查询词典 gradient

In Salsola passerina -Reaumuria soongorica community, the first ordination axes explains the salinization gradient, along the order of Caragana tibetica community,Salsola passerina-Oxytropis aciphylla community,and Reaumuria soongorica-Salsola passerine community,soil alkalization increases. The second ordination axis explains soil structure gradient, along the order of Caragana tibetica community, Reaumuria soongorica-Salsola passerina community and Salsola passerina community, soil texture becomes coarser. In the Stipa breviflora-Stipa grandis community, the first ordination axis indicates the soil water gradient, and the second ordination axes explains hydrothermal coupling gradient. In the Prunus mongolica-Ulmus glaucescens community, the first ordination axis explains the soil pH gradient, along the order of Ulmus glaucescens-Prunus mongolica community, Prunus mongolica-Potentilla fruticosa community, and Potentilla fruticosa-Prunus mongolica community, soil pH value reduces. The second ordination axis explains soil structure gradient, along the order Prunus mongolica-Ulmus glaucescens community, and Prunus mongolica-Potentilla fruticosa community, the contents of silt and clay increase, and soil texture suggests a fine trend.

在珍珠猪毛菜-红砂群落，第一排序轴反映了土壤盐碱化梯度，沿着藏锦鸡儿群落—珍珠猪毛菜、猫头刺群落—珍珠猪毛菜、红砂群落序列，土壤盐碱化程度不断增强；第二排序轴则反映了土壤结构梯度，沿着藏锦鸡儿群落—珍珠猪毛菜、红砂群落—珍珠猪毛菜、猫头刺群落序列，土壤质地逐渐粗化；在短花针茅-大针茅群落，第一排序轴反映了土壤水分梯度，第二排序轴反映了海拔梯度上的水热组合梯度；在蒙古扁桃-灰榆群落，第一排序轴反映了土壤pH梯度，沿着灰榆、蒙古扁桃群落—蒙古扁桃、金露梅群落—蒙古扁桃群落序列，土壤pH值逐渐下降；第二排序轴主要反映了土壤结构梯度，沿着蒙古扁桃群落—灰榆、蒙古扁桃群落—蒙古扁桃、金露梅群落序列，土壤中粉粒、粘粒含量逐渐增加，土壤质地呈细化趋势。

In Salsola passerina-Reaumuria soongoriea community, the first ordination axes explains the salinization gradient. along the order of Caragana tibetica community, Salsola passerina-Oxytropis aciphylla community, and Reaumuria soongorica-Salsola passerine community, soil alkalization increases. The second ordination axis explains soil structure gradient, along the order of Caragana tibetica community, Reaumuria soongorica-Salsola passerina community and Salsola passerina community, soil texture becomes coarser. In the Stipa breviflora-Stipa grandis community, the first ordination axis indicates the soil water gradient, and the second ordination axes explains hydrothermal coupling gradient. In the Prunus mongolica-Ulmus glaucescens community, the first ordination axis explains the soil pH gradient, along the order of Ulmus glaucescens-Pnuius mongolica community, Prunus mongolica-Potentilla fruticosa community, and Potentilla fnuicosa-Prunus mongolica community, soil pH value reduces. The second ordination axis explains soil structure gradient, along the order Prunus mongolica-Ulmus glaucescens community, and Prunus mongolica-Potentilla fruticosa community, the contents of silt and clay increase, and soil texture suggests a fine trend.

在珍珠猪毛菜－红砂群落，第一排序轴反映了土壤盐碱化梯度，沿着藏锦鸡儿群落－珍珠猪毛菜、猫头刺群落－珍珠猪毛菜、红砂群落序列，土壤盐碱化程度不断增强；第二排序轴则反映了土壤结构梯度，沿着藏锦鸡儿群落－珍珠猪毛菜、红砂群落－珍珠猪毛菜、猫头刺群落序列，土壤质地逐渐粗化；在短花针茅－大针茅群落，第一排序轴反映了土壤水分梯度，第二排序轴反映了海拔梯度上的水热组合梯度；在蒙古扁桃－灰榆群落，第一排序轴反映了土壤pH梯度，沿着灰榆、蒙古扁桃群落－蒙古扁桃、金露梅群落－蒙古扁桃群落序列，土壤pH值逐渐下降；第二排序轴主要反映了土壤结构梯度，沿着蒙古扁桃群落－灰榆、蒙古扁桃群落－蒙古扁桃、金露梅群落序列，土壤中粉粒、粘粒含量逐渐增加，土壤质地呈细化趋势。

Among used machine learning methods, the gradient descent method is widely used to train various classifiers, such as Back-propagation neural network and linear text classifier. However, the gradient descent method is easily trapped into a local minimum and slowly converges. Thus, this study presents a gradient forecasting search method based on prediction methods to enhance the performance of the gradient descent method in order to develop a more efficient and precise machine learning method for Web mining.However, a prediction method with few sample data items and precise forecasting ability is a key issue to the gradient forecasting search method. Applying statistic-based prediction methods to implement GFSM is unsuitable because they require a large number of data items to model a prediction model. In the contrast with statistic-based prediction methods, GM(1,1) grey prediction model does not need a large number of data items to build a prediction model, and it has low computational load. However, the original GM(1,1) grey prediction model uses a mathematical hypothesis and approximation to transform a continuous differential equation into a discrete difference equation in order to model a forecasting model.

其中梯度法是一个最常被使用来实现机器学习的方法之一，然而梯度法具有学习速度慢以及容易陷入局部最佳解的缺点，因此，本研究提出一个梯度预测搜寻法则(gradient forecasting search method， GFSM)来改善传统梯度法的缺点，用来提升一些以梯度学习法则为基础的分类器在资讯探勘上的效率与正确性；而一个所需资料量少、计算复杂度低且精确的预测模型是梯度预测搜寻法能否有效进行最佳解搜寻之关键因素，传统统计为基础之预测方法的缺点是需要较大量的数据进行预测，因此计算复杂度高，灰色预测模型具有建模资料少且计算复杂度低等优点，然而灰色预测理论以连续之微分方程式为基础，并且透过一些数学上的假设与近似，将连续之微分方程式转换成离散之差分方程式来对离散型资料进行建模及预测，这样的作法不尽合理，且缺乏数学理论上的完备性，因为在转换过程中已经造成建模上的误差，且建模过程仅考虑相邻的两个资料点关系，无法正确反应数列未来的变化趋势。

Features find, manage icons on your system, image-editing features such as effects, filters, rotation, cropping, as well as a huge color palette, easy-to-use editor that has all the functions for creating/editing ICO, ANI, CUR, GIF, JPEG, WMF, EMF, TGA, and WBMP files, image filters, including Blurring, Sharpening, Embossing, Diffusing, and Color Balance, image effects, including Linear Gradient, Radial Gradient, Rectangular Gradient, WAV, 3D Shadow, 3D Button, Text Gradient Effects, Noise, and Arbitrary Rotation, create icons with a preset sizes, use dynamic transparent colors, create cursors with free hotspots, create, view animated cursors, extract icon from any file or folder, paste images from clipboard, select Regions for Import from .bmp,.jpg,.gif, or .wmf images, Drag and Drop files for editing, unlimited undo and redo steps, support multi-language interface.

特点寻找，管理图标，您的系统，图像编辑等功能的影响，过滤器，旋转，剪裁，以及作为一个巨大的调色盘，易於使用的编辑器已全部职能，为创造/编辑保险公司条例，新华社，当前有GIF ， JPEG ，有WMF ，电磁场，热重分析， wbmp文件，图像过滤，包括模糊，锐化，压花，扩散，色彩平衡，图像效果，包括线性梯度，径向梯度，梯度矩形， wav ，三维阴影，三维按钮，文本梯度效应，噪音，以及任意旋转，创建图标与预设的大小，使用动态透明的颜色，创建游标与免费的热点，创建，查看动画光标，提取图标从任何文件或文件夹，粘贴图像从剪贴板中，选择地区进口。

Furthermore, the first-order optimality condition and its equivalent reformulations for generalized semi-infinite max-min programming with a non-compact set are presented using the lower-Hadamard directional derivative and subdifferential.2. Chapter 3 studies the gradient-type methods for unconstrained optimization problems. Section 1 proposes a new class of three-term memory gradient methods. The global convergence property of the method is established. Furthermore, in order to improve the convergence property of the method, a new class of memory gradient projection methods is presented with the property that the whole sequence of iterates converges to a solution to the problem under the conditions such as pseudo-convexity and continuous differentiability of objective function. In section 2, two new classes of methods, called gradient-type method with perturbations and hybrid projection method with perturbations, are proposed. In these methods, non-monotone line search technique is employed, which makes them easily executed in computer.

第3章研究了无约束优化问题的梯度型算法，第1节提出了一类新的三项记忆梯度算法，讨论了算法的全局收敛性，进一步提出了一类新的具有更好收敛性质的记忆梯度投影算法，并证明了该算法在函数伪凸的情况下具有整体收敛性，第2节在非单调步长搜索下提出了带扰动项的梯度型算法及其混合投影算法，这两类算法的一个重要特征就是步长采用线搜索确定而不象许多文献中那样要求步长趋于零，这样更容易在计算机上实现，在较弱的条件下证明了这些算法的全局收敛性，数值算例表明了算法的有效性。

In chapter 2 we propose a linear equality constraint optimization question , the new algorithm is combined with the new conjugate gradient method(HS-DY conjugate gradient method)and Rosen"s gradient projection method , and has proven it"s convergence under the Wolfe line search.In chapter 3 we have combined a descent algorithm of constraint question with Rosen"s gradient projection, and proposed a linear equality constraint optimization question"s new algorithm, and proposed a combining algorithm about this algorithm, then we have proven their convergence under the Wolfe line search, and has performed the numerical experimentation.

在第三章中我们将无约束问题的一类下降算法与Rosen投影梯度法相结合，将其推广到线性等式约束最优化问题，提出了线性等式约束最优化问题的一类投影下降算法，并提出了基于这类算法的混合算法，在Wolfe线搜索下证明了这两类算法的收敛性，并通过数值试验验证了算法的有效性。

MethodsTraditional gradient force, gradient plus pressure force and gradient vector flow force were used in active contour models to segment malleus, incus, eardrum and some shared surface images. The segmentation results were compared. ResultsBoth the gradient force and the GVF force resulted in satisfactory results when the initial contours were close to the boundary.

将传统的梯度外力、梯度加压力及梯度矢量流外力应用于主动轮廓模型，分别对砧骨、锤骨、砧骨和锤骨间的共享狭窄面及鼓膜等图像进行图像分割，比较图像分割的结果，选择收敛速度快、分割结果准确的方法。

In order to enlarge the application scope of our algorithm, the expansions of eLiveSet on four ways are studied, that is, the gradient of multi-relation, the constraints on the gradient cell, the gradient in the interval and the different gradient function.

为了扩大了算法处理问题的范围，给出了eLiveSet算法在多种关系间的梯度、梯度元组约束条件、区间上的梯度、不同的梯度函数四个方面的扩展，这些扩展可以通过修改算法的相关部分来实现。

The differential exhumation caused by the NNE-trending faults probably continued all the way from the 180℃isothermal surface, through 110℃isothermal surface to the 70℃isothermal surface. At 90Ma, Dabie orogen"s average height of topography, compared with the current sea level, reached 1.45 km (when geothermal gradient is chosen as 25℃/km) or 1.75 km (when geothermal gradient is chosen as 20℃/km), which is 4(when geothermal gradient is chosen as 25℃/km) or 5(when geothermal gradient is chosen as 20℃/km) times of the height of today"s surface topography in simulated region.

这种由于NNE向断裂系引发的差异推隆剥露，可能从等温面180℃锆石(U-Th／He的封闭温度、经过110℃(磷灰石裂变径迹的退火温度)一直延续到70℃等温面磷灰石(U-Th／He的封闭温度大别造山带90Ma时与现今海平面相比地形平均高度可达1.45km(地温梯度取25℃／km时)或1.75km(地温梯度取20℃／km时)，是现今模拟区域地形平均高度的4倍(地温梯度取25℃／km时)或5倍(地温梯度取20℃／km时)。

Sympegma regelii community, a rangeland desert vegetation, has the highest Shannon-Winner species diversity indices (1.706); the communities of Haloxylon ammodendron and Ephedra przewalskii, which have obvious feature of desert vegetation, are in the middle in species diversity indices (0.875-0.890); the communities of Calligonum mongolicum, Populus euphratica, Tamarix ramosissima and Glycyrrhiza inflata, characterized by desert forest of which saline desert bushes and saline meadows are scattered in the communities, have lowest value of the species diversity indices (0.079-0.495). 3 The structure of desert plant community is dominated by the bush layer. The species diversity indices of bush layer (0.769-1.451) is much higher than that of herb layer (0.193-0.254), and the diversity in herb layer is strongly influenced by bush layer. 4 The species diversity of desert plant communities shows a gradient of change with respect to longitude, latitude and elevation. For example, rangeland plant Sympegma regelii, with a high level of diversity indices (1.706), is in transition to desert plants Haloxylon ammodendronn community (with a low level of diversity indices of 1.379) in a longitude gradient and to saline Tamarix ramosessima community (with a low level of diversity indices of 0.376) in a latitude gradient. Calligonum mongolicum community, with a low level of species diversity (0.819), is in transition to Ephedra przewalskii (with a low level of diversity indices of 0.890) and Haloxylon ammondendron community (with the diversity indices of 0.645) in an elevational gradient.

群落Shannon-Wiener物种多样性水平表现为合头草群落最高(1.706)，具有草原化荒漠植被类型的成分；梭梭群落、膜果麻黄群落居中(0.875~0.890)，荒漠植被类型特征明显；沙拐枣群落、胡杨群落、多枝柽柳群落、胀果甘草群落较低(0.079~0.495)，荒漠林、盐地沙生灌丛及盐化草甸植被均有零星分布。3荒漠植物群落结构层次中，灌木层占居主导地位，群落灌木层物种多样性水平(0.769~1.451)远远大于草本层(0.193~0.254)，且草本层物种多样性受灌木层影响较大。4荒漠植物群落物种多样性分布格局表现为经向、纬向和海拔梯度的变化，经向、纬向变化为物种多样性水平较高的草原化植物合头草群落(1.076)向物种多样性水平较低的荒漠植物梭梭群落(1.379)和盐化植物多枝柽柳群落(0.376)的过渡，海拔梯度则呈现低水平的沙拐枣群落(0.819)到高水平的膜果麻黄群落(0.890)向低水平的梭梭群落(0.645)变化。

You can snipe the second and third union leaders from this position.

您可以鹬第二和第三工会领袖从这一立场出发。

Aiming at the currently shortage of XML streams quality detecting, this paper proposes a new forecasting method of XML streams quality by least squares support vector machines, which is used the method of XML keys' vector matrix as windows, and vector product wavelet transform to multilevel decompose and refactor the XML streams series, that can fulfill real-time checking demand of XML quality, and ensure constraint, consist- ency and integrality. For even more adapting net load, it proposes a control strategy by weight and adaptive adjustment to ensure XML streams quality.

针对当前XML数据流质量检测存在的不足，提出构建XML键的矢量矩阵作为窗口，利用矢量积小波变换多级分解与重构XML数据流，再结合最小二乘支持向量机对XML数据流质量进行预测的一种方法，满足XML数据流质量重构时实时检测的要求，保证XML数据的约束性、一致性与完整性；为了更好的适应网络负载，采取加权与自适应窗口调整等调度策略充分保证XML数据流的质量检测。