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991.
992.
A multi-spectral non-local (MSN) method is developed for advanced retrieval of boundary layer cloud properties from remote sensing data, as an alternative to the independent pixel approximation (IPA) method. The non-local method uses data at both the target pixel and neighboring pixels to retrieve cloud properties such as pixel-averaged cloud optical thickness and effective droplet radius. Radiance data to be observed from space were simulated by a three-dimensional (3D) radiation model and a stochastic boundary layer cloud model with two-dimensional (horizontal and vertical) variability in cloud liquid water and effective radius. An adiabatic assumption is used for each cloud column to model the geometrical thickness and vertical profiles of cloud liquid water content and effective droplet radius, neglecting drizzle and cloud brokenness for simplicity. The dependence of radiative smoothing and roughening on horizontal scale, optical thickness and single scattering albedo are investigated. Then, retrieval methods using 250-m horizontal resolution data onboard new generation satellites are discussed. The regression model for the MSN method was trained based on datasets from numerical simulations. The training was performed with respect to various domain averages of optical thickness and effective radius, because smoothing and roughening effects are strongly dependent on the two variables. Retrieval accuracy is discussed here with datasets independent of those used in the training, towards assessing the generality of the technique. It is demonstrated that retrieval accuracy of cloud optical thickness, which is often retrieved from single-spectral visible-wavelength data, is improved the most using neighboring pixel data and secondly using multi-spectral data, and ideally with both. When the IPA retrieval method is applied to optical thickness and effective radius, the root-mean-square relative errors can be 15-90%, depending on solar and view directions. In contrast, the MSN method has errors of 4-10%, which is smaller than IPA by a factor of 2-10. It is also suggested that the accuracy of the MSN method is insensitive to some assumptions in the inhomogeneous cloud input data used to train the regression model. 相似文献
993.
994.
995.
This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-flight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. 相似文献
996.
Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data 总被引:1,自引:0,他引:1
Raymond F Kokaly Don G DespainRoger N Clark K.Eric Livo 《Remote sensing of environment》2003,84(3):437-456
Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62). 相似文献
997.
基于WWW的多层螺旋调强放疗装置远程故障诊断系统 总被引:2,自引:0,他引:2
介绍了基于WWW的多层螺旋调强放疗装置远程故障诊断系统的体系结构、基本工作原理;对系统所涉及的关键技术,如Activex控件技术、Web数据库技术等进行了探讨,并举例说明了这两种技术在远程故障诊断系统中的综合应用方法。 相似文献
998.
999.
手臂机器人远程实验系统的设计和实现 总被引:3,自引:0,他引:3
本文介绍了一个基于Intemet的手臂机器人远程实验系统,提出了一个切实可行的B/S结构模型,并且阐述了该系统后台控制端,服务器端以及客户端的设计和实现。 相似文献
1000.
Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets 总被引:5,自引:0,他引:5
This research develops a methodology for the intelligent remote monitoring and diagnosis of manufacturing processes. A back propagation neural network monitors a manufacturing process and identifies faulty quality categories of the products being produced. For diagnosis of the process, rough set is used to extract the causal relationship between manufacturing parameters and product quality measures. Therefore, an integration of neural networks and a rough set approach not only provides information about what is expected to happen, but also reveals why this has occurred and how to recover from the abnormal condition with specific guidelines on process parameter settings. The methodology is successfully implemented in an Ethernet network environment with sensors and PLC connected to the manufacturing processes and control computers. In an application to a manufacturing system that makes conveyor belts, the back propagation neural network accurately classified quality faults, such as wrinkles and uneven thickness. The rough set also determined the causal relationships between manufacturing parameters, e.g., process temperature, and output quality measures. In addition, rough set provided operating guidelines on specific settings of process parameters to the operators to correct the detected quality problems. The successful implementation of the developed methodology also lays a solid foundation for the development of Internet-based e-manufacturing. 相似文献