二分光反射模型在中医舌苔润燥分析中的应用

二分光反射模型能客观地反映物体的光反射机理 ,将其应用于计算机视觉领域 ,可以实现反射光斑的识别 .本文深入研究了舌苔表面的光反射属性和其图像特征 ,在我们已提出的舌苔润燥分析算法[2～ 4] 的基础上进行改进 ,提出了一种新的基于二分光反射模型的舌苔润燥分析算法 ,大大提高了识别正确率 .

光反射模型与照明设计计算机仿真

本文着重讨论照明设计仿真技术中光反射模型及全域照明算法。简要讨论物体表面亮度计算的两种方法。为使彩色ＣＲＴ能正确地模拟照明设计，要进行两项工作。一是在硬件方面，要进行彩色显示器的白场平衡和ｒ－校正，得到正确的颜色三刺激值输出；二是在软件方面，要开发照明设计ＣＧ计算方法。

基于二分光反射模型与随机森林的中医舌苔润燥识别研究

在以往的中医舌苔润燥识别研究中,只利用单个特征或指标,通过与经验阈值进行比较来进行舌苔润燥的识别。提取的特征不能充分地反映水分亮斑区的特性,同时利用经验阈值作为分类依据,易受人为主观因素的影响。本课题组提出一种基于二分光反射模型与随机森林的舌苔润燥识别方法。在已有研究的基础上,基于二分光反射模型分析亮斑区与本色区的光学特性差异,提取舌图像的水分亮斑区的RGB协方差矩阵特征值、亮度和等特征,并对这些特征进行统计分析;基于随机森林算法建立舌苔润燥识别模型。实验结果表明,该方法分类准确率可达95.9%,与已有的舌苔润燥识别方法相比有所提升。该研究为舌苔润燥识别提供了一种新的思路和方法,对舌诊客观化具有一定的实用价值。

利用光照反射模型分析中高轨人造卫星测光特性

利用光照反射模型分析中高轨人造卫星测光特性,以不同形状特性、姿态稳定方式和技术状态的中高轨人造卫星为研究样本,进行可见光波段的时间序列测光观测,并给出了卫星视星等斜距改正和相位角计算的方法。首先,利用球状卫星测光特性实验数据验证了其镜面反射和漫反射光变特性理论模型计算结果,并进一步探讨了复杂形状卫星的光变特征。其次,分别对类柱状受控卫星和失控卫星的理论模型计算结果和实验观测数据进行对比分析。实验结果表明,卫星光变曲线与卫星物理特性,如生存状态、形状特性、姿态稳定方式等相关,可为分析卫星的技术状态及其历史演化过程提供判定依据。

基于SFS算法的网点立体形态的获取与解析

目的在印刷传递过程中对网点微观立体形态进行准确提取并解析。方法在显微测试系统准确采集的网点二维数据的基础上,利用明暗恢复形貌(SFS)原理,构建出不同形状的印刷网点微观三维结构模型。结果直观并精确地还原了不同形状网点在承印物表面附着的立体形态,网点微观立体特性的不同会对印刷质量产生相应的影响。结论 SFS算法推导过程严谨,构建出的立体模型精度较高,能真实、细致地反映出印刷过程中网点传递的具体形态,是网点立体形态还原的有效方法。精确获取并解析印刷网点微观立体形态,可以直观有效地检测网点传递效果,并能从源头上控制印刷质量,是图像复制质量评价的新方法。

基于色彩均匀敏感度的磨削表面粗糙度测量

对于纹理较弱、粗糙度较低的磨削加工表面,采用传统的粗糙度测量方法存在提取表面纹理特征困难,且测量精度受表面纹理方向的影响,对此提出了一种对纹理方向不敏感的彩色光测量方法。首先,基于Beckmann-Spizzichino光反射模型,分析了粗糙面的光反射机理;然后,将磨削加工表面作为反射面,提出了一种基于彩色光反射图像的磨削表面粗糙度测量方案,并搭建了测量平台;最后,结合磨削表面存在的纹理方向特性,设计了一种色彩均匀敏感度测量指标Cu sd,建立了与磨削表面粗糙度的强相关性,实现了对磨削表面的粗糙度测量。实验结果表明:指标Cu sd性能明显优于其他对比指标;与粗糙度的相关性达到0.978,具有较强的正相关性,且该指标对纹理方向不敏感;对粗糙度Ra范围在0.017~0.646μm的磨削表面,指标Cu sd预测均方差为0.00084,平均测量误差为0.02μm,可满足磨削表面测量要求。

Construction of universal quantitative models for the determination of cefoperazone sodium/sulbactam sodium for injection from different manufacturers using near-infrared reflectance spectroscopy

To develop near-infrared （NIR） reflectance spectroscopic methods for the quantitative analysis of cefoperazone sodium/ sulbactam sodium from different manufacturers for injection powder medicaments. Various powders of cefoperazone sodium/ sulbactam sodium were directly analyzed by non-destructive NIR reflectance spectroscopy using the spectrometer EQUINOX55. Two quantitative methods via integrating sphere （IS） and fiberoptic probe （FOP） models were explored from 6 batches of commercial samples and 42 batches of laboratory samples at a content ranging from 30% to 70% for cefoperazone and 60% to 20% for sulbactam. The root mean square errors of cross validation （RMSECV） and the root mean square errors of prediction （RMSEP） of IS were 1.79% and 2.85%, respectively, for cefoperazone sodium, and were 1.86% and 3.08%, respectively, for sulbactam sodium; and those of FOP were 2.93% and 2.92%, respectively, for cefoperazone sodium, and were 2.23% and 3.01%, respectively, for sulbactam sodium. Based on the ICH guidelines and Ref. 12, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, precision, robustness and model transferability. The non-destructive quantitative NIR methods used in this study are applicable for rapid analysis of injectable powdered drugs from different manufacturers.

计算机作为真正的设计工具的新突破即将来临!

计算机硬件在处理能力、记忆容量、通带宽度、数据存贮等方面均已迅猛发展,价钱又迅速降低,不久的将来,所有的事务所均可安上自己的计算机系统。然而,建筑设计方面的软件开发远远地未能同步,适用程度很差。比使用方便更令人关切的是提供给建筑师的软件系统的局限性,这些软件系统根本算不上是什么设计系统。现在的系统在施工图过程中能有作用,但在进行初步设计推敲方案上都不够灵便,无法使用。

Remote Sensing Retrieval of Surface Suspended Sediment Concentration in the Yellow River Estuary

Accurate assessment of surface suspended sediment concentration （SSSC） in estuary is essential to address several important issues： erosion, water pollution, human health risks, etc. In this study, an empirical cubic retrieval model was developed for the retrieval of SSSC from Yellow River Estuary. Based on sediments and seawater collected from the Yellow River and southeastern Laizhou Bay, SSSC conditions were reproduced in the laboratory at increasing concentrations within a range common to field observations. Continu- ous spectrum measurements of the various SSSCs ranging from 1 to 5700 mg/1 were carried out using an AvaField-3 spectrometer. The results indicated the good correlation between water SSSC and spectral reflectance （Rrs） was obtained in the spectral range of 726-900 nm. At SSSC greater than 2700 mg/L, the 740-900 nm spectral range was less susceptible to the effects of spectral reflectance saturation and more suitable for retrieval of high sediment concentrations. The best correlations were obtained for the reflectance ratio of 820 nm to 490 nm. Informed by the correlation between Rrs and SSSC, a retrieval model was developed （R2 = 0.992）. The novel cubic model, which used the ratio of a near-infrared （NIR） band （740-900 nm） to a visible band （400-600 nm） as factors, provided robust quantifica- tion of high SSSC water samples. Two high SSSC centers, with an order of 103 mg/1, were found in the inversion results around the abandoned Diaokou River mouth, the present Yellow River mouth to the abandoned Qingshuigou River mouth. There was little sedi- ment exchange between the two high SSSC centers due to the directions of the residual currents and vertical mixing.

Evaluation of Spectral Scale Effects in Estimation of Vegetation Leaf Area Index Using Spectral Indices Methods

Spectral index methodology has been widely used in Leaf Area Index(LAI) retrieval at different spatial scales. There are differences in the spectral response of different remote sensors and thus spectral scale effect generated during the use of spectral indices to retrieve LAI. In this study, PROSPECT, leaf optical properties model and Scattering by Arbitrarily Inclined Layers(SAIL) model, were used to simulate canopy spectral reflectance with a bandwidth of 5 nm and a Gaussian spectral response function was employed to simulate the spectral data at six bandwidths ranging from 10 to 35 nm. Additionally, for bandwidths from 5 to 35 nm, the correlation between the spectral index and LAI, and the sensitivities of the spectral index to changes in LAI and bandwidth were analyzed. Finally, the reflectance data at six bandwidths ranging from 40 to 65 nm were used to verify the spectral scale effect generated during the use of the spectral index to retrieve LAI. Results indicate that Vegetation Index of the Universal Pattern Decomposition(VIUPD) had the highest accuracy during LAI retrieval. Followed by Normalized Difference Vegetation Index(NDVI), Modified Simple Ratio Indices(MSRI) and Triangle Vegetation Index(TVI), although the coefficient of determination R^2 was higher than 0.96, the retrieved LAI values were less than the actual value and thus lacked validity. Other spectral indices were significantly affected by the spectral scale effect with poor retrieval results. In this study, VIUPD, which exhibited a relatively good correlation and sensitivity to LAI, was less affected by the spectral scale effect and had a relatively good retrieval capability. This conclusion supports a purported feature independent of the sensor of this model and also confirms the great potential of VIUPD for retrieval of physicochemical parameters of vegetation using multi-source remote sensing data.

Analysis of reflection coefficient for coated optical waveguide devices

From an ordinary condition,using a full three-dimensional model theory and an infinite perturbation expansion method,an exact solution of the reflection coefficient for the coated narrow stripe-geometry optical waveguide devices has been derived.All six components and the vector property of the electromagnetic field have been considered.The results are suitable for the symmetric and asymmetric waveguides.

Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.

Estimating biophysical parameters of rice with remote sensing data using support vector machines

Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application.Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters,comprising leaf area index (LAI;m-2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD;mg chlorophyll m 2 soil),using stepwise multiple regression (SMR) models and support vector machines (SVMs).Four transformations of the rice canopy data were made,comprising reflectances (R),first-order derivative reflectances (D1),second-order derivative reflectances (D2),and logarithm transformation of reflectances (LOG).The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI,with a root mean square error (RMSE) of 1.0496 LAI units.The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD,with an RMSE of 523.0741 mg m-2.The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters,but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data.

Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data:A Case Study of Selected South African Soils

Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.

A unified canopy bidirectional reflectance (BRDF) model for row ceops

Row sowing is a basic crop sowing method in China,and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological parameters.Because of the macroscopically geometric difference,the row crop is usually regarded as a transition between continuous and discrete vegetation in previous studies.Were row treated as the unit for calculating the four components in the Geometric Optical model (GO model),the formula would be too complex and difficult to retrieve.This study focuses on the microscopic structure of row crops.Regarding the row crop as a result of leaves clumped at canopy scale,we apply clumping index to link continuous vegetation and row crops.Meanwhile,the formula of clumping index is deduced theoretically.Then taking leaf as the basic unit,we calculate the four components of the GO model and develop a BRDF model for continuous vegetation,which is gradually extended to the unified BRDF model for row crops.It is of great importance to introduce clumping index into BRDF model.In order to evaluate the performance of the unified BRDF model,the canopy BRDF data collected in field experiment,"Watershed Allied Telemetry Experiment Research (WATER)",from May 30th to July 1st,2008 are used as the validation dataset for the simulated values.The results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row crops.In addition,the model is simple and easy to retrieve.In general,there is no irreconcilable conflict between continuous and discrete vegetation,so understanding their common and individual characteristics is advantageous for simulating canopy BRDF.It is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.

Miniature Fabry-Perot interferometric sensor for temperature measurement based on photonic crystal fiber

A novel miniature Fabry-Perot interferometric(FPI) temperature sensor is proposed and demonstrated experimentally. The modal interferometer is fabricated by just splicing a section of photonic crystal fiber(PCF) with a single-mode fiber(SMF). The air holes of the PCF are fully collapsed by the discharge arc during the splicing procedure to enhance the reflection coefficient of the splicing point. The transmission spectra with different temperatures are measured, and the experimental results show that the linear response of 11.12 pm/°C in the range of 30–80 °C is obtained. This sensor has potential applications in temperature measurement field.