On dual-phase-lag magneto-thermo-viscoelasticity theory with memory-dependent derivative

A new mathematical model of generalized magneto-thermo-viscoelasticity theories with memory-dependent derivatives (MDD) of dual-phase-lag heat conduction law is developed. The equations for one-dimensional problems including heat sources are cast into matrix form using the state space and Laplace transform techniques. The resulting formulation is applied to a problem for the whole space with a plane distribution of heat sources. It is also applied to a perfect conducting semi-space problem with a traction-free surface and plane distribution of heat sources located inside the medium. The inversion of the Laplace transforms is carried out using a numerical approach. Numerical results for the temperature, displacement, stress and heat flux distributions as well as the induced magnetic and electric fields are given and illustrated graphically. A comparison is made with the results obtained in the coupled theory. The impacts of the MDD heat transfer parameter and Alfven velocity on a viscoelastic material, for example, poly (methyl methacrylate) (Perspex) are discussed.

     

热通量传感器的应用及检定

通过对热通量传感器在新材料的研制及热分析仪参量测量领域的研究,分别给出了其在表面安装和热分析仪中的应用方法,并针对应用中出现的问题提出解决方案,同时通过比较法求解检定常数。给出了热通量传感器的检定方法,提出了热通量微观效应的概念,对埋入绝缘材料和表面镀金属层热偶的微观效应进行了分析。结果证明,周围材料的不同是影响检定误差的重要因素。     

Estimating the depth of buried hot features from thermal IR remote sensing data: a conceptual approach

Estimating the depth of buried thermal features from remotely sensed data is a difficult problem. The theoretical basis discussed in this letter shows that if the time of initiation of the heat source is known, then utilizing two sets of thermal Infrared ( IR) data of different time instances for the same anomaly area, the depth of the buried heat source can be computed. However, if the thermal IR data are collected at time intervals close to the time of initiation of heat source, the knowledge of initial conditions of heat source is obviated. In this latter case, a double ratioing technique, utilizing three sets of thermal IR remote sensing data, acquired at different times for the same area, can be used for estimating the depth of buried heat source. Respective sample sets of characteristic curves designed for depth estimation are presented along with results of sensitivity analysis tests, performed on simulated data.     

Variation of atmospheric optical depth for remote sensing radiance calculations

An analysis of the radiative transfer processes in the atmosphere suggests that the ratio between the diffuse radiance flux and the directly transmitted flux may be a useful parameter for investigating the variation of atmospheric optical depth. The radiative parameter is derived for an observer at the top of the atmosphere in the position of a satellite. One of the important variables required in the computations of the radiative parameter is the aerosol phase function which can be satisfactorily fitted by the two-term Henyey-Greenstein function. When the surface reflectance equals zero, representing the atmospheric radiance alone without any influence from an oceanic surface, the study shows that the variation of the optical depth is associated with the changing of aerosol size distribution. The optical depth increases with the presence of large aerosol particles. The results indicate that inhomogeneities in aerosol optical properties, such as the variation of the scattering phase function due to the changes in aerosol size distribution, can affect the magnitude of the optical depth.     

Nonstructured light-based sensing for 3D reconstruction

Structured light-based sensing (SLS) requires the illumination to be coded either spatially or temporally in the illuminated pattern. However, while the former demands the use of uniquely coded spatial windows whose size grows with the reconstruction resolution and thereby demanding increasing smoothness on the imaged scene, the latter demands the use of multiple image captures. This article presents how the illumination of a very simple pattern plus a single image capture can also achieve 3D reconstruction. The illumination and imaging setting has the configuration of a typical SLS system, comprising a projector and a camera. The difference is, the illumination is not much more than a checkerboard-like pattern - a non-structured pattern in the language of SLS - that does not provide direct correspondence between the camera’s image plane and the projector’s display panel. The system works from the image progressively, first constructing the orientation map of the target object from the observed grid-lines, then inferring the depth map by the use of a few tricks related to interpolation. The system trades off little accuracy of the traditional SLSs with simplicity of its operation. Compared to temporally coded SLSs, the system has the essence that it requires only one image capture to operate; compared with spatially coded SLSs, it requires no use of spatial windows, and in turn a less degree of smoothness on the object surface; compared with methods like shape from shading and photometric stereo, owing to the use of artificial illumination it is less affected by the surface reflectance property of the target surface and the ambient lighting condition.     

Construction and accuracy analysis of images of the daily-mean mixed-layer depth

Abstract

Using satellite data only, a unique model of the upper-ocean mixed-layer has been developed recently by Yan for calculating the thermal inertia (or the heat flux change per unit temperature change per day) of the mixed-layer from which daily mean mixed-layer depth is estimated. Thus digital maps of the oceanic daily mixed-layer depth in the vicinity of the Sargasso Sea for 14 May-30 August, 1982 have been generated from visible, near-infrared, thermal-infrared and microwave observations made by radiometers on board the NOAA-7, -8, GOES and Nimbus-7 satellites. The errors incurred in producing such maps are of two general type: measurement from satellites and model simplification. The sensitivity of the modelled mixed-layer depth to simplifications in the model (modelling errors) and to errors in data used for forcing the model (data errors) is considered here. To emph isize the oceanographic relevance of these errors, we express the errors in mixed-layer depth as a function of model errors and data errors, and compare these errors with the daily mixed-layer depth changes of in situ data. We also discuss some of the error sources in this paper. Thus the applications and practical limitations of the model are evaluated. The results of this study show that errors in the surface temperature field cause the largest errors in the predicted mixed-layer depth, because the surface temperature errors affect both the heat flux estimation and the thermal inertia estimation. The errors in wind speed field are less severe although these errors may become more significant at wind speed higher than 8.5 ms^?1, below which most of our field comparisons were made. The smallest uncertainty is found to occur with remotely estimated changes in sea-surface albedo as this error in a very small way affects the heat flux. These results imply that the errors due to direct atmospheric radiative effects or those which determine radiative fluxes and the determination of sea surface temperature should be reduced to improve prediction of mixed-layer thermal inertia and mixed-layer depth in summer using satellite data.     

点云下地平面检测的RGB-D相机外参自动标定

目的 RGB-D相机的外参数可以被用来将相机坐标系下的点云转换到世界坐标系的点云,可以应用在3维场景重建、3维测量、机器人、目标检测等领域。 一般的标定方法利用标定物（比如棋盘）对RGB-D彩色相机的外参标定,但并未利用深度信息,故很难简化标定过程,因此,若充分利用深度信息,则极大地简化外参标定的流程。基于彩色图的标定方法,其标定的对象是深度传感器,然而,RGB-D相机大部分则应用基于深度传感器,而基于深度信息的标定方法则可以直接标定深度传感器的姿势。方法 首先将深度图转化为相机坐标系下的3维点云,利用MELSAC方法自动检测3维点云中的平面,根据地平面与世界坐标系的约束关系,遍历并筛选平面,直至得到地平面,利用地平面与相机坐标系的空间关系,最终计算出相机的外参数,即相机坐标系内的点与世界坐标系内的点的转换矩阵。结果 实验以棋盘的外参标定方法为基准,处理从PrimeSense相机所采集的RGB-D视频流,结果表明,外参标定平均侧倾角误差为-1.14°,平均俯仰角误差为4.57°,平均相机高度误差为3.96 cm。结论 该方法通过自动检测地平面,准确估计出相机的外参数,具有很强的自动化,此外,算法具有较高地并行性,进行并行优化后,具有实时性,可应用于自动估计机器人姿势。     

Simultaneous space diffusivity and source term reconstruction in 2D IHCP

A numerical marching scheme is presented for the simultaneous recovery of the diffusivity coefficient, spatial source term, temperature, and heat flux distributions in the two-dimensional inverse heat conduction problem when noisy data at the active boundary and the initial measured temperature distribution are given. No information about the quality and/or quantity of the noise is assumed. A proof of stability and convergence of the algorithm are provided together with several numerical examples of interest.     

图像亮度线索下的单目深度信息提取

目的 深度信息的获取是3维重建、虚拟现实等应用的关键技术,基于单目视觉的深度信息获取是非接触式3维测量技术中成本最低、也是技术难度最大的手段。传统的单目方法多基于线性透视、纹理梯度、运动视差、聚焦散焦等深度线索来对深度信息进行求取,计算量大,对相机精度要求高,应用场景受限,本文基于固定光强的点光源在场景中的移动所带来的物体表面亮度的变化,提出一种简单快捷的单目深度提取方法。方法 首先根据体表面反射模型,得到光源照射下的物体表面的辐亮度,然后结合光度立体学推导物体表面辐亮度与摄像机图像亮度之间的关系,在得到此关系式后,设计实验,依据点光源移动所带来的图像亮度的变化对深度信息进行求解。结果 该算法在简单场景和一些日常场景下均取得了较好的恢复效果,深度估计值与实际深度值之间的误差小于10%。结论 本文方法通过光源移动带来的图像亮度变化估计深度信息,避免了复杂的相机标定过程,计算复杂度小,是一种全新的场景深度信息获取方法。     

基于EFAST方法的SEBS模型参数全局敏感性分析

应用高效、稳健的EFAST方法,以黑河流域盈科绿洲站为例,从3个方面对SEBS模型的参数敏感性进行了分析:分别以感热通量(H)、潜热通量(λE)、蒸发比(fr)作为SEBS模型的输出结果,分析其对12个输入参数的敏感性;利用气象数据驱动模型,分析H、λE和fr对6个地表特征参数的敏感性;分析了参数取值范围对敏感性分析结果的影响。研究结果表明:H、λE与fr都对参考高度处的气温和风速、地表温度以及植被特征参数的敏感性较高。参数间相互作用对H、λE的间接影响很小,而对fr的影响较大。当气象输入参数确定时,6个地表参数中地表温度对模型输出的直接贡献最大,其主敏感度指数接近0.6。参数采样范围不同时,模型输入参数的敏感性表现不同。     

Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment

Understanding the spatial scaling behavior of evapotranspiration and its relation to controlling factors on the land surface is necessary to accurately estimate regional water cycling. We propose a method for ascertaining this scaling behavior via a combination of wavelet multi-resolution analysis and information theory metrics. Using a physically-based modeling framework, we are able to compute spatially distributed latent heat fluxes over the tall-grass prairie in North-central Kansas for August 8, 2005. Comparison with three eddy-covariance stations and a large aperture scintillometer demonstrates good agreement, and thus give confidence in the modeled fluxes. Results indicate that the spatial variability in radiometric temperature (a proxy for soil moisture) most closely controls the spatial variability in evapotranspiration. Small scale variability in the water flux can be ascribed to the small scale spatial variance in the fractional vegetation. In addition, correlation analysis indicates general scale invariance and that low spatial resolution data may be adequate for accurately determining water cycling in prairie ecosystems.     

Edge detection and surface reconstruction using refinedregularization

An edge detection and surface reconstruction algorithm in which the smoothness is controlled spatially over the image space is presented. The values of parameters in the model are adaptively determined by an iterative refinement process; hence, the image-dependent parameters such as the optimum value of the regularization parameter or the filter size are eliminated. The algorithm starts with an oversmoothed regularized solution and iteratively refines the surface around discontinuities by using the structure exhibited in the error signal. The spatial control of smoothness is shown to resolve the conflict between detection and localization criteria of edge detection by smoothing the noise in continuous regions while preserving discontinuities. The performance of the algorithm is quantitatively and qualitatively evaluated on real and synthetic images, and it is compared with those of Marr-Hildreth and Canny edge detectors     

Effects of slip on sheet-driven flow and heat transfer of a non-Newtonian fluid past a stretching sheet

The flow and heat transfer of an electrically conducting non-Newtonian fluid due to a stretching surface subject to partial slip is considered. The constitutive equation of the non-Newtonian fluid is modeled by that for a third grade fluid. The heat transfer analysis has been carried out for two heating processes, namely, (i) with prescribed surface temperature (PST-case) and (ii) prescribed surface heat flux (PHFcase) in presence of a uniform heat source or sink. Suitable similarity transformations are used to reduce the resulting highly nonlinear partial differential equations into ordinary differential equations. The issue of paucity of boundary conditions is addressed and an effective second order numerical scheme has been adopted to solve the obtained differential equations. The important finding in this communication is the combined effects of the partial slip, magnetic field, heat source (sink) parameter and the third grade fluid parameters on the velocity, skin friction coefficient and the temperature field. It is interesting to find that slip decreases the momentum boundary layer thickness and increases the thermal boundary layer thickness, whereas the third grade fluid parameter has an opposite effect on the thermal and velocity boundary layers.     

Sample‐Based Manifold Filtering for Interactive Global Illumination and Depth of Field

We present a fast reconstruction filtering method for images generated with Monte Carlo–based rendering techniques. Our approach specializes in reducing global illumination noise in the presence of depth‐of‐field effects at very low sampling rates and interactive frame rates. We employ edge‐aware filtering in the sample space to locally improve outgoing radiance of each sample. The improved samples are then distributed in the image plane using a fast, linear manifold‐based approach supporting very large circles of confusion. We evaluate our filter by applying it to several images containing noise caused by Monte Carlo–simulated global illumination, area light sources and depth of field. We show that our filter can efficiently denoise such images at interactive frame rates on current GPUs and with as few as 4–16 samples per pixel. Our method operates only on the colour and geometric sample information output of the initial rendering process. It does not make any assumptions on the underlying rendering technique and sampling strategy and can therefore be implemented completely as a post‐process filter.     

Land Surface Temperature Data Fusion of Landsat ETM and MODISby Combining TsHARP and STITFM Model

Land Surface Temperature (LST) is an important parameter that describes energy balance of substance and energy exchange between the surface and the atmosphere,and LST has widely used in the fields of urban heat island effect,soil moisture and surface radiative flux.Currently,no satellite sensor can deliver thermal infrared data at both high temporal resolution and spatial resolution,which strongly limits the wide application of thermal infrared data.Based on the MODIS land surface temperature product and Landsat ETM+image,a temporal and spatial fusion method is proposed by combining the TsHARP (Thermal sHARPening) model with the STITFM (Spatio\|Temporal Integrated Temperature Fusion Model) algorithm,defined as CTsSTITFM model in this study.The TsHARP method is used to downscale the 1 km MODIS land surface temperature image to LST data at spatial resolution of 250 m.Then the accuracy is verified by the retrieval LST from Landsat ETM+ image at the same time.Land surface temperature image at 30 m spatial scale is predicted by fusing Landsat ETM+ and downscaling MODIS data using STITFM model.The fusion LST image is validated by the estimated LST from Landsat ETM+ data for the same predicted.The results show that the proposed method has a better precision comparing to the STITFM algorithm.Under the default parameter setting,the predicted LST values using CTsSTITFM fusion method have a root mean square error (RMSE) less than 1.33 K.By adjusting the window size of CTsSTITFM fusion method,the fusion results in the selected areas show some regularity with the increasing of the window.In general,a reasonable window size set may slightly improve the effects of LST fusion.The CTsSTITFM fusion method can solve the problem of mixed pixels caused by coarse\|scale MODIS surface temperature images to some degree.     

A novel rapid point-cloud surface reconstruction algorithm for laser imaging radar

In order to obtain the fast three-dimensional surface reconstruction from given scattered point clouds, a novel improved point-cloud surface reconstruction algorithm for laser imaging radar is proposed so as to reconstruct the three-dimensional depth surface from the depth data and image data in this paper. Firstly, the three-dimensional space is partitioned into voxels with local distance points and finds outliers with point histogram features; then the Gaussian process (GP) regression is adopted to generate a plane similar to a Gaussian distribution; finally, the high-resolution gray data and three-dimensional interpolation points are fused by using Markov random fields to build a dense three-dimensional depth surface. Experimental results show that our proposed algorithm will greatly improve the robustness and reconstruction accuracy of three-dimensional surface reconstruction algorithm and can be used to assist unmanned driving in complex urban scenes.

     

基于最优投影平面的立体视觉空间圆位姿高精度测量方法

现有基于双目立体视觉的测量方法的测量精度依赖于标定精度,在受遮挡时测量精度不高.文中首先分析并证明在双目立体视觉系统外部参数误差存在时,投影曲线上点的立体匹配误差对点的重建精度的影响.然后基于此误差分析结论,设计立体视觉空间圆位姿测量方法,通过轮廓点筛选算法筛选投影曲线上的点,得到匹配误差较小的点并进行重建.利用重建点在深度方向上对非线性优化得到最优投影平面的投影,对空间圆进行拟合,得到空间圆的位置姿态.文中方法有效减小三维点重建误差对空间圆拟合精度的影响,提高圆形特征在受遮挡情况下的测量精度.最后通过实验验证方法的有效性.     

LYTRO相机光场图像深度估计算法及重建的研究

光场相机目前已广泛应用于消费领域和工业应用领域,利用光场相机对目标物进行深度重建成为了一项重要的研究课题。在实际研究过程中,Lytro相机空间信息与角度信息复用于同一传感器,导致图像分辨率较低,从而使得重建效果不甚理想。为解决这一问题,提出了一种亚像素精度的光场图像深度估计方法,在频率域对子孔径图像进行多标签下的亚像素偏移,以中心视角图像为参照,建立像素匹配代价行为;使用引导滤波抑制噪声的同时保持了图像边缘;对多标签下的匹配代价行为进行优化,得到精确的深度估计结果。对目标深度图进行表面渲染、纹理映射等重建处理,得到较为精细的重建结果。实验结果表明,该算法在对复杂度较高的物体进行重建时,解决了重建模糊等问题,有较好的表现。     

基于单类Kinect的人体三维重建与优化

使用单一运动的类Kinect深度相机重建和优化静态3D全身人体模型.首先针对类Kinect深度相机产生噪声原因提出一种降噪处理方法进行降噪.结合深度信息和RGB信息获取匹配块,使用高斯混合模型进行局部配准和逐层封闭曲线拟合方法进行全局配准,结合改进方向距离函数进行合并,最后使用泊松表面重建方法获取三维模型.实验结果表明,该方法能够重建出较高精度的三维人体模型.