多元时间序列PCA分割及在天然气脱水装置工况识别中的应用

目的建立不同的工作状态下的设备状态预测评估模型。方法利用主成分分析法将多元时间序列数据进行切割,将分割后的数据段按照基于密度的方法,依照定义的距离,对多元时间序列进行聚类合并,从而得到不同工况下的时间序列。结果按照脱水装置工况状态,将重沸器特征对应的多元时间序列划分为不同的数据段。结论有效实现了脱水装置重沸器的参数数据分割,并识别出不同工况。     

基于surfer8.0的高密度电阻率法在水利水电工程灾害性评价中的应用

surfer8.0是一款画三维图的软件,结合高密度电阻率法在水利水电工程灾害性评价中的应用实例,验证了高密度电阻率法在水利水电工程中具有的可行性、有效性和实用性.     

浅谈遥感解译技术在生态环境状况评价中的应用

简述了遥感解译这一新兴学科的技术特点,以及它在生态环境状况监测和评价中所起的重要作用。详细介绍了利用遥感解译手段对影像进行处理的技术流程和方法,提出了用来判定生态环境状况的各类生态指数的计算方法。     

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

An image-recognition-based diagnosis system of pipe defect types was established. 1043 practical pipe images were gathered by CCTV robot in a southern Chinese city. The overall accuracy of the system is 84% and the highest accuracy is 99.3%. The accuracy shows positive correlation to the number of training samples. Closed circuit television (CCTV) systems are widely used to inspect sewer pipe conditions. During the diagnosis process, the manual diagnosis of defects is time consuming, labor intensive and error prone. To assist inspectors in diagnosing sewer pipe defects on CCTV inspection images, this paper presents an image recognition algorithm that applies features extraction and machine learning approaches. An algorithm of image recognition techniques, including Hu invariant moment, texture features, lateral Fourier transform and Daubechies (DBn) wavelet transform, was used to describe the features of defects, and support vector machines were used to classify sewer pipe defects. According to the inspection results, seven defects were defined; the diagnostic system was applied to a sewer pipe system in a southern city of China, and 28,760 m of sewer pipes were inspected. The results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%. The overall accuracy reached 84.1%. The diagnosing accuracy depended on the number of the training samples, and four fitting curves were applied to fit the data. According to this paper, the logarithmic fitting curve presents the highest coefficient of determination of 0.882, and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.     

Measuring Plant Cover in Sagebrush Steppe Rangelands: A Comparison of Methods

Methods that are more cost-effective and objective are needed to detect important vegetation change within acceptable error rates. The objective of this research was to compare visual estimation to three new methods for determining vegetation cover in the sagebrush steppe. Fourteen management units at the US Sheep Experiment Station were identified for study. In each unit, 20 data collection points were selected for measuring plant cover using visual estimation, laser-point frame (LPF), 2 m above-ground-level (AGL) digital imagery, and 100-m AGL digital imagery. In 11 of 14 management units, determinations of vegetation cover differed (P < 0.05). However, when combined, overall determinations of vegetation cover did not differ. Standard deviation, corrected sums of squares, coefficient of variation, and standard error for the 100 m AGL method were half as large as for the LPF and less than the 2-m AGL and visual estimate. For the purpose of measuring plant cover, all three new methods are as good as or better than visual estimation for speed, standard deviation, and cost. The acquisition of a permanent image of a location is an important advantage of the 2 and 100 m AGL methods because vegetation can be reanalyzed using improved software or to answer different questions, and changes in vegetation over time can be more accurately determined. The reduction in cost per sample, the increased speed of sampling, and the smaller standard deviation associated with the 100-m AGL digital imagery are compelling arguments for adopting this vegetation sampling method.     

利用动态分段技术进行河流一维水质扩散模拟

作为GIS新技术,动态分段可以有效地实现河流一维水质扩散的模拟和空间显示分析。该文给出了动态分段数据库设计的基本思路、河流一维水质模型的功能设计及整个模拟系统的结构流程。      

Audiences for Science Communication in the United States

ABSTRACT

The US National Science Foundation collects biennial data on views about science and technology (S&T). Analyses have typically focused on the degree to which demographics, knowledge, and attitudes predict S&T support or views about specific technologies or issues. Published efforts do not appear to have focused on identifying latent classes within the data. Initial analyses using latent profile analysis suggests that Americans can be categorized into six substantive audience groups when it comes to views about S&T. These groups vary by demographics, including scientific background and ideology, as well as in views about S&T. Post hoc analyses explore how “class-focused” analyses may provide additional insight beyond what can be obtained from “variable-focused” analyses.     

基于彩色图像的林火图像火焰分割研究

结合实际林火图像的特点,提出了一种基于彩色图像分割策略的林火图像的火焰分割算法。该算法首先将图像转化为HSI彩色模型,然后根据各个分量的特点进行分割,并且进行了仿真实验,实验结果表明此算法能够很好地将火焰从复杂的背景中分割出来,克服了基于灰度图像分割不能准确分割烟雾和火焰的弊端,为今后对林火图像进一步分析提供了良好的基础。     

Ground-Cover Measurements: Assessing Correlation Among Aerial and Ground-Based Methods

Wyoming’s Green Mountain Common Allotment is public land providing livestock forage, wildlife habitat, and unfenced solitude, amid other ecological services. It is also the center of ongoing debate over USDI Bureau of Land Management’s (BLM) adjudication of land uses. Monitoring resource use is a BLM responsibility, but conventional monitoring is inadequate for the vast areas encompassed in this and other public-land units. New monitoring methods are needed that will reduce monitoring costs. An understanding of data-set relationships among old and new methods is also needed. This study compared two conventional methods with two remote sensing methods using images captured from two meters and 100 meters above ground level from a camera stand (a ground, image-based method) and a light airplane (an aerial, image-based method). Image analysis used SamplePoint or VegMeasure software. Aerial methods allowed for increased sampling intensity at low cost relative to the time and travel required by ground methods. Costs to acquire the aerial imagery and measure ground cover on 162 aerial samples representing 9000 ha were less than $3000. The four highest correlations among data sets for bare ground—the ground-cover characteristic yielding the highest correlations (r)—ranged from 0.76 to 0.85 and included ground with ground, ground with aerial, and aerial with aerial data-set associations. We conclude that our aerial surveys are a cost-effective monitoring method, that ground with aerial data-set correlations can be equal to, or greater than those among ground-based data sets, and that bare ground should continue to be investigated and tested for use as a key indicator of rangeland health.     

Developments in remote sensing, dynamic modelling and GIS applications for integrated coastal zone management

The International Institute for Aerospace Survey and Earth Sciences (ITC) has a research programme that should result in an integrated environmental coastal zone management system through three subprojects. The programme aims to develop methodologies and tools for assessing coastal zone changes, and for the evaluation of scenarios for coastal zone management, based on a spatio-temporal Geographical Information System (GIS) working platform which integrates remote sensing data, physical-morphodynamic and eco-hydrologic modelling, and a decision support system. The first subproject develops methodologies for the generation of optimum Remote Sensing (RS) data sets, leading to better interpretation and complementary use of conventional and new remote sensing imagery. It also integrates RS, GIS, and modelling through hypothesis generation, parameter estimation, evaluation and validation. The second subproject facilitates qualitative and quantitative analysis and prediction of the physical aspects of coastal landscape development under the influence of natural processes and human impacts. This subproject is based on the application of remote sensing and dynamic modelling. The third subproject leads to a spatio-temporal working platform which supports data integration of RS and in-situ measurements, and qualitative and quantitative analysis for the prediction of coastal landscape development. Both support decision making in Integrated Coastal Zone Management.