Size-Matching/Size-Calling Algorithm Size-Matching Size-Calling Algorithm This algorithm uses a dynamic programming approach that is efficient (runs in low polynomial time and space) and guarantees an optimal solution. It first matches a list of peaks from the electropherogram to a list of fragment sizes from the size standard. It then derives quality values statistically by examining the similarity between the theoretical and actual distance between the fragments. Size-Matching Algorithm Example Figure 3-14 shows an example of how the size-matching/calling algorithm works using contaminated GeneScan™ 120 size standard data. Detected peaks (standard and contamination) are indicated by blue lower bars along the x-axis. The size standard fragments as determined by the algorithm (and their corresponding lengths in base pairs) are designated by the upper green bars. Note that there are more peaks than size standard locations because the standard was purposely contaminated to test the algorithm. The algorithm correctly identifies all the size standard peaks and removes the contamination peaks (indicated by the black triangles) from consideration. The large peak is excluded from the candidate list by a filter that identifies the peak as being atypical with respect to the other peaks. Figure 3-14 Size-matching example
Abstract: In traditional planar maps, we use symbols to represent spatial objects. With the development of computer technique and 3D techniques and their application in GIS, traditional 2D static planar map is developed to 3D dynamic virtual environment. CyberCity is a hotspot in the field of GIS and it needs not only by DEM and 3D terrain to deliver the topographic information, but also 3D symbol models to represent spatial objects. Representation of CyberCity is not reconstruction in all details, but based on the abstraction and interpretation of real world according to the temporal- spatial, economic and technical constraints. So it is necessary to study on the 3D model. This paper discusses the transmission pattern of spatial information, the difference and relations between map symbols and 3D models, then illustrates the principles of 3D models based on their determinability, location, simplification and logicality. Keywords: 3D model; visualization; 3D GIS 信息 是表达一切事物存在方式和运动状态的知识 , 具有通讯、运算和感知等特性 ; 任何信息总是存在于某个信息系统中 , 信息在系统中传输和反馈 , 构成某种信息传输模式 ( 王家耀 , 陈毓芬 ,2000) 。 地图符号 是地图上用来表示实地物体与现象的特定图解记号 , 是以约定关系为基础的与客观事物具有指代关系的物质对象 ( 图形、图解 ) 。它有两个基本功能 : 首先 , 它能指出目标种类及数量和质量特征 ; 其次 , 能确定对象的空间位置和现象分布。 三维模型（朱庆，等） 就是在三维的条件下 , 描述实地物体与现象的图解模型。它具有平面地图符号的所有特征和基本功能 ( 危拥军 , 西安测绘研究所 , 2000) , 但更加直观逼真。笔者将三维模型定义为 : 在三维环境中 , 用来描述各种地理实体的形状、位置、大小、姿态以及地理现象的时空分布和变化特征的图形、图像以及图解模型。三维模型以更逼真的形式提 供给观察者更 多的空间信息 , 通过读者的视觉被感知。综合起来 , 三维模型具有以下的基本特性。 1) 三维模型能够给予读者更加直观的三维空间信息。而平面地图符号反映的都是空间物体的平面布局 , 其高程信息只是作为一个属性值而存在 , 并不能被直观地反映出来。三维模型不仅能够反映空间物体或对象的平面位置 ( x, y) , 高程信息 ( z ) 也同样可以反映出来。 2) 以更加直观和逼真的方式指出空间目标种类、数量和质量特征以及对象的空间位置和现象的时空分布 , 所以三维模型具有完整的时空定位特征。 3) 三维模型以电子为介质 , 读者不仅可以从电子介质观察图形图像的效果 , 还能够从具体的文件中得到数字信息 , 也就是说数字信息是以单独的文件保存起来的 , 并且这些数字信息是比较详细的。 三维模型分为以下三类 。 1) 第一类模型具有几何形态的不变性和表面材质纹理的相似性 , 具有重要的形状和位置特征。建立一个逼真的三维模型便可以重复使用 , 如电杆、路灯模型等。 2 ）第二类模型具有几何形态的随机性和表面材质纹理的相似性 , 具有重要的大小和位置特征。通过纹理图像表示这些目标 , 如树木、花草等。 3) 第三类模型具有几何形态与表面材质纹理表示的随机性。通过特定的随机函数模拟这些目标 , 如喷泉、瀑布、雨水、海浪、烟火等。 三维模型的定位特点： 三维空间地物的表达也需要一定的定位方法来确定空间对象的位置顺序。传统的二维点状符号在普通地图和专题地图中都有广泛应用。在地形图上 , 控制点、居民点、独立地物都采用了定名的或顺序的点状符号 , 这些符号的重心或质点 , 都与地物的地理位置 ( 经纬度或直角 坐标 ) 相重合 ( 蔡孟裔 , 2000) , 这是传统地图上点状符号的定位规则。与这种平面地图点状符号定位规则明显不同的是 , 三维模型的定位是由其三维空间表达的地理位置 ( x, y, z ) 或空间姿态参数决定的 , 如描述太阳系中的地球不仅需要地理位置 ( x , y , z ) 来表达空间位置 , 还需要地轴的倾角参数来表达地球在太阳系中的倾斜度。 视觉变量 包括形状、尺寸、颜色、纹理、方向以及透明度等。 总结 ，在以下方面还需要大量更加深入的研究 : 地理信息三维表示的空间认知 ; 研究网络环境下三维模型的实时生成技术 ; 具有时间维的三维模型动态演变技术 , 通过模型反映各种现象的时空变化。 朱庆 , 教授 , 博士生导师。主要研究方向是数字摄 影测量、虚拟现实和 GIS 等。 Email: zhuq66@ 263. net
Dynamic Days Asia Pacific 7 (DDAP7) Photos Group Photo Apart of members of DDAP7 International Advisory Committee consult about future venues of DDAP10 and DDAP 11
http://www.mdbbs.org/thread-16673-1-3.html fix deposit 各位大虾, 我想利用 fix deposit来沉积 原子 ,但是奇怪的是,写了这行 命令 ,查看dump 文件 ,居然没有沉积.请 高手 帮忙 . 以下是我的in文件. 看看问题到底出在哪里 units metal dimension 3 boundary p p fs newton on atom_style molecular ############## first test including injecting atoms bulk ########################## read_data data.m-bulk region origbox block 1.8 43.4 0.0 41.6 0.0 40.2 units box group origbox region origbox ##################################################################################### #add atom lattice bcc 5 mass 1 69.72 mass 2 14.01 #pair parameter pair_style tersoff pair_coeff * * GaN.tersoff Ga N neighbor 2 bin neigh_modify delay 0 #region region addbox block 1.8 43.4 0.0 41.6 0.0 40.2 units box group addbox region addbox region fix block 1.8 43.4 0.0 41.6 0.1 6.0 units box group fix region fix velocity fix set 0.0 0.0 0.0 fix fix fix setforce 0.0 0.0 0.0 region mobile block 1.8 43.4 0.0 41.6 7.0 40.2 units box group mobile region mobile velocity mobile create 1322.0 43454 dist gaussian fix mobile mobile nvt 1322.0 1322.0 1 drag 0.0 region substrate block 3.8 41.4 2.0 39.6 19.0 40.0 units box group substrate region substrate group substrate type 1 2 fix 4 substrate deposit 100 1 10 431579 region substrate vz -0.2 -0.2 fix 5 substrate deposit 100 2 10 630952 region substrate vz -0.3 -0.3 ################################ injection1 ########################################### # run thermo 100 #timestep 0.001ps dump 2 addbox xyz 5 dump.c2-T1 run 10000000 你试一下定义一个运算 compute mobile_temp mobile temp compute_modify mobile_temp dynamic yes 再在 fix mobile mobile nvt 1322.0 1322.0 1 drag 0.0 后加上 fix_modify mobile temp mobile_temp 注： 关键是要dynamic yes, 这样体系运算时才会动态改变总原子数
全名：2004-03Enhancing the instantaneous dynamic range of electronic warfare receivers using statistical signal processing 共121页。 与 电子战 接收机设计相关的毕业论文。 网盘直接下载地址： http://www.ctdisk.com/file/4353064
Dynamic-factor models Statas new dfactor command estimates the parameters of dynamic-factor models by maximum likelihood. Dynamic-factor models are flexible models for multivariate time series in which the observed endogenous variables are linear functions of exogenous covariates and unobserved factors, which have a vector autoregressive structure. The unobserved factors may also be a function of exogenous covariates. The disturbances in the equations for the dependent variables may be autocorrelated. We have data on industrial production ( ipman ), real disposable income ( dsp ), weekly hours worked ( awhi ), and the unemployment rate ( unrate ). We suspect there exists a latent factor that can explain all four of these series, and we conjecture that latent factor follows an AR(2) process. The first step is to fit our model: With our model fit, lets obtain dynamic forecasts for disposable income beginning in December 2008: . tsappend, add(3) . predict dsp_f, dynamic(tm(2008m12)) . tsline dsp dsp_f if month = tm(2005m1) Even more interesting is the path of our unobserved factor. We have hypothesized that all our observed variables follow the unobserved latent factor. We can obtain the one-step predictions of the factor by typing . predict factor, factor We can then trace the path of the factor by graphing the result: . tsline factor Extracting the latent factor in this manner is sometimes referred to as extracting or estimating an indicator. dfactor also estimates the parameters of static-factor models, seemingly unrelated regression (SUR) models, and vector autoregressive (VAR) models by maximum likelihood. dfactor allows for constraints on the covariance matrix of the errors in an SUR model and a VAR model. After estimation, you can predict both the endogenous variables and the unobserved factors. In addition to one-step predictions, dfactor can produce dynamic multistep predictions. For a complete list of whats new in time-series analysis, click here .
标签: dynamic
