基于小波包分解的结构自振特征提取及多路径误差分离

为了准确获取结构自振特性,通过分析多路径误差和结构振动的频率特征,采用小波包分解和频谱分析相结合的方法,在不同尺度下进行特定成分的提取,再作频率特性分析。实验结果表明,小波包能够有效地分离多路径误差,实现结构振动特征的提取,GPS测定的结构自振频率与理论值吻合较好,并且具有很好的稳定性。     

基于GNSS-RTK的在建超高层风载动态变形监测

首先以海星达H32全能型GNSS-RTK接收机为试验仪器,进行不同星系组合的RTK定位精度试验研究。然后以在建的天津高银117大厦为监测对象,采用GPS+GLONASS+BDS三星系组合对其进行现场强风作用下的RTK动态变形监测。利用巴特沃斯滤波对实测数据进行去噪处理,得到各测点的位移曲线。分析结果表明:GPS单星系定位的平面坐标精度相对较差,双星系组合定位的平面坐标精度与三星系组合定位的平面坐标精度基本相当,但GPS+GLONASS+BDS三星系组合定位的稳定性和可靠性最强;由于施工阶段大厦结构的刚度和整体性与竣工后存在差异,故在强风作用下其按一定施工步距滞后的低矮外框的振动位移幅度大于其高耸内筒结构的振动位移幅度;大厦外框和内筒均存在横风向振动位移,且横风向振动位移与顺风向振动位移的大小相近;监测环境对监测结果有着一定的影响,66层各点的监测值误差要大于95层各点的监测值误差。     

Update and spatial extension of strategic forest inventories using time series remote sensing and modeling

Up-to-date forest inventory information relating the characteristics of managed and natural forests is fundamental to sustainable forest management and required to inform conservation of biodiversity and assess climate change impacts and mitigation opportunities. Strategic forest inventories are difficult to compile over large areas and are often quickly outdated or spatially incomplete as a function of their long production cycle. As a consequence, automated approaches supported by remotely sensed data are increasingly sought to provide exhaustive spatial coverage for a set of core attributes in a timely fashion. The objective of this study was to demonstrate the integration of current remotely-sensed data products and pre-existing jurisdictional inventory data to map four forest attributes of interest (stand age, dominant species, site index, and stem density) for a 55 Mha study region in British Columbia, Canada. First, via image segmentation, spectrally homogenous objects were derived from Landsat surface-reflectance pixel composites. Second, a suite of Landsat-based predictors (e.g., spectral indices, disturbance history, and forest structure) and ancillary variables (e.g., geographic, topographic, and climatic) were derived for these units and used to develop predictive models of target attributes. For the often difficult classification of dominant species, two modelling approaches were compared: (a) a global Random Forests model calibrated with training samples collected over the entire study area, and (b) an ensemble of local models, each calibrated with spatially constrained local samples. Accuracy assessment based upon independent validation samples revealed that the ensemble of local models was more accurate and efficient for species classification, achieving an overall accuracy of 72% for the species which dominate 80% of the forested areas in the province. Results indicated that site index had the highest agreement between predicted and reference (R² = 0.74, %RMSE = 23.1%), followed by stand age (R² = 0.62, %RMSE = 35.6%), and stem density (R² = 0.33, %RMSE = 65.2%). Inventory attributes mapped at the image-derived unit level captured much finer details than traditional polygon-based inventory, yet can be readily reassembled into these larger units for strategic forest planning purposes. Based upon this work, we conclude that in a multi-source forest monitoring program, spatially localized and detailed characterizations enabled by time series of Landsat observations in conjunction with ancillary data can be used to support strategic inventory activities over large areas.     

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.     

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.     

基于自由振动响应的基础隔震建筑减震能力评估方法研究

目前国内外已修建完成了大量隔震建筑,但仅有少量经受了地震检验,绝大部分隔震结构减震能力能否达到设计目标尚存疑问.本文针对基础隔震建筑,提出了一种基于自由振动响应的减震能力评估方法.首先,对隔震建筑进行多级幅值初位移自由振动原位试验,获取结构的抗震能力曲线;其次,根据地震反应谱建立地震需求曲面,进而确定隔震结构性能点;最...     

ZHDN-SDR 150A型高频声波钻机设计

文章主要介绍了ZHDN-SDR 150A 型高频声波钻机设计及其验证测试,为该钻机应用于污染场地调查和治理提供理论依据和技术指导。论文阐述了钻机总体设计思路及主要性能参数指标,系统分析了高频声波动力头、钻机主体、配套钻具结构设计及其技术创新。该钻机在两种不同地层进行了验证测试,各项测试指标均达到设计要求。将验证测试结果与国内外同类钻机对比表明,ZHDN-SDR 150A型钻机主要性能参数与关键装置已处于国内先进和国外并跑水平。该型钻机在振动频率、地层适用性、钻具使用寿命等方面优势显著,但是存在过高温度运行条件下钻进效率降低、智能自动化水平不足的问题,需进一步研发耐高温强硬度新材料和改进远程无线操控系统。     

Heave compensation system of lifting pipeline for deep-sea mining based on combined action of vibration absorber and accumulator

ABSTRACT

A heave compensation system combined with a vibration absorber and accumulator is proposed, with regard to the requirement for poly-metallic nodule mining systems at an ocean depth of 6,000?m in China. The principle of the compensation system was analyzed, its main parameters were designed, and its mathematical model was established. The system performance was simulated and analyzed in MATLAB, and the frequency domain characteristics and compensation rate of the system with different volume accumulators, and with or without vibration absorbers, were compared. A simulation test of the compensation system was conducted. The proposed compensation system had a better compensation effect for a random wave interference under level-6 sea conditions, and the compensation rate being more than 72% with optimal system parameters.     

考虑弯曲刚度的悬索面内自由振动解析解

对索的准确模拟有时应考虑弯曲刚度的影响.在已有研究基础上,考虑弯曲刚度对悬索静力构形的影响,分别推导了两端固结和两端铰接边界条件下水平悬索面内自由振动的解析解,并修正了已有文献中的不足之处,该解在理论上较已有解析解更为准确,它可看作是已有文献在均匀索情况下的解析解.本文推导的解析解具有一定的理论意义.