校正耗时超30 min,本文开发的模块大大提高了FY\|3/MERSI数据处理效率,为其在城市热环境、积雪等领域的应用做好准备工作。     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.