A spatial sampling optimization package using MSN theory
砒砂岩区典型坡面土壤水分空间分布特征
中国水土保持科学Science o£ Soil and Water Conservation第19卷第1期2021年2月Vol. 19 No. 1Feb.2021砒砂岩区典型坡面土壤水分空间分布特征辛军伟尚振坤1,王俊鹏1,朱世雷1,甄 庆23,张兴昌心,马炳召2」（1•西北农林科技大学资源环境学院,712100,陕西杨凌;2.西北农林科技大学水土保持研究所黄土高原土壤侵蚀与旱地农业国家重点实验室,712100,陕西杨凌;3.中国科学院水利部水土保持研究所,712100,陕西杨凌）摘要：土壤水分是水文循环的重要组成部分,是干旱半干旱地区植被重建和生态环境修复的重要影响因素。
探明坡面土壤水分的空间分布特征，对于植被重建具有指导意义。
采用经典统计学和地统计学相结合的方式，分析坡面土壤水分的分布规律、变异特征和空间结构。
结果表明：1）坡面0~ 200 cm 土壤平均含水量介于9. 93% ~13. 88%之间,随土层深度的增加而增大，各层土壤水分均为中等程度变异，变异系数随深度的增加呈现减小趋势。
2）坡面不同深度土壤水分的差异与坡位有关。
浅层土壤水分坡中和坡下高于坡上，而在60 cm 以下正好相反，土壤水分坡上高于坡下。
3）高斯模型和球状模型能拟合大部分土层的空间结构，除60-80 cm 土层以外，其他土层土壤含水量均为强空间依赖性。
4）最小变程为15.60 m,可以为后续样点布设提供参考依据。
该研究结果有助于了 解砒砂岩地区土壤水分分布特征，对该地区土壤水资源评价和植被重建具有重要意义。
0 ~40 cm 土层坡中和坡下的土壤含水量比坡上高，坡下和坡中更有利于植被的恢复。
关键词：土壤水分；植被重建；空间变异；深剖面；砒砂岩区中图分类号：S152.7文献标志码：A 文章编号：2096-2673（2021）01 -0052-08DOI : 10.16843/j. sswc. 2021.01.007Spatial distribution characteristics of soil moisture on a typical slopein the feldspathic sandstone area of Inner MongoliaXIN Junwei 1, SHANG Zhenkun 1 ,WANG Junpeng 1 ,ZHU Shilei 1 ,ZHEN Qing 2-3,ZHANG Xingchang 1,2,3, MA Bingzhao 2,3（1. College of Natural Resources and Environment , Northwest A&F University , 712100, Yangling, Shaanxi , China ；2. State Key Laboratory of Soil Erosion and Dryland Agriculture on the Loess Plateau , Institute of Soil and Water Conservation ,Northwest A&F University , 712100, Yangling, Shaanxi , China ； 3. Institute of Soil and Water Conservation ,Chinese Academy of Sciences and Ministry of Water Resources , 712100, Yangling , Shaanxi, China ）Abstract : [ Background ] Soil moisture is an important part of hydrological cycle , and significantlyinfluences vegetation recovery and ecological environment restoration in arid and semiarid area. Understanding the spatial distribution characteristics of soil moisture on the slope is crucial for vegetationrestoration in the feldspathic sandstone area, which is widely distributed in the border of Shanxi ,Shaanxi , and Inner Mongolia in the north of the Loess Plateau. [ Methods ] A case study was conducted to reveal the spatial distribution characteristics of soil moisture , and 0 - 600 cm deep layer soil moisturesamples were obtained by soil drill sampling. Soil samples were collected at 10 cm intervals in the surface0-20 cm layer , and 20 cm intervals under 20 cm layer , and totally 862 samples were obtained . Classical收稿日期：2020-01-16修回日期：2020-02-13项目名称：国家重点研发计划“鄂尔多斯高原砒砂岩区生态综合治理技术” （2017YFC0504504）第一作者简介：辛军伟（1994—）,男,硕士研究生。
地统计与遥感---专业英语词汇
地统计以及遥感英文词汇300个：gray level co-occurrence matrix algorithm灰度共生矩阵算法characteristic of atmospheric transmission 大气传输特性earth resources technology satellite,ERTS 地球资源卫星Land-use and land-over change 土地利用土地覆盖变化Multi-stage stratified random sample 多级分层随机采样Normalized Difference Vegetation Index归一化植被指数Soil-Adjusted Vegetation Index土壤调整植被指数Modified Soil-Adjusted Vegetation Index修正土壤调整植被指数image resolution ,ground resolution影象分辨力(又称“象元地面分辨力”。
指象元地面尺寸。
) remote sensing information transmission遥感信息传输remote sensing information acquisition遥感信息获取multi- spectral remote sensing technology多光谱遥感技术Availability and accessibility 可用性和可获取性Association of Geographic Information (AGI) 地理信息协会Difference Vegetation Index差值植被指数image quality 影象质量Enhanced Vegetation Index增强型植被指数Ratio Vegetation Index比值植被指数Spatial autocorrelation 空间自相关Lag Size 滞后尺寸Ordinary kriging 普通克里金Indicator kriging 指示克里金Disjunctive kriging 析取克里金Simple kriging 简单克里金Bivariate normal distributions 双变量正态分布Universal kriging 通用克里金conditional simulation 条件模拟image filtering 图像滤波optimal sampling strategy 最优采样策略temporal and spatial patterns 时空格局Instantaneous field-of-view瞬时视场角azimuth 方位角wavelet transform method 小波变换算法priori probability 先验概率geometric distortion 几何畸变active remote sensing主动式遥感passive remote sensing 被动式遥感multispectral remote sensing多谱段遥感multitemporal remote sensing 多时相遥感infrared remote sensing 红外遥感microwave remote sensing微波遥感quantizing，quantization量化sampling interval 采样间隔digital mapping数字测图digital elevation model,DEM 数字高程模型digital surface model,DSM 数字表面模型solar radiation spectrum太阳辐射波谱atmospheric window 大气窗atmospheric transmissivity大气透过率atmospheric noise 大气噪声atmospheric refraction 大气折射atmospheric attenuation 大气衰减back scattering 后向散射annotation 注解spectrum character curve 波谱特征曲线spectrum response curve 波谱响应曲线spectrum feature space波谱特征空间spectrum cluster 波谱集群infrared spectrum 红外波谱reflectance spectrum反射波谱electro-magnetic spectrum 电磁波谱object spectrum characteristic地物波谱特性thermal radiation 热辐射microwave radiation微波辐射data acquisition数据获取data transmission数据传输data processing 数据处理ground receiving station地面接收站environmental survey satellite环境探测卫星geo-synchronous satellite地球同步卫星sun-synchronous satellite太阳同步卫星satellite attitude卫星姿态remote sensing platform 遥感平台static sensor 静态传感器dynamic sensor动态传感器optical sensor光学传感器microwave remote sensor微波传感器photoelectric sensor光电传感器radiation sensor辐射传感器satellite-borne sensor星载传感器airborne sensor机载传感器attitude-measuring sensor 姿态测量传感器image mosai图象镶嵌c image digitisation图象数字化ratio transformation比值变换biomass index transformation生物量指标变换tesseled cap transformation 穗帽变换reference data 参照数据image enhancement 图象增强edge enhanceme边缘增强ntedge detection边缘检测contrast enhancement反差增强texture enhancement 纹理增强ratio enhancement 比例增强texture analysis 纹理分析color enhancement 彩色增强pattern recognition 模式识别classifier 分类器supervised classification监督分类unsupervised classification非监督分类box classifier method 盒式分类法fuzzy classifier method 模糊分类法maximum likelihood classification最大似然分类minimum distance classification最小距离分类Bayesian classification 贝叶斯分类Computer-assisted classification机助分类illumination 照度principal component analysis 主成分分析spectral mixture analysis 混合像元分解fuzzy sets 模糊数据集topographic correction 地形校正ground truth data 地面真实数据Tasselled cap 缨帽变换Artificial neural networks 人工神经网络Visual interpretation 目视解译accuracy assessment 精度评价Omission error漏分误差commission error 错分误差Multi-source data 多源数据heterogeneous 非均质的Training sample 训练样本ancillary data 辅助数据dark-object subtraction 暗目标相减法discriminant analysis 判别分析‘salt and pepper’ effects 椒盐效应spectral confusion光谱混淆Cluster sampling 聚簇采样systematic sampling 系统采样Error matrix误差矩阵hard classification 硬分类Soft classification 软分类decision tree classifier 决策树分类器Spectral angle classifier 光谱角分类器support vector machine支持向量机Fuzzy expert system 模糊专家系统endmember spectral端元光谱Future extraction 特征提取image mosaic图像镶嵌density slicing密度分割least squares correlation 最小二乘相关data fusion 数据融合Image segmentation图像分割urban remote sensing 城市遥感atmospheric remote sensing大气遥感geomorphological remote sensing地貌遥感ground resolution地面分辨率ground date processing system地面数据处理系统ground remote sensing地面遥感object spectrum characteristic地物波谱特性space characteristic of object地物空间特性geological remote sensing地质遥感multispectral remote sensing多光谱遥感optical remote sensing technology光学遥感技术ocean remote sensing海洋遥感marine resource remote sensing海洋资源遥感aerial remote sensing航空遥感space photography航天摄影space remote sensing航天遥感infrared remote sensing红外遥感infrared remote sensing technology红外遥感技术environmental remote sensing环境遥感laser remote sensing激光遥感polar region remote sensing极地遥感visible light remote sensing可见光遥感range resolution空间分辨率radar remote sensing雷达遥感forestry remote sensing林业遥感agricultural remote sensing农业遥感forest remote sensing森林遥感water resources remote sensing水资源遥感land resource remote sensing土地资源遥感microwave emission微波辐射microwave remote sensing微波遥感microwave remote sensing technology微波遥感技术remote sensing sounding system遥感测深系统remote sensing estimation遥感估产remote sensing platform遥感平台satellite of remote sensing遥感卫星remote sensing instrument遥感仪器remote sensing image遥感影像remote sensing cartography遥感制图remote sensing expert system遥感专家系统active remote sensing主动式遥感passive remote sensing被动式遥感resource remote sensing资源遥感ultraviolet remote sensing紫外遥感attributive geographic data 属性地理数据attributes, types 属性，类型Geographic database types 地理数据库类型attribute data 属性数据Geographic individual 地理个体Geographic information (GI) 地理信息Exponential transform指数变换false colour composite 假彩色合成Image recognition 图像识别image scale 图像比例尺Spatial frequency 空间频率spectral resolution 光谱分辨率Logarithmic transform对数变换mechanism of remote sensing 遥感机理adret 阳坡beam width波束宽度biosphere生物圈curve fitting 曲线拟合geostationary satellite对地静止卫星glacis缓坡Field check 野外检查grating 光栅gray scale 灰阶Interactive 交互式interference干涉inversion 反演Irradiance 辐照度landsatscape 景观isoline 等值线Lidar激光雷达landform analysis地形分析legend 图例Map projection地图投影map revision地图更新Middle infrared中红外Mie scattering 米氏散射opaco 阴坡orbital period 轨道周期Overlap重叠parallax 视差polarization 极化Phase 相位pattern 图案quadtree象限四分树Radar returns雷达回波rayleigh scattering 瑞利散射reflectance 反射率Ridge山脊saturation 饱和度solar elevation太阳高度角Subset 子集telemetry遥测surface roughness表面粗糙度Thematic map专题制图thermal infrared热红外uniformity均匀性Upland 高地vegetal cover 植被覆盖watershed流域White plate白板zenith angle天顶角radiant flux 辐射通量Aerosol 气溶胶all weather 全天候angle of field 视场角Aspect 坡向atmospheric widow大气窗口atmospheric 大气圈Path radiance 路径辐射binary code二进制码black body 黑体Cloud cover云覆盖confluence 汇流点diffuse reflection漫反射Distortion畸变divide分水岭entropy熵meteosat气象卫星bulk processing粗处理precision processing精处理Bad lines 坏带single-date image单时相影像Decompose 分解threshold 阈值relative calibration 相对校正post-classification 分类后处理Aerophotograph 航片Base map 底图muti-temporal datasets 多时相数据集detector 探测器spectrograph 摄谱仪spectrometer 波谱测定仪Geostatistics 地统计Semivariogram 半方差sill 基台Nugget 块金Range 变程Kriging 克里金CoKriging 共协克里金Anisotropic 各向异性Isotropic 各向同性scale 尺度regional variable 区域变量transect 横断面Interpolation 插值heterogeneity 异质性texture 纹理digital rectification数字纠正digital mosaic 数字镶嵌image matching影像匹配density 密度grey level灰度pixel,picture element 象元target area目标区searching area 搜索区Spacelab 空间实验室space shuttle航天飞机Landsat陆地卫星Seasat 海洋卫星Mapsat测图卫星Stereosat 立体卫星aspatial data 非空间数据。
融合粒子群与改进蚁群算法的AUV路径规划算法
2021576海洋资源已经成为人类开发的重点，但复杂的海洋环境对人类水下作业有着极大的限制，水下机器人正在成为海洋作业的主角，自主式水下机器人（Autono-mous Underwater Vehicle，AUV）依靠自身携带的能源进行水下作业。
由于在整个过程中无法补充能源，因此利用路径规划与安全避障技术对AUV导航控制，是其能否精确、安全和完整地完成水下作业的关键。
AUV 路径规划问题已经成为了一个研究热点[1]，主要涉及两方面问题：一是对海洋环境进行三维建模；二是选取合适的算法进行全局路径规划。
海洋环境建模主要有两类方法：一类是规则地形模型，主要利用正方形、矩形等规则形状进行组合来表示海底表面；另一类是不规则地形模型，将三角形、多边形等不规则形状作为模型单元的基础[2]。
文献[3]使用Voronoi图法简化三维水下环境，生成全局路线图；文献[4]将Delaunay三角模型应用于被测地标，建立拓扑模型。
文献[5]利用八叉树模型来反映AUV工作环境，但主要应用于较大障碍物之间的路径规划，不适合存在许多小障碍物的环境；文献[6-7]不考虑水深，将三维空间简化为二维栅格模型，节省了空间，但却丢失了环境信息；文献[8-9]将三维空间划分为若干平面，然后利用二维栅格模型将每个平面栅格化，有效实现三维栅格建融合粒子群与改进蚁群算法的AUV路径规划算法朱佳莹，高茂庭上海海事大学信息工程学院，上海201306摘要：针对传统蚁群算法在处理自主式水下机器人AUV（Autonomous Underwater Vehicle）三维路径规划问题时存在初期寻径能力弱、算法收敛速度慢等问题，提出一种融合粒子群与改进蚁群算法的AUV路径规划算法PSO-ACO（Particle Swarm Optimization-improved Ant Colony Optimization）。
基于空间分层思想建立三维栅格模型实现水下环境建模；综合考虑路径长度、崎岖性、危险性等因素建立路径评价模型；先使用粒子群算法预搜索路径来优化蚁群算法的初始信息素；再对蚁群算法改进状态转移规则、信息素更新方式并加入奖惩机制实现全局路径规划。
基于改进遗传算法的磁流变阻尼器多目标空间优化布置
第50 卷第 5 期2023年5 月Vol.50，No.5May 2023湖南大学学报（自然科学版）Journal of Hunan University（Natural Sciences）基于改进遗传算法的磁流变阻尼器多目标空间优化布置张香成1，徐宏辉1，赵军2†，杨洋1（1.郑州大学力学与安全工程学院，河南郑州 450001；2.郑州大学水利与土木工程学院，河南郑州 450001）摘要：为解决空间结构中阻尼器位置和数量优化问题，基于遗传算法提出一种新型编码方式——“A-B”型数字编码，实现了对阻尼器优化布置的精确定位. 推导了磁流变阻尼器（MRD）附加刚度和附加阻尼矩阵，采取H2范数优化控制理论、代间比较权重等理论提出了改进遗传算法，并采用MATLAB软件开发了多目标空间优化布置改进遗传算法程序. 以10层钢筋混凝土框剪偏心结构为例，依据规范选取7条地震波作为动力时程输入，对优化方案及3种工况下结构位移、加速度、扭转控制进行计算，并分析了不同减震指标随MRD数量变化的发展趋势. 结果表明：优化目标函数值随进化代数迅速收敛；4种工况中优化方案控制效果最佳，顶层88号节点X、Y向减震率分别达39.45%、32.68%，结构扭转得到有效控制. 算例表明了改进遗传算法的有效性，实现了对空间结构阻尼器布置的精确定位，且结构得到最优控制.关键词：磁流变阻尼器；遗传算法；代间比较权重；多目标优化；动力分析中图分类号：TU352.1 文献标志码：AMulti-objective Spatial Optimization Arrangement of Magnetorheological Dampers Based on Improved Genetic AlgorithmZHANG Xiangcheng1，XU Honghui1，ZHAO Jun2†，YANG Yang1（1.School of Mechanics and Safety Engineering， Zhengzhou University， Zhengzhou 450001， China；2.School of Water Resources and Civil Engineering， Zhengzhou University， Zhengzhou 450001， China）Abstract：To solve the optimization problem of the position and quantity of dampers in spatial structure， a new coding method，“A-B”type digital coding， was proposed based on a genetic algorithm， which realizes the precise positioning of the optimal arrangement of dampers. The additional stiffness and damping matrix of the magnetorheo⁃logical damper （MRD） were deduced， the improved genetic algorithm was proposed by adopting the H2 norm opti⁃mal control theory and the intergenerational comparison weight， and the multi-objective spatial optimization arrange⁃ment was used to develop the improved genetic algorithm program using MATLAB software. Taking a ten-story rein⁃forced concrete frame-shear wall eccentric structure as an example， seven seismic waves were selected as dynamic∗收稿日期：2022-03-01基金项目：国家自然科学基金资助项目（51878621）， National Natural Science Foundation of China（51878621）；中原科技创新领军人才计划项目（ZYQR201912029）， Central Plains Technology Innovation Leading Talent Program（ZYQR201912029）；河南省重点研发与推广专项（202102310239）， Promotion Projects in Henan Province （202102310239）作者简介：张香成（1983—），男，河南漯河人，郑州大学副教授，博士† 通信联系人，E-mail：*************.cn文章编号：1674-2974（2023）05-0085-10DOI：10.16339/ki.hdxbzkb.2023058湖南大学学报（自然科学版）2023 年time history input according to the specification， the structural displacement， acceleration， and torsion control un⁃der an optimization scheme， and three working conditions were calculated， and the development trends of different shock absorption indexes with the number of MRDs were analyzed. The results show that the value of the optimization objective function converged rapidly with the evolutionary algebra. The optimization scheme has the best control ef⁃fect in the four working conditions， the shock absorption rates in the X and Y directions of the No. 88 node on the top floor reached 39.45% and 36.48%， respectively， and the structure torsion was effectively controlled. The numerical example showed the effectiveness of the improved genetic algorithm， the precise positioning of the damper arrange⁃ment of the space structure is realized， and the structure is optimally controlled.Key words：magnetorheological damper；genetic algorithm；generation-compared weight；multi-objective opti⁃mization；dynamic analysis磁流变阻尼器（Magnetorheological Damper，MRD）作为结构振动控制中最具有前景的半主动控制装置之一，因结构简单、响应迅速、阻尼力大且连续顺逆可调等特点而广泛应用于土木结构减震控制中［1-5］. 阻尼器对结构的减震控制效果不仅与阻尼器的数量、阻尼力大小有关，而且与阻尼器在结构中的位置密切相关，为了降低控制成本并且提高控制效率，需要对结构中的阻尼器进行优化配置. 1997年Takewaki［6］和Wu等［7］在结构振动控制领域引入遗传算法，并进行了大量的研究工作，至此，遗传算法在结构阻尼器优化布置中得到普遍应用. 针对具体的工程优化问题，选用适当的编码和相应的遗传算子进行优化，是遗传算法的一个重要研究方向.很多学者采用遗传算法“0-1”编码方式对阻尼器进行优化布置研究，0表示该处不设置阻尼器，1表示设置阻尼器. 贝伟明等［8］基于遗传算法采用等效二次型性能指标对MRD进行优化布置，证明了遗传算法的有效性；閤东东等［9］采用H2范数控制理论和改进遗传算法对相邻结构间阻尼器布置位置进行优化研究，结果表明该方法可有效减小结构的二次型性能指标；孙彤等［10］基于遗传算法对轨道式负刚度装置提出一种优化布置数学模型，并以10层钢筋混凝土（Reinforced Concrete，RC）结构为例研究了该装置优化布置基本原则；燕乐纬等［11］提出一种相对适应度函数，提高了遗传算法的进化效率；金波等［12］基于遗传算法将黏滞阻尼器替换杆件的模态应变能作为优化函数，实现了对网架结构的优化控制. “0-1”编码多将结构简化为层间剪切模型，忽略了阻尼器在结构平面和空间上分布方式的影响；对于大型空间结构，若实现精准定位则会造成“维度灾难”，计算量和计算时间难以承受.实数编码是用具体实数表示每层阻尼器数量，郭勇等［13］采用实数编码方式将遗传算法与劣出优入算法相结合，对输电塔阻尼器的布置方式进行了多目标优化研究. 数字序列编码中每个基因位数字表示楼层号，基因位数表示阻尼器个数. 燕乐纬等［14］基于遗传算法提出数字序列编码，并验证了该方法的有效性；马宏伟等［15］提出粗粒度并行遗传算法，提高了优化结果收敛速度及精度. 实数编码和数字序列编码大大缩短了基因维度，解决了二进制编码不能完备表达求解空间的问题，但无法实现阻尼器的精准定位.为实现空间结构中阻尼器优化布置的精准定位，基于遗传算法提出一种新型编码方式——“A-B”型数字编码，推导了MRD附加刚度和附加阻尼矩阵，采取H2范数优化控制理论、相对适应度函数、多目标优化、代间比较权重等理论提出了改进遗传算法. 采用MATLAB软件开发了改进遗传算法MRD多目标空间优化布置分析程序. 基于文献［16-17］的建模理论，以10层框剪偏心结构为算例验证了本文改进遗传算法的正确性和程序的有效性.1 改进遗传算法1.1 编码方式为实现高层空间结构阻尼器优化布置的精确定位，对遗传算法进行合理改进，提出一种新型编码方式——“A-B”型序列编码. 其中，A对应建筑结构的楼层数，B对应该楼层的具体位置，例如基因型为86第 5 期张香成等：基于改进遗传算法的磁流变阻尼器多目标空间优化布置［2-1，2-4，8-7，5-3，6-10，7-5］的个体对应表现型为该结构共布置6个阻尼器，分别布置在第二层第一个位置、第四个位置，第八层第七个位置等. 假设10层结构拟布置18个阻尼器，共100个可选位置，采用遗传算法对其进行优化布置研究，4种编码方式对比如表1所示.“A-B”型序列编码方式仅需明确定义每层可安设阻尼器位置的编号，相比于实数编码和数字序列编码可精确定位空间结构阻尼器具体安设位置，相比于“0-1”编码大大缩短基因长度，从而使得遗传算法更容易寻找最优解，加快寻优速度.1.2 遗传操作遗传操作包括选择、交叉、变异操作，是遗传算法的主体模块，其运算方法的设计决定了种群进化的方向.采用轮盘赌选择方式对各个体进行优胜劣汰操作，采用单点交叉方式，对A、B基因分开进行交叉操作，分别给予特定的交叉概率，互不影响. 其中交叉位置随机生成. 例如：a1 A：3 5 4 6-8 5 6；B：6-1 9 6 3 8 7.a2 A：3 6 6 5-4 5 8；B：6-5 9 6 3 8 1.a1和a2表示两个不同基因型的个体，其中“-”表示交叉位置，完成交叉操作后得到两个全新个体为：a1 A：3 5 4 6-4 5 8；B：6-5 9 6 3 8 1.a2 A：3 6 6 5-8 6 5；B：6-1 9 6 3 8 7.变异操作和交叉一致，A、B基因互不影响，其中变异基因位、变异基因编码在允许范围内随机生成.1.3 约束性条件及精英保留策略相比于“0-1”编码及数字序列编码方式，“A-B”型编码方式无须考虑交叉、变异操所造成的阻尼器数量变化约束处理问题，但会出现基因重复现象，即在交叉、变异过程中有可能出现两个基因位基因代码相同的情况. 基于“A-B”型编码方式的特点，提出一种基因代码转换方式，将个体各基因位代码转换为十进制数字，每个数字在交叉、变异过程中进行甄别，若出现相同数字则对其进行“变异”操作，直至无重复数字为止.在每代的选择、交叉、变异操作之后，将当代种群中最小适应度值与上述操作前最大适应度值进行对比，保留适应度值高的个体进入下一代，即“末尾淘汰、精英保留”. 随着种群的不断迭代，种群整体适应度值不断提高，即基因优良性不断提高.2 优化控制理论2.1 优化目标函数采用H2范数优化控制理论对MRD进行优化布置研究，MRD受控结构减震运动微分方程如下：M x(t)+C x(t)+K x(t)=-MR x g(t)-HF(t).（1）式中：M、C、K分别是结构的总质量矩阵、阻尼矩阵、总刚度矩阵；x(t)、x(t)、x(t)分别为受控框剪结构的加速度、速度、位移响应；x g(t)为施加在结构上的地震波加速度；R是地震作用列向量；H是MRD的位置矩阵；F(t)是MRD所提供的控制力向量. 阻尼矩阵C采用Rayleigh正交阻尼模型，详见文献［16］.对阻尼力进行变形重组，可得等效刚度和等效阻尼，如下：F(t)=GZ=Géëêêùûúúx(t)x(t)=G1x(t)+G2x(t).（2）式中：G为反馈增益矩阵，由线性二次型调节器控制算法求得，G1=G（：，1：m），G2=G（：，m+1：2m），m为结构自由度个数；Z=[x(t) x(t)]T为受控系统状态向量.结合公式（1）、（2）可得：M x(t)+Cˉx(t)+Kˉx(t)=-MR x g(t).（3）式中：Cˉ=C+HG2为结构的总阻尼矩阵；Kˉ=K+ HG1为结构的总刚度矩阵.式（3）的状态空间的状态函数为：z(t)=A z(t)+B w(t).zˉ(t)=C z(t)+D w(t).（4）式中：A=éëêêùûúú0I-M-1Kˉ-M-1Cˉ为受控系统阵（状态阵）；I为单位矩阵；B=éëêêùûúú0R为地震波输入阵（控制分布阵）；C为输出阵（测量阵），C=[-M-1K-M-1Cˉ]为加速度输出阵，C=[C1C2]为层间位移输出阵，表1 不同编码方式对比Tab.1 Comparison of different encoding methods编码方式“0-1”实数数字序列“A-B”合理性满足满足满足满足优良基因遗传性满足满足满足满足精准定位满足不满足不满足满足基因长度10010181887湖南大学学报（自然科学版）2023 年其中C 1=éëêêêêêêêêùûúúúúúúúú1000-11⋮⋮0⋮⋮000-11m ×m，C 2=zeros (m ，m )；D 为前馈阵（输入输出阵），D =0.对受控系统状态方程进行拉普拉斯变换，并求取其传递函数：G (s )=C (s I -A )-1B +D .（5）式中：s 为拉普拉斯算子［18］.传递函数的H 2范数可用式（6）计算：G (s )2=tr (C L c C Τ).（6）式中：L c 为Lyapunov 方程AL c +L c A Τ=-BB Τ的非负定解.2.2 多目标优化与代间比较权重多目标优化最优解称为Pareto 最优解，其特点是随着优化进程各目标函数齐头并进，且在无法提升任何目标函数的同时保证不削弱任何目标函数. 多目标优化算法以MRD 受控结构层间位移的H 2范数指标和加速度的H 2范数指标联合控制对MRD 布置位置进行优化计算，优化目标函数如式（7）所示.f =ωd()Zmax d-Z d +γ()Zmax d-Z mind+γ+ωα()Z max α-Z α+γ()Zmax α-Z minα+γ .（7）式中：ωd 与ωa 分别为结构层间位移和加速度的H 2范数指标的代间比较权重；Z max d 与Z max α与Z min d 与Z minα、Z d 与Z α分别为二者在未控、最优控制、当前方案下的指标值；γ为非零小量，避免分母为零.代间比较权重定义如下：ωk =t k ∑k =1n t k .（8）式中：t k =1n ，S =1；t k =Z S k min Z S -1k min ，S ≥2，n 为目标函数数量，S 为遗传代数，Z Sk min为第S 代种群中第k 个指标的最小值.代间比较权重使得进步慢的目标函数将获得较大的权值，以促进各目标函数间的均衡发展. 相比于人为定义权重值以及适应性权重的方法，其不仅排除了人为因素定义两个权重值不同造成的影响，而且提供了种群向最优目标进化的方向和搜索压力，同时又使得这种压力在各指标间均匀地进行分配.2.3 适应度函数适应度函数值的大小影响着个体被遗传到下一代的概率，个体适应度函数值越大，遗传到下一代的概率越高. 为实现目标函数到适应度函数的良性映射，提高优良个体遗传到下一代的概率，采用相对适应度函数进行遗传操作.F ˉ(x )=f (x i )-min {f (x )}.（9）式中：F ˉ(x )为相对适应度函数；f (x i )为个体目标函数；{f (x )}为当代种群目标函数集合.2.4 程序编制基于MRD 受控结构三维计算模型、经典遗传算法、“A-B ”型编码方式、H 2范数优化控制理论、多目标优化与代间比较权重等理论，采用MATLAB 软件开发了改进遗传算法阻尼器空间位置寻优程序. 程序计算流程如图1所示.3 优化模型及地震波选取3.1 优化模型以10层RC 框剪结构为例，结构的混凝土强度等级为C35，弹性模量为3.25×104 MPa ，泊松比为0.2，钢筋混凝土密度为2 500 kg/m 3，楼板厚度为0.12 m ，剪力墙厚度为0.20 m. 结构的三维模型如图2所示，一至三层框架柱的截面尺寸为0.7 m×0.7 m ，四层及以上框架柱的截面尺寸为0.6 m×0.6 m ；跨度为9 m 梁的截面尺寸为0.3 m×0.7 m ，跨度为4.5 m 梁的截面尺寸为0.25 m×0.5 m ，跨度为6.3 m 和6.0 m 梁的截面尺寸为0.25 m×0.6 m. RC 框剪结构的前两阶振型阻尼比假定为0.05，并将底层柱下端视为固结. 图2中图1 改进遗传算法阻尼器空间位置寻优程序流程图Fig.1 The flow chart of the optimization procedure for the spa⁃tial position of the damper in the improved genetic algorithm88第 5 期张香成等：基于改进遗传算法的磁流变阻尼器多目标空间优化布置数字为节点编号值及阻尼器位置编号. 建模理论、程序中所采用的MRD 及其在结构中的设置方式和文献［17］中一致，此处不赘述.3.2 地震波选取根据《建筑抗震设计规范》（GB 50011—2010）［19］，拟建场地建筑抗震设防烈度为7（0.1g ）度、Ⅲ类场地、设计地震分组为第一组，地震动加速度反应谱特征周期为0.45 s ，水平地震影响系数为0.5. 根据场地类别和设计地震分组，选取ATC-63 FEMA P-695规范［20］推荐的22条远场地震记录中的5条水平分量作为时程分析的实震输入，另外选取2条人工波作为补充，将7条地震波加速度时程最大值均设置为220 gal ，分别绘制加速度响应谱曲线、平均谱曲线与设计谱曲线，如图3所示. 与设计反应谱相比，7组地震波平均反应谱在结构主周期点上误差为6.48%（设计反应谱：0.209 3，平均反应谱：0.223 8），满足误差不超过20%的规范要求.4 优化方案验证结构共100个可选位置（每层10个），拟设18个MRD. 经过程序试算，初始种群大小设为300，交叉概率设置为0.6，A 、B 变异概率均设置为0.1，程序可迅速得出最优解. 程序设置为连续进化20代适应度函数值不发生改变或者进化到50代时程序自动停止搜索. 对于超大型建筑结构可参照文献［15］，能提高寻优速度和最终优化结果的准确度.4.1 位置优化结果程序运行235 min 计算完毕，多目标优化进程如图4所示. 图4（a ）~（c ）分别为以层间位移为输出的H 2范数指标、以加速度为输出的H 2范数指标和以多目标评价函数随进化代数的变化. 从图4（c ）中可以看出多目标评价函数指标随着进化进程前期迅速增大，中期缓慢上升，后期趋于稳定. 为验证优化结果的正确性，对4种工况进行对比，即：工况一，不设置MRD ；工况二，随机布置；工况三，底层均匀布置；工况四，优化布置方案. 优化方案中X 向MRD 共12个，Y 向6个，为排除各工况X 、Y 向阻尼器数量不同的影响，工况二、工况三中X 、Y 向阻尼器数量调整为12个、6个，并分别计算各工况下以层间位移为输出结构的H 2范数指标值H 2d 和以加速度为输出结构的H 2范数指标值H 2a ，如表2所示.4.2 时程响应对比为了更为明显地对比4种工况下MRD 对结构的减震控制效果，以顶层88号节点为例，7条地震波激励下的水平双向位移时程响应分别取前30 s 进行绘制（RSN-848地震波总时长28 s ，取全程），并分别在位移峰值处局部放大，如图5所示. 从图5中可以明显地看出，无论是在哪一条地震波激励下，工况二、工况三、工况四对应结构顶层88号节点的X 、Y 向位移均明显小于工况一条件下对应时间点下的位移响应，说明在地震波激励下MRD 可明显减小结构的位移响应. 工况二、工况三、工况四在7条地震波激励下结构顶层88号节点X 向最大位移的平均减震率分别为31.97％、34.81％、39.45％，Y向最大位移的平均图2 三维框剪偏心模型（单位：m ）Fig.2 3D frame shear eccentric model （unit ： m）图3 输入地震波各时程谱及平均谱与设计谱对比Fig.3 Comparison of each time-history spectrum and average spectrum of the input seismic wave with the design spectrum89湖南大学学报（自然科学版）2023 年减震率分别为22.93%、29.95%、32.68%. 该结果与表2各工况范数指标值相对应，说明了基于范数优化控制理论的改进遗传算法空间位置寻优程序的有效性，实现了对阻尼器最佳布置位置的精确定位.4.3 各层最大位移、加速度对比图6为7条地震波作用下框剪结构各层水平双向位移、加速度平均值包络图. 由图6可知，MRD 受控结构各层最大位移、最大加速度响应均小于未控结构. 其中，工况四X 向各层位移明显小于工况二、工况三，Y 向略小于工况二、工况三. 以中间楼层 第五层为例，7条地震波激励下工况一至工况四X 向平均位移分别为54.52 mm 、36.84 mm 、36.12 mm、（a ）层间位移指标优化进程 （b ）加速指标优化进程 （c ）评价函数指标优化进程图4 改进遗传算法多目标优化进程Fig.4 Improved genetic algorithm multi-objective optimization process（a ）RSN-169波作用 （b ）RSN-1602波作用 （c ）RSN-752波作用（d ）RSN-848波作用 （e ）RSN-1116波作用 （f ）人工波1作用（g ）人工波2作用图5 7条地震波激励下结构顶层88号节点水平双向位移时程响应Fig.5 Time history response of horizontal bidirectional displacement of No. 88 node on the top floor of the structure under the excitationof seven seismic waves90第 5 期张香成等：基于改进遗传算法的磁流变阻尼器多目标空间优化布置34.88 mm ，相比于工况一，工况二、工况三、工况四分别减少了32.43%、33.75、36.05%；Y 向平均位移分别为23.18 mm 、18.34 mm 、15.18 mm 、14.96 mm ，相比于工况一，工况二、工况三、工况四分别减少了20.88%、34.51%、35.46%. Y 向一至三层位移略小于其他各层，原因是该结构一至三层在Y 向布置了剪力墙，该位置自身刚度足够大，抗剪能力足够强.在地震波激励下，结构刚度和阻尼的增加均会减小位移响应，对于加速度而言，虽然阻尼的增加会减小结构的加速度响应，但刚度的增加会加大结构加速度响应. 因此，MRD 受控结构的加速度减震效果不如位移减震效果明显，甚至在某些时刻加速度会出现增大的现象. 因结构加速度响应结果的复杂性，单层加速度对比有大有小，故采用式（10）对工况二、工况三、工况四的加速度响应进行综合评价.J 1=1n ∑i =1n ()a ix maxa ix 0max +a iy max a iy 0max.（10）式中：J 1为加速度指标；n 为楼层数；a ix max 、a iy max 、a ix 0max 、a iy 0max 分别为X 、Y 向受控和未控状态下各层各节点最大加速度均值.经过计算，工况二、三、四加速度综合指标值J 1分别为1.794、1.778、1.771，说明3种工况相比，工况四的加速度综合减震率最大，工况三次之，工况二相对最小.4.4 结构扭转控制7条地震波激励下结构各层水平双向最大位移对比如表3所示. 从表3可看出工况二、工况三、工况四与工况一相比，X 向扭转略有增加但不明显，其中工况四各层位移比值更接近于1；Y 向工况二扭转加剧，工况三、工况四明显减小，其中工况四各层位移表2 各工况MRD 布置位置及H 2范数值Tab.2 MRD layout position and H 2 norm value of each scheme工况工况一工况二工况三工况四最优布置位置A BA B A B A B ――541228――352257――1023273――194224――945229――936236――231532――5102563――813523――264522――155531――486539――624725――485771――636767――74410310――8751051――3961053H 2d 1.771.040.980.88H 2a89.1675.4774.5873.82（a ）位移包络图 （b ）加速度包络图图6 框剪结构各层水平双向位移、加速度包络图Fig.6 Horizontal bidirectional displacement and acceleration envelopes of each layer of frame-shear structure91湖南大学学报（自然科学版）2023 年比值更接近于1，最大位移比值顶层1.064，远远小于工况一顶层最大位移比值1.314. 对比分析结果表明：MRD 布置位置不当会加剧结构的扭转响应；空间位置寻优算法计算结果对结构的扭转控制作用显著.4.5 数量优化研究基于结构安全性和设置MRD 带来的经济支出，本节对安设MRD 数量与结构减震性能的关系进行分析. 提出3种评价指标，分别为加速度指标J 1、层间位移角指标J 2、综合评价指标J 3，从多角度综合分析不同数量的阻尼器对结构整体的减震控制效果.J 2=1n ∑i =1n ()θix maxθix 0max +θiy max θiy 0max.（11）J 3=1néëêê∑i =1n (θix maxθix 0max +θiy max θiy 0max )+∑i =1n()a ix maxa ix 0max +a iy max a iy 0maxùûúú.（12）式中：θix max 、θiy max 、θix 0max 、θiy 0max 分别为X 、Y 向受控和未控状态下各层各节点最大层间位移角均值.为充分说明不同数量的阻尼器对结构综合减震率的影响，基于MRD 受控框剪结构三维框剪计算模型和改进遗传算法，对该结构不同数量的阻尼器布置位置进行优化. 优化程序参数设置和上文一致，分别计算设置12、16、20、24、28、32、36个阻尼器的最优分布位置，并计算在7条地震波激励下各工况以及满置下结构的综合评价指标，并取其平均值绘制阻尼器数量对应综合评价指标影响关系曲线，如图7所示. 从图7中可看出：J 1与J 2相比，J 2即层间位移角指标值较小，说明MRD 对结构的位移减震效果强于加速度的减震效果；3种评价指标均随着MRD 数量的增多总体上逐渐减小，减小幅值趋于平缓，说明随着结构中MRD 的不断增加，结构的层间位移角、加速度减震率虽逐渐增大，但减震性价比逐渐减小；通过对比3种评价指标走势发现，当MRD 数量超过18时，3种指标下降率均趋于平缓，在不考虑其他因素的情况下，说明该结构设置18个MRD 减震控制效果最佳.4.6 阻尼器耗能分析图8为RSN-1116地震波作用下X 向2~4号和Y 向6~7号MRD 的阻尼力-位移滞回曲线. 从图8中可以看出，在地震波的作用下，MRD 的滞回环呈椭圆形，且阻尼力幅值随位移幅值的增大而增大，说明表3 7条地震波作用下各层水平双向最大位移比平均值Tab.3 The average value of the horizontal bidirectional maximum displacement ratio of each layer under the action of sevenseismic wavesX 向位移比值楼层（节点/节点）1（12/16）2（20/24）3（28/32）4（36/40）5（44/48）6（52/56）7（60/64）8（68/72）9（76/80）10（84/88）工况一0.9990.9990.9970.9930.9880.9830.9810.9800.9800.980工况二1.0051.0010.9870.9840.9750.9600.9500.9410.9350.932工况三1.0011.0020.9920.9910.9870.9780.9730.9680.9650.964工况四0.9941.0020.9920.9900.9860.9800.9760.9740.9720.971Y 向位移比值楼层（节点/节点）1（13/16）2（21/24）3（29/32）4（37/40）5（45/48）6（53/56）7（61/64）8（69/72）9（77/80）10（85/88）工况一1.0981.1381.1441.2221.2681.2881.3001.3091.3121.314工况二1.1361.2071.2381.3291.3891.4451.4721.4851.4911.491工况三0.9831.0151.0211.0871.1271.1401.1511.1571.1601.162工况四0.9600.9890.9931.0331.0391.0491.0511.0561.0611.064图7 各减震评价指标随MRD 数量变化趋势图Fig.7 Trend chart of each shock absorption evaluation indexwith the number of MRDs92第 5 期张香成等：基于改进遗传算法的磁流变阻尼器多目标空间优化布置MRD 能够稳定地耗能；此外，位移和阻尼力不会在同一时刻达到最大值. 当滞回环中位移幅值小于2 mm 时，MRD 的阻尼力为10 kN ；当滞回环的位移幅值大于 14 mm 时，滞回环中MRD 的阻尼力最大可以达到200 kN.5 结 论1）基于遗传算法提出了“A-B ”型数字编码，推导了MRD 附加刚度和附加阻尼矩阵，采用H 2范数优化控制、代间比较权重等理论提出了改进遗传算法，并开发了MATLAB 程序，实现了对空间结构中MRD 的精确定位和多目标优化布置.2）基于MRD 受控RC 结构三维计算模型程序，采用该算法对10层框剪结构MRD 的布置进行优化研究，结果表明：改进遗传算法优化结果迅速收敛；4种工况中优化方案控制效果最佳，顶层88号节点X 、Y 向减震率分别达39.45%、32.68%，结构扭转得到有效控制.3）对不同数量MRD 优化布置进行分析，结果表明其综合减震指标虽然随着数量的增加而减小，但降低趋势也逐渐减小，该指标可为阻尼器的数量优化提供依据.4）MRD 的滞回环呈椭圆形，且阻尼力幅值随位移幅值的增大而增大，说明MRD 能够稳定地发挥耗能作用.参考文献［1］LI R ，ZHOU M J ，WU M J ，et al ．Semi-active predictive controlof isolated bridge based on magnetorheological elastomer bearing［J ］．Journal of Shanghai Jiaotong University （Science ），2019，24（1）：64-70．［2］董小闵，王陶，王羚杰，等．旋转式磁流变螺旋流动阻尼器扭矩增强研究［J ］．湖南大学学报（自然科学版），2021，48（10）：39-47．DONG X M ，WANG T ，WANG L J ，et al ．Research on torque enhancement of rotary magnetorheological damper based on helical flow ［J ］．Journal of Hunan University （Natural Sciences ），2021，48（10）：39-47．（in Chinese ）［3］梅真，郭子雄．磁流变阻尼器减振结构振动台试验与动力可靠性分析［J ］．湖南大学学报（自然科学版），2017，44（7）：41-48．MEI Z ，GUO Z X ．Shaking table test and dynamic reliability analysis of structures with MR dampers ［J ］．Journal of HunanUniversity （Natural Sciences ），2017，44（7）：41-48．（in Chinese ）［4］SEID S ，CHANDRAMOHAN S ，SUJATHA S ．Optimal design ofan MR damper valve for prosthetic knee application ［J ］．Journalof Mechanical Science and Technology ，2018，32（6）：2959-2965．［5］XU Z D ，XU F H ，CHEN X ．Vibration suppression on a platformby using vibration isolation and mitigation devices ［J ］．NonlinearDynamics ，2016，83（3）：1341-1353．［6］TAKEWAKI I ．Optimal damper placement for minimum transferfunctions ［J ］．Earthquake Engineering & Structural Dynamics ，1997，26（11）：1113-1124．［7］WU B ，OU J P ，SOONG T T ．Optimal placement of energydissipation devices for three-dimensional structures ［J ］．Engineering Structures ，1997，19（2）：113-125．［8］贝伟明，李宏男．磁流变阻尼器在结构减震控制中的位置优化研究［J ］．工程抗震与加固改造，2006，28（3）：73-78．BEI W M ，LI H N ．Study on the optimal placement of magnetorheological damper in structural control ［J ］．Earthquake Resistant Engineering and Retrofitting ，2006，28（3）：73-78．（inChinese ）［9］閤东东，朱宏平，陈晓强．相邻结构间被动控制装置的位置优化设计［J ］．振动、测试与诊断，2010，30（1）：11-15．GE D D ，ZHU H P ，CHEN X Q ．Optimal design of passive damper's positions between two adjacent structures ［J ］．Journalof Vibration ，Measurement & Diagnosis ，2010，30（1）：11-15．（in（a ）NO.2~4 MRD（b ） NO.6~7 MRD图8 RSN-1116波作用下MRD 的阻尼力-位移滞回曲线Fig.8 Damping force-displacement hysteresis curve of MRDunder the action of RSN-1116 wave93。
结合超体素与PFCM的点云分割方法
第４５卷 第４期２０２１年７月激 光 技 术ＬＡＳＥＲＴＥＣＨＮＯＬＯＧＹＶｏｌ．４５，Ｎｏ．４Ｊｕｌｙ，２０２１ 文章编号：１００１ ３８０６（２０２１）０４ ０５３５ ０６结合超体素与ＰＦＣＭ的点云分割方法张树益１，常建华１，２，毛仁祥１，李红旭１，张露瑶１（１．南京信息工程大学江苏省大气环境与装备技术协同创新中心，南京２１００４４；２．南京信息工程大学江苏省气象探测与信息处理重点实验室，南京２１００４４）摘要：为了实现点云数据的区域划分，提出了一种结合超体素与粒子群优化模糊Ｃ均值（ＰＦＣＭ）的聚类分割算法（ＳＰＦＣＭ）。
用随机采样一致性算法去除点云平面，根据３ Ｄ点云的空间位置、曲率以及快速直方图特征，利用八叉树体素化点云得到超体素。
采用ＰＦＣＭ算法对超体初步划分，并对粘连的点云再划分，克服了ＰＦＣＭ算法对于堆叠物体无法分割及较大物体过分割的缺点，并在ＯＳＤ ｖ０．２数据集上对ＳＰＦＣＭ算法进行了性能测试。
结果表明，相较于ＰＦＣＭ算法，ＳＰＦＣＭ不仅保留了其参量少、操作简单等优点，而且指标得到了较大提升，准确率达到８６％，查全率达到８３％。
该研究对３ Ｄ点云复杂场景的准确分割提供了帮助与参考。
关键词：激光技术；点云分割；超体素；模糊聚类；粒子群优化中图分类号：ＴＰ３９１ 文献标志码：Ａ ｄｏｉ：１０ ７５１０／ｊｇｊｓ ｉｓｓｎ １００１ ３８０６ ２０２１ ０４ ０２０ＰｏｉｎｔｃｌｏｕｄｓｅｇｍｅｎｔａｔｉｏｎｍｅｔｈｏｄｃｏｍｂｉｎｉｎｇｓｕｐｅｒｖｏｘｅｌｓａｎｄＰＦＣＭＺＨＡＮＧＳｈｕｙｉ１，ＣＨＡＮＧＪｉａｎｈｕａ１，２，ＭＡＯＲｅｎｘｉａｎｇ１，ＬＩＨｏｎｇｘｕ１，ＺＨＡＮＧＬｕｙａｏ１（１．ＣｏｌｌａｂｏｒａｔｉｖｅＩｎｎｏｖａｔｉｏｎＣｅｎｔｅｒｏｆＡｔｍｏｓｐｈｅｒｉｃＥｎｖｉｒｏｎｍｅｎｔａｎｄＥｑｕｉｐｍｅｎｔＴｅｃｈｎｏｌｏｇｙ，ＮａｎｊｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＩｎｆｏｒｍａｔｉｏｎＳｃｉｅｎｃｅ＆Ｔｅｃｈｎｏｌｏｇｙ，Ｎａｎｊｉｎｇ２１００４４，Ｃｈｉｎａ；２．ＪｉａｎｇｓｕＫｅｙＬａｂｏｒａｔｏｒｙｏｆＭｅｔｅｏｒｏｌｏｇｉｃａｌＯｂｓｅｒｖａｔｉｏｎａｎｄＩｎｆｏｒｍａｔｉｏｎＰｒｏｃｅｓｓ ｉｎｇ，ＮａｎｊｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＩｎｆｏｒｍａｔｉｏｎＳｃｉｅｎｃｅ＆Ｔｅｃｈｎｏｌｏｇｙ，Ｎａｎｊｉｎｇ２１００４４，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ｉｎｏｒｄｅｒｔｏｒｅａｌｉｚｅｔｈｅａｒｅａｄｉｖｉｓｉｏｎｏｆｐｏｉｎｔｃｌｏｕｄｄａｔａ，ａｓｅｇｍｅｎｔａｔｉｏｎａｌｇｏｒｉｔｈｍ（ＳＰＦＣＭ）ｃｏｍｂｉｎｉｎｇｓｕｐｅｒｖｏｘｅｌｓａｎｄｐａｒｔｉｃｌｅｓｗａｒｍｏｐｔｉｍｉｚａｔｉｏｎｆｕｚｚｙＣ ｍｅａｎｓ（ＰＦＣＭ）ｗａｓｐｒｏｐｏｓｅｄ．Ａｒａｎｄｏｍｓａｍｐｌｉｎｇｃｏｎｓｅｎｓｕｓａｌｇｏｒｉｔｈｍｗａｓｕｓｅｄｔｏｒｅｍｏｖｅｔｈｅｐｏｉｎｔｃｌｏｕｄｐｌａｎｅ．Ａｃｃｏｒｄｉｎｇｔｏｔｈｅｓｐａｔｉａｌｐｏｓｉｔｉｏｎ，ｃｕｒｖａｔｕｒｅａｎｄｆａｓｔｐｏｉｎｔｆｅａｔｕｒｅｈｉｓｔｏｇｒａｍｃｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆｔｈｅ３ Ｄｐｏｉｎｔｃｌｏｕｄ，ｔｈｅｏｃｔｒｅｅｖｏｘｅｌｉｚａｔｉｏｎｐｏｉｎｔｃｌｏｕｄｗａｓｕｓｅｄｔｏｏｂｔａｉｎｔｈｅｓｕｐｅｒｖｏｘｅｌ．ＴｈｅＰＦＣＭａｌｇｏｒｉｔｈｍｗａｓｕｓｅｄｔｏｐｒｅｌｉｍｉｎａｒｉｌｙｄｉｖｉｄｅｔｈｅｓｕｐｅｒｂｏｄｙａｎｄｓｕｂｄｉｖｉｄｅｔｈｅｃｏｎｎｅｃｔｅｄｐｏｉｎｔｃｌｏｕｄ，ｗｈｉｃｈｏｖｅｒｃｏｍｅｓｔｈｅｓｈｏｒｔｃｏｍｉｎｇｓｏｆｔｈｅＰＦＣＭａｌｇｏｒｉｔｈｍｆｏｒｓｔａｃｋｉｎｇｏｂｊｅｃｔｓａｎｄｏｖｅｒ ｓｅｇｍｅｎｔａｔｉｏｎｏｆｌａｒｇｅｒｏｂｊｅｃｔｓ．ＴｈｅｐｅｒｆｏｒｍａｎｃｅｔｅｓｔｏｆｔｈｅＳＰＦＣＭａｌｇｏｒｉｔｈｍｗａｓｐｅｒｆｏｒｍｅｄｏｎｔｈｅＯＳＤ ｖ０．２ｄａｔａｓｅｔ．ＴｈｅｅｘｐｅｒｉｍｅｎｔａｌｒｅｓｕｌｔｓｓｈｏｗｔｈａｔｃｏｍｐａｒｅｄｗｉｔｈｔｈｅＰＦＣＭａｌｇｏｒｉｔｈｍ，ｉｔｎｏｔｏｎｌｙｒｅｔａｉｎｓｉｔｓａｄｖａｎｔａｇｅｓｓｕｃｈａｓｆｅｗｅｒｐａｒａｍｅｔｅｒｓａｎｄｓｉｍｐｌｅｏｐｅｒａｔｉｏｎ，ｂｕｔａｌｓｏｔｈｅｉｎｄｅｘｈａｓｂｅｅｎｇｒｅａｔｌｙｉｍｐｒｏｖｅｄ，ａｎｄｔｈｅａｃｃｕｒａｃｙｉｓｕｐｔｏ８６％，ｗｈｉｌｅｔｈｅｒｅｃａｌｌｒａｔｅｒｅａｃｈｅｓ８３％．Ｔｈｉｓｒｅｓｅａｒｃｈｐｒｏｖｉｄｅｓｈｅｌｐａｎｄｒｅｆｅｒｅｎｃｅｆｏｒｔｈｅａｃｃｕｒａｔｅｓｅｇｍｅｎｔａｔｉｏｎｏｆｃｏｍｐｌｅｘｓｃｅｎｅｓｉｎ３ Ｄｐｏｉｎｔｃｌｏｕｄｓ．Ｋｅｙｗｏｒｄｓ：ｌａｓｅｒｔｅｃｈｎｉｑｕｅ；ｐｏｉｎｔｃｌｏｕｄｓｅｇｍｅｎｔａｔｉｏｎ；ｓｕｐｅｒｖｏｘｅｌ；ｆｕｚｚｙｃｌｕｓｔｅｒｉｎｇ；ｐａｒｔｉｃｌｅｓｗａｒｍｏｐｔｉｍｉｚａｔｉｏｎ 基金项目：国家自然科学基金资助项目（６１８７５０８９）；江苏省研究生科研与实践创新计划资助项目（ＳＪＣＸ１９ ０３０８）作者简介：张树益（１９９５ ），女，硕士研究生，现主要从事３维点云处理方面的研究。
NC Sampling 1.0 最近中心采样软件说明书
Package‘NCSampling’October12,2022Type PackageTitle Nearest Centroid(NC)SamplingVersion1.0Date2017-06-26Author GJ MelvilleMaintainer Gavin Melville<**********************.gov.au>Imports yaImpute,lattice,randomForestDescription Provides functionality for performing Nearest Centroid(NC)Sampling.The NC sam-pling procedure was developed for forestry applications and selects plots for ground measure-ment so as to maximize the efficiency of imputation estimates.It uses multiple auxiliary vari-ables and multivariate clustering to search for an optimal sample.Further de-tails are given in Melville G.&Stone C.(2016)<doi:10.1080/00049158.2016.1218265>. License GPL-2NeedsCompilation noRepository CRANDate/Publication2017-06-2706:14:25UTCR topics documented:NCSampling-package (2)Addz (2)Alloc (4)Centroids (5)Check.pop (6)DesVar (7)DVar (8)Existing (9)NC.sample (10)NC.select (11)nundle.sf (12)R.sample1 (13)Spatial.plot (14)training (15)12AddzIndex17 NCSampling-package Nearest Centroid(NC)SamplingDescriptionSuite of functions to perform NC ed by forestry practitioners to select reference plotsfor imputation using remotely sensed data,for example aerial laser scanning(ALS)data.DetailsPackage:NCSamplingType:PackageVersion:1.0Date:2017-06-26License:GPL-2Depending on the application,the functions are usually called in the following order:-Check.pop-check populationfile for errorsAlloc-allocate sample numbers to strataExisting-determine the virtual plots,in the target set,which are neighbours to pre-existing plotsAlloc-re-allocate sample numbers to strata,taking into account pre-existing plots and their neighboursNC.sample-select reference plots from the candidate set,using the internal functions Centroids and NC.select. Spatial.plot-display the virtual plots,including the NC sample plots,as an x-y graph.DesVar-calculate approximate design variances for each stratum and for the whole population.Author(s)G Melville Maintainer:<**********************.gov.au>ReferencesG.Melville&C.Stone.(2016)Optimising nearest neighbour information-a simple,efficientsampling strategy for forestry plot imputation using remotely sensed data.Australian Forestry,79:3,217:228,DOI:10.1080/00049158.2016.1218265.Addz AddzDescriptionAdd variable/s to the populationfile which are good predictors of the variables/s of interestAddz3UsageAddz(popfile,training,yvars,xvars,pool)Argumentspopfile dataframe containing population data-as a minimum there must be columns named’PID’(plot identifier),’Strata’and’plot_type’.training dataframe containing training data.Must contain auxiliary variables and vari-able/s of interest.yvars vector containing the name of each variable of interest(dependent variable).xvars vector containing the names of the auxiliary variables.pool logical value-should the training data be pooled across strata prior tofitting the regression model?DetailsThe predictor variable for the each variable of interest(dependent variable)is obtained by perform-ing random forest regression on the training data using the designated auxiliary variables.The training data can be pooled across strata(pool=T),orfitted separately within each strata(the de-fault).Not normally called directly.ValueA list with components:-popfile populationfile-data frame,as above,with predictor variable/s added to thefile r.sqared dataframe containing the R-squared values obtained from the random forest re-gression/sAuthor(s)G.MelvilleReferencesRandom forest regression is performed using the randomForest package.See AlsoDesVar,randomForest.Examples##Addz(popfile,training,yvars,xvars)4Alloc Alloc AllocationDescriptionAllocate sample among several strata,using proportional allocation.Inputs populationfile and total sample size.Outputs sample sizes for each stratumUsageAlloc(popfile,ntotal)Argumentspopfile dataframe containing population data-as a minimum there must be columns named’PID’(plot identifier),’Strata’and’plot_type’.ntotal total sample size-required number of reference plots for all strata combined. DetailsPerforms a proportional allocation,by calculating the required sample size for each stratum(i)using the formula n_i=n*N_i/N,where n is the sample size(number of reference plots)and N is the number of target plots.ValueA vector of sample sizes,one for each stratum in the populationfile.Author(s)G.MelvilleSee AlsoExisting and NC.sample.Examplespopfile<-data.frame(PID=1:20,Strata=rep(c( A , B ),c(12,8)),plot_type=rep( B ,20))tot.samp<-6Alloc(popfile,tot.samp)Centroids5 Centroids Calculate centroidsDescriptionSeparates a single stratum of the populationfile into n clusters andfinds the centroid of each cluster, where n is the sample size.Not intended to be called directly.UsageCentroids(popfile,nrefs,desvars,ctype,imax,nst)Argumentspopfile populationfile-dataframe containing information relating to all plots in the stratum.nrefs scalar defining the number of reference plots-required sample size for the stra-tum.desvars character vector containing the names of the design variables.ctype clustering type-either k-means(’km’)or Ward’s D2(’WD’).imax maximum number of iterations when calling the k-means clustering procedure.nst number of random initial centroid sets when calling the k-means clustering pro-cedure.DetailsThe virtual plots are partitioned so as to minimise the sums of squares of distances from plots to cluster centroids.This is done by using a multivariate clustering procedure such as k-means clustering(Hartigan&Wong,1979)or Ward’s D2clustering(Murtagh&Legendre,2013),using standardized design variables and a Euclidean distance metric.Valuecentroids dataframe containing centroids.cmns dataframe containing centroid means.Author(s)G MelvilleReferencesHartigan&Wong(1979)Algorithm AS136:a K-means clustering algorithm.Applied Statistics 28,100-108,DOI:10.2307/2346830.Murtagh,M&Legendre,P.(2014)Ward’s hierarchical agglomerative clustering method:which al-gorithms implement Ward’s criterion?Journal of Classification,31,274-295,DOI:10.1007/s00357-014-9161-z.6Check.pop See AlsoExisting,NC.sample and kmeans.Examples##Centroids(popfile,nrefs,desvars,ctype= km ,imax=200,nst=20)Check.pop Check populationfileDescriptionCarries out a range of checks on the populationfile to detect the most commonly encountered errors.Provides a barchart showing the population structure.UsageCheck.pop(popfile,desvars)Argumentspopfile dataframe containing information for all plots in the population.desvars vector containing the names of the design variables.ValueReports on any errors found and produces a barchart.Author(s)G.MelvilleSee AlsoNC.sample.Examples##Check.pop(popfile,desvars)DesVar7 DesVar Design variances for NC sample.DescriptionFor each stratum,and for the population as a whole,approximate design variances are calculated.UsageDesVar(popfile,nrefs,desvars,yvars,kvalue,B=1000,zvars=NULL,training=NULL,xvars=NULL,pool=F)Argumentspopfile dataframe containing information on all plots in the population.nrefs vector containing the sample size of each stratum.desvars vector containing the names of the design variables.yvars character vector containing the name of each variable of interest(dependent variable)for which design variances are required.kvalue scalar specifying the value of k for the k-nn imputation.B number of re-samples used to calculate the design variances.zvars character vector containing the name/s of the predictor variables.training dataframe containing the data needed to determine the predictor variable.Must contain the necessary yvars and xvars.If missing,predictor variables are sup-plied by the user(zvars)xvars character vector containing the name/s of the predictor variables.pool logical value-should strata be pooled prior tofitting regression model? DetailsApproximate design variances are calculated using a re-sampling procedure in conjunction with a predictor variable.The predictor variable can be user-supplied or determined by the program using random forest regression based on a set of training data.The regression model can befitted sepa-rately for each strata(pool=F),the default,or based on pooled training data with stratum included in the regression model as a factor.ValueA dataframe containing the design variances for each stratum and for the whole population. Author(s)G.Melville8DVarSee AlsoNC.sample.Examples##DesVar(popfile,nrefs,desvars,yvars,B=1000,zvars=NULL,##training=NULL,xvars=NULL,pool=F)DVar Design variances for single stratum.DescriptionFor a single stratum approximate design variances are calculated.Not intended to be called directly. UsageDVar(popfile,nrefs,yvars,desvars,kvalue,B=1000)Argumentspopfile dataframe containing information on stratum of interest.nrefs scalar containing the sample size of the stratum.yvars character vector containing the name of each variable of interest(dependent variable)for which design variances are required.desvars character vector containing the names of the design variables.kvalue scalar specifying the value of k for the k-nn imputation.B number of re-samples used to calculate the design variances.ValueA dataframe containing the design variances for the stratum of interest.Data used to calculate theseare also returned.Author(s)G.MelvilleSee AlsoNC.sample,DesVar.Examples##DesVar(popfile,nrefs,yvars,kvalue,desvars,B=1000)Existing9 Existing Pre-existing plot neighboursDescriptionDetermines the plots which are close,in the auxiliary space,to the pre-existing plots.UsageExisting(popfile,nrefs,desvars,draw.plot)Argumentspopfile dataframe containg information on all plots in the populationfile.nrefs vector containing the number of reference plots in each stratum.desvars vector containing the names of the design variables.draw.plot logical variable-should a bar graph be drawn to show the number of neighbours for each pre-existing plot?ValueA list with components:-Nx vector containing the number of neighbours to existing plots in each stratum.Ng vector containing the number of target plots in each stratum.popfile dataframe containing the original populationfile with neighbours to pre-existing plots separately identified.Author(s)G Melville.See AlsoNC.sample.Examples##Existing(popfile,nrefs,desvars,draw.plot=T)10NC.sample NC.sample Nearest Centroid(NC)SampleDescriptionSelects NC sample in multiple strata.UsageNC.sample(popfile,nrefs,desvars,ctype,imax,nst)Argumentspopfile dataframe containing information on all plots in the population.nrefs vector containing the sample size of each stratum.desvars vector containing the names of the design variables.ctype clustering type-either k-means(’km’)or Wards D(’WD’).imax maximum number of iterations for the k-means procedure.nst number of initial random sets of cluster means for the k-means procedure.DetailsIn each stratum the population of virtual plots is segregated into n clusters where n is the stratum sample size(number of reference plots).The virtual plots are partitioned so as to minimise the sums of squares of distances from plots to cluster centroids.This is achieved by using a multivariate clustering procedure such as k-means clustering(Hartigan&Wong,1979)or Ward’s D clustering (Murtagh&Legendre,2013),using standardized design variables and a Euclidean distance metric.Following determination of the cluster centroids,the virtual plot,in the candidate set,closest to each centroid is selected as a reference plot.ValueA list with components:-popfile populationfile-dataframe,as above,with reference plots designated as’R’cmns centroid meansAuthor(s)G.MelvilleNC.select11 ReferencesG.Melville&C.Stone.(2016)Optimising nearest neighbour information-a simple,efficientsampling strategy for forestry plot imputation using remotely sensed data.Australian Forestry, 79:3,217:228,DOI:10.1080/00049158.2016.1218265.Hartigan&Wong(1979)Algorithm AS136:a K-means clustering algorithm.Applied Statistics 28,100-108,DOI:10.2307/2346830.Murtagh,M&Legendre,P.(2013)Ward’s hierarchical agglomerative clustering method:Which algorithms implement Ward’s criterion?Journal of Classification.See AlsoSee also NC.sample.Examples##NC.sample(popfile,nrefs,desvars,ctype= km ,imax=200,nst=20)NC.select Nearest Centroid(NC)Plot SelectionDescriptionSelect the reference plots closest,in the auxiliary space,to the target plot centroids.Not intended to be called directly.UsageNC.select(popfile,nrefs,desvars,centroids)Argumentspopfile dataframe containing information on all plots in the stratum.nrefs vector containing the number of reference plots in the stratum.desvars vector containing the names of the design variables.centroids dataframe containing the centroids for the stratum.ValueA list with components:-refs dataframe containing reference plotsexist dataframe containing pre-existing plotstargs dataframe containing target plots12nundle.sfAuthor(s)G.MelvilleSee AlsoNC.sample.Examples##NC.select(popfile,nrefs,desvars,centroids)nundle.sf Nundle State Forest LiDAR dataDescriptionLiDAR data from two strata acquired by over-flying the Nundle State Forest(SF),NSW,Australia in2011Usagedata(nundle.sf)FormatA data frame with2068observations on the following12variables.PID numeric vector containing unique plot IDsheight numeric vector containing LiDAR heightsmeanht numeric vector containing LiDAR mean heightsmam a numeric vector containing mean above mean heightsmdh a numeric vector containing LiDAR mean dominant heightspstk a numeric vector containing LiDAR stocking ratecc a numeric vector containing LiDAR canopy coverOV a numeric vector containing LiDAR occupied volumevar a numeric vector containing LiDAR height variancesStrata a factor with levels O,Yx a numeric vector containing x-coordinatesy a numeric vector containing y-coordinatesDetailsThe LiDAR variables were calculated as outlined in Turner et al.(2011).R.sample113SourceForestry Corporation of NSWReferencesMelville G,Stone C,Turner R(2015).Application of LiDAR data to maximize the efficiency of inventory plots in softwood plantations.New Zealand Journal of Forestry Science,45:9,1-16.doi:10.1186/s40490-015-0038-7.Stone C,Penman T,Turner R(2011).Determining an optimal model for processing lidar data at the plot level:results for a Pinus radiata plantation in New SouthWales,Australia.New Zealand Journal of Forestry Science,41,191-205.Turner R,Kathuria A,Stone C(2011).Building a case for lidar-derived structure stratification for Australian softwood plantations.In Proceedings of the SilviLaser2011conference,Hobart, Tasmania,Australia.Examplesdata(nundle.sf)R.sample1Random sample.DescriptionSelects random sample in a single stratum.UsageR.sample1(popfile,nrefs)Argumentspopfile dataframe containing information on all plots in the stratum.nrefs vector containing the required sample size of the stratum.DetailsA random sample of virtual plots is selected from the candidate set in the stratum of interest. ValueA list with components:-popfile populationfile-dataframe,as above,with plot type of reference plots set to’R’Author(s)G.Melville14Spatial.plotSee AlsoNC.sample.Examples##R.sample1(popfile,nrefs)Spatial.plot Spatial PlotDescriptionSpatial(x-y)graph of candidate plots,target plots,pre-existing plots,reference plots and neighbours to pre-existing plots.UsageSpatial.plot(popfile,sampfile)Argumentspopfile dataframe containing information on all plots in the population prior to the sam-ple.sampfile dataframe containing information on all plots in the population after the sample. ValueDraws an x-y plot showing the location of different plots in each stratum.Author(s)G.MelvilleSee AlsoSee also NC.sample.Examples##Spatial.plot(popfile,sampfile)training Nundle State Forest LiDAR dataDescriptionContains LiDAR data for200plots from two strata acquired by over-flying the Nundle State Forest (SF),NSW,Australia in2011Usagedata(training)FormatA data frame with200observations on the following10variables.OV a numeric vector containing LiDAR occupied volumeheight numeric vector containing LiDAR heightscc a numeric vector containing LiDAR canopy coverpstk a numeric vector containing LiDAR stocking ratevar a numeric vector containing LiDAR height variancesx a numeric vector containing x-coordinatesy a numeric vector containing y-coordinatesStrata a factor with levels O YPID numeric vector containing unique plot IDsplot_type a factor with levels B C TDetailsThe LiDAR variables were calculated as outlined in Turner et al.(2011).SourceForestry Corporation of NSWReferencesMelville G,Stone C,Turner R(2015).Application of LiDAR data to maximize the efficiency of inventory plots in softwood plantations.New Zealand Journal of Forestry Science,45:9,1-16.doi:10.1186/s40490-015-0038-7.Stone C,Penman T,Turner R(2011).Determining an optimal model for processing lidar data at the plot level:results for a Pinus radiata plantation in New SouthWales,Australia.New Zealand Journal of Forestry Science,41,191-205.Turner R,Kathuria A,Stone C(2011).Building a case for lidar-derived structure stratification for Australian softwood plantations.In Proceedings of the SilviLaser2011conference,Hobart, Tasmania,Australia.Examplesdata(training)Index∗datasetsnundle.sf,12training,15∗packageNCSampling-package,2Addz,2Alloc,4Centroids,5Check.pop,6DesVar,3,7,8DVar,8Existing,4,6,9NC.sample,4,6,8,9,10,11,12,14NC.select,11NCSampling(NCSampling-package),2 NCSampling-package,2nundle.sf,12R.sample1,13Spatial.plot,14training,1517。
Data Sampling Methods
Data Sampling MethodsData sampling methods are essential in the field of data analysis as they help researchers and analysts to draw conclusions about a population based on a sample of data. There are various data sampling methods, each with its own advantages and disadvantages. In this response, we will explore different data sampling methods, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and convenience sampling. We will also discuss the importance of choosing the right sampling method and the potential biases that can arise from using certain methods.Simple random sampling is one of the most basic and commonly used data sampling methods. It involves selecting a random sample from the entire population, where each individual has an equal chance of being chosen. This method is relatively easy to implement and helps to ensure that the sample is representative of the population. However, it can be time-consuming and costly to obtain a complete list of the population in order to select a random sample.Systematic sampling is another method that is relatively easy to implement. It involves selecting every kth individual from the population, where k is calculated as the population size divided by the sample size. This method is less time-consuming than simple random sampling and still ensures a degree of randomness in the sample. However, if there is a pattern in the population, systematic sampling can lead to a biased sample.Stratified sampling involves dividing the population into subgroups or strata based on certain characteristics, such as age, gender, or income level, and then selecting a random sample from each stratum. This method ensures that each subgroup is represented in the sample, which can lead to more accurate results when analyzing the data. However, it requires prior knowledge of the population characteristics and can be more complex and time-consuming to implement.Cluster sampling involves dividing the population into clusters, such as geographic areas or schools, and then randomly selecting clusters to include in the sample. This method is often more practical and cost-effective than other sampling methods, especially when thepopulation is large and dispersed. However, it can lead to clusters being overrepresented or underrepresented in the sample, which can introduce bias.Convenience sampling is a non-probability sampling method that involves selecting individuals who are easily accessible or convenient to the researcher. This method is quick and inexpensive, making it a popular choice for many researchers. However, it can lead to a biased sample, as individuals who are easily accessible may not be representative of the entire population.When choosing a data sampling method, it is important to consider the specific research question, the characteristics of the population, and the resources available. Each sampling method has its own advantages and disadvantages, and the choice of method can have a significant impact on the results of the analysis. It is also important to be aware of potential biases that can arise from using certain sampling methods, such as selection bias or non-response bias, and to take steps to minimize these biases.In conclusion, data sampling methods play a crucial role in the field of data analysis, as they help researchers and analysts to draw conclusions about a population based on a sample of data. There are various data sampling methods, each with its own advantages and disadvantages, and it is important to choose the right method based on the specific research question and the characteristics of the population. It is also important to be aware of potential biases that can arise from using certain sampling methods and to take steps to minimize these biases in order to ensure the accuracy and reliability of the results.。
IBM Cognos Transformer V11.0 用户指南说明书
计算机英语词汇
计算机英语词汇---数据库transaction n.交易，办理，执行query n.查询license n.执照，许可证，特许subschemas n.子模式criminal a.犯了罪的，有罪的individual n.个体，个人conviction n.定罪，信服，坚信employee n.职员，受雇人员bureaus n.局，办公处integrity n.完整，正直insurance n.保险，保险业，保险费duplicate a.复制的，二重的retrieval n.取回，恢复，修补interactive n.交谈式security n.安全，安全性audit n.查帐，稽核integrity n.完整，正直，廉正trail n.痕迹，踪迹consume Vt.消耗multiuse n.多用户manually ad.用手full-fledged a.喂养tedious a.沉闷的，冗长乏味的compound document复合文件DBMS数据库管理系统recognizant a.认识的，意识的consensus n.一致，交感user manual用户手册semantics n.语义学bug n.缺陷，错误impediment n.妨碍，阻碍，阻止encrypt v.加密，译成密码intuitively a直觉的malicious a.环恶意的，恶毒的module n.模块，组件bottleneck n,瓶颈schema n.轮廓，概要，图解mainstream n主流proposal n建议spatial a.空间的，空间性的tailor vi.定制，制作，缝制relevant a.有关联的，中肯的plausible a.似真实的，似合理的urgency n.紧急，催促virtually ad.事实上optimization n.最佳化impracticably ad.不能实validation n.确认flaw n.缺点，裂纹，瑕疵typically a.典型的，象征性的assumption n.假定，视为当然之事index n.索引Yi.做索引duration n.持续时间，为期component n.组件，成分intolerably ad.难耐的程度temporal n.当时的，现世的abort vi.流产，失败semantics n.语义学rigorous a.严厉的，严酷的，苛刻的interval n．时间间隔criterion n.标准，准据，轨范catalogue n.目录v.编入目录consistency n.一致性，坚固性，浓度cabinet n.橱柜，内阁adopt vt.采用，收养illustration n.例证，插图serialization n.连载长篇efficient a有效率的，能干的log n.日志，记录clerical a.事务上的，抄写员的focus n.焦点，焦距access n.进入．进入twin n.双胞胎中人warehouse n.大商店．仓库protocol n.协议wholesale n.批发conflict n神突，矛盾chore n.零工，家务negotiate vi.商议，谈判，谈妥mode n.模式，模态drag vi.拖拉，拖累long-duration长期architects n.建筑师short-duration短期partition n.分割，隔离物ascend V.上升，追溯，登高．inherent a.固有的，与生俱来的descend vi.下降，传下necessitate Vt.迫使，使成为必需dimensional a.空间的versa a.反physical organization物理组织operator n.操作员正文计算机英语词汇：数字电路digital circuit数字电路inclusive a.一包含的，包括的logic n.逻辑bit n.少量gate n逻辑门multibit多位logical methodology逻辑方法arithmetic operation算术运算Boolean algebra布尔代数bus总线two-state两态data bus数据总线logical multiplication逻辑乘simultaneously ad.同时地logical addition逻辑加parallel register并行寄存器logical complementation逻辑非serial register串行寄存器logical function逻辑函数shift register移位寄存器inverter n.反相器latch n．锁存器transistor n.晶体管electromechanical calculator电动式计算器diode n.二极管logic symbol逻辑符号resistor n电阻器electromagnet n.电磁铁logic circuit逻辑电路energize Vt.使活跃，激励Flip-flop n.发器armature n.电枢counter n.计数器relay n.电器adder n.加法器mechanical latch机械式,logic variable逻辑变量set vt.置位logic operation逻辑运算reset vt.复位characteristic n.特征，特性figure图the SET output置位输出端conjunction(logical product) n.合取the RESET input复位输入端disjunction(logical sum) n.析取first-level n.一级active a.有效的negation(NOT) n. 反（非）inactive a.无效的AND gate与门construct vt．构造，设想truth table真值表resident program常驻程序power n.功率，乘幂utility 公用程序，实用condition n.条件diskcopy n.磁盘拷贝命令verbalize v.以语言表现，唠叨exception n.例外vice Vera反之亦然batch n.批，成批the AND function＂“与”函数specify vt.指定，说明the OR function＂“或”函数discrepancy n.相差，差异，差别the NOT function＂“非”函数trigger n.触发器exemplify vt.例证，例示representative n.代表，典型计算机英语词汇---应用软件指南maintenance n.维护，维修Quit Batch退出批处理install vt.安装．安置adapter n.适配器advanced a.高等的,在前的MDA单显适配器copyright n.版权，著作权CGA彩色图形适配器duplication n.副本，复制EGA增强型图形适配器key letter关键字VGA视频图形阵列delete vt.删除destructive a.破坏的，毁灭性的character string字符串insert vt.寸击入，镶补verify vt.查证，证实bland a.温和的，乏味的readable a.可读的capacity n.容量，能力attribute n.属性，标志seek vt.搜寻，试图list n.目录，名单，明细serial port串行口sort vt.排序，分类，挑选loopback回送alternate a.交互的，轮流的specify vt.叙述，指定format n.格式plug n.插日argument n.争论，引数，要旨ommunicate vt.沟通，传达match vt.使相配，使比赛peripheral a.周边的，外设的path n.路径，小路，轨道aspect n.外观，方面pathname n.路径名transfer n.迁移，转移，传递head n.头cache program高速缓存程序relocation n.再布置，变换布置subsystem n.子系统，次要系统add vt.增加overall a.全部的．全体的prune/graft修剪／移植throughput n.生产量．处延艳力resident n.常驻程序numeric coprocessor数学处理器compression n. 缩，缩小identify vt.识别，认明，鉴定reduce vt.减少，分解bargraph n.长条图，直方图comment n.批评，注解report n.报告，报道extract vt.摘录，析取virus病毒query n.查询anti-virus反病毒integrity n.完整immunize vt.使免疫，赋予免疫性convert vt.使改变infection n.传染，影响self-extractor自抽出器original a.最初的，原始的batch n.批，成批result n.结果，成绩，答案filename n.文件名consider vt. Vi.考虑，思考，认为freshen vt.Vi.（使）显得新鲜extra n.额外的事物check n.支票，检查restart v.重新启动join Vt.连接，结合detect vt.发现，察觉verbose a.冗长，累赘的define vt.定义，详细说明edit vt.编辑编校suspicious a.可疑的，疑惧的backup file备份文件activity n.活动,动作switch n.开关转换warn n.警告，注意beep vi.&vt.嘟嘟响present a.现在的，出席的setting n设置exclusive a.独占的，唯一的set mode设置模式configuration n.配置assume vi.假定，承担virus protection防病毒density n.密度scan n.扫描细查inch n.英寸signature file签一名文件compatible a.兼容的，能共处的editor n.编辑器exception n.例外，除外microcomputer n.微机support n.支持，支撑，援助retrieve v.恢复，检索executable a.可执行的，可运行的innovation n.改革，创新documentation n文件manipulate vt.操纵，利用hit n.打击，冲撞hardcopy n.硬拷贝parameter n.参数，媒介变数spell-checking拼写检查evaluate vt.评估，评价indicator n.指示器thesaurus n.辞典，同义词occur vi.发生，想到，存在merge vt.使合并，使消失valid a.有效的，正当的function key功能键buffer n.缓冲区，缓冲familiarize vt.使熟悉，使熟知destination disk目标盘wrap n. /vt.包装，限制，包裹source disk源盘blink n.闪亮，闪烁overwrite vt.改写block vt.阻塞，封锁test n.检验restore恢复由backup制作的盘horizontal n.水平线，水平面performance n.绩效，表现，演出the space bar空格键interrupt n.中断accessory n附件，同谋group n.团体，团retain vt.保持，留住，保有floppy drive软盘驱动器locking n.锁定hard drive硬盘驱动器monitor n.显示器parallel ports并行口appropriate a.适当的arrow n.箭，箭头记号button n．按钮highlight n.加亮区，精彩场面optimize Vt.使完善，优化计算机英语高级词汇---Video3D（Three Dimensional，三维）3DS（3D SubSystem，三维子系统）AE（Atmospheric Effects，雾化效果）AFR（Alternate Frame Rendering，交替渲染技术）Anisotropic Filtering（各向异性过滤）APPE（Advanced Packet Parsing Engine，增强形帧解析引擎）AV（Analog Video，模拟视频）Back Buffer（后置缓冲）Backface culling（隐面消除）Battle for Eyeballs（眼球大战，各3D图形芯片公司为了争夺用户而作的竞争）Bilinear Filtering（双线性过滤）CEM（cube environment mapping，立方环境映射）CG（Computer Graphics，计算机生成图像）Clipping（剪贴纹理）Clock Synthesizer（时钟合成器）compressed textures（压缩纹理）Concurrent Command Engine （协作命令引擎）Center Processing Unit Utilization（中央处理器占用率）DAC（Digital to Analog Converter，数模传换器）Decal（印花法，用于生成一些半透明效果，如：鲜血飞溅的场面）DFP（Digital Flat Panel，数字式平面显示器）DFS（Dynamic Flat Shading，动态平面描影，可用作加速）Dithering（抖动）Directional Light（方向性光源）DME（Direct Memory Execute，直接内存执行）DOF（Depth of Field，多重境深）dot texture blending（点型纹理混和）Double Buffering（双缓冲区）DIR（Direct RenderingInfrastructure，基层直接渲染）DVI（Digital Video Interface，数字视频接口）DxR（DynamicXTendedResolution，动态可扩展分辨率）DXTC（Direct X TextureCompress，DirectX纹理压缩，以S3TC为基础）Dynamic Z-buffering（动态Z轴缓冲区，显示物体远近,可用作远景）E-DDC（Enhanced Display DataChannel，增强形视频数据通道协议，定义了显示输出与主系统之间的通讯通道，能提高显示输出的画面质量）Edge Anti－aliasing（边缘抗锯齿失真）E-EDID（Enhanced ExtendedIdentification Data，增强形扩充身份辨识数据，定义了电脑通讯视频主系统的数据格式）Execute Buffers（执行缓冲区）environment mapped bumpmapping（环境凹凸映射）Extended Burst Transactions（增强式突发处理）Front Buffer（前置缓冲）Flat（平面描影）Frames rate is King（帧数为王）FSAA（Full Scene Anti－aliasing，全景抗锯齿）Fog（雾化效果）flip double buffered（反转双缓存）fog table quality（雾化表画质）GART（Graphic AddressRemappng Table，图形地址重绘表）Gouraud Shading（高洛德描影，也称为内插法均匀涂色）GPU（Graphics Processing Unit，图形处理器）GTF（Generalized TimingFormula，一般程序时间，定义了产生画面所需要的时间，包括了诸如画面刷新率等）HAL（Hardware AbstractionLayer，硬件抽像化层）hardware motion compensation（硬件运动补偿）HDTV（high definition television，高清晰度电视）HEL（Hardware Emulation Layer，硬件模拟层）high triangle count（复杂三角形计数）ICD（Installable Client Driver，可安装客户端驱动程序）IDCT（Inverse Discrete Cosine Transform，非连续反余弦变换，GeForce的DVD硬件强化技术）Immediate Mode（直接模式）IPPR（Image Processing and Pattern Recognition，图像处理和模式识别）large textures（大型纹理）LF（Linear Filtering，线性过滤，即双线性过滤）lighting（光源）lightmap（光线映射）Local Peripheral Bus（局域边缘总线）mipmapping（MIP映射）Modulate（调制混合）Motion Compensation（动态补偿）motion blur（模糊移动）MPPS（Million Pixels Per Second，百万个像素/秒）Multi-Resolution Mesh（多重分辨率组合）Multi Threaded Bus Master（多重主控）Multitexture（多重纹理）nerest Mipmap（邻近MIP映射，又叫点采样技术）Overdraw（透支，全景渲染造成的浪费）partial texture downloads（并行纹理传输）Parallel Processing PerspectiveEngine（平行透视处理器）PC（Perspective Correction，透视纠正）PGC（Parallel GraphicsConfiguration，并行图像设置）pixel（Picture element，图像元素，又称P像素，屏幕上的像素点）point light（一般点光源）point sampling（点采样技术，又叫邻近MIP映射）Precise Pixel Interpolation（精确像素插值）Procedural textures（可编程纹理）RAMDAC（Random AccessMemory Digital to AnalogConverter，随机存储器数/模转换器）Reflection mapping（反射贴图）render（着色或渲染）S端子（Seperate）S3（Sight、Sound、Speed，视频、音频、速度）S3TC（S3 Texture Compress，S3纹理压缩，仅支持S3显卡）S3TL（S3 Transformation &Lighting，S3多边形转换和光源处理）Screen Buffer（屏幕缓冲）SDTV（Standard DefinitionTelevision，标准清晰度电视）SEM（spherical environmentmapping，球形环境映射）Shading（描影）Single Pass Multi-Texturing（单通道多纹理）SLI（ScanlineInterleave，扫描线间插，3Dfx的双Voodoo 2配合技术）Smart Filter（智能过滤）soft shadows（柔和阴影）soft reflections（柔和反射）spot light（小型点光源）SRA（Symmetric RenderingArchitecture，对称渲染架构）Stencil Buffers（模板缓冲）Stream Processor（流线处理）SuperScaler Rendering（超标量渲染）TBFB（Tile Based Frame Buffer，碎片纹理帧缓存）texel（T像素，纹理上的像素点）Texture Fidelity（纹理真实性）texture swapping（纹理交换）T&L（Transform and Lighting，多边形转换与光源处理）T-Buffer（T缓冲，3dfx Voodoo4的特效，包括全景反锯齿Full-scene Anti-Aliasing、动态模糊Motion Blur、焦点模糊Depth of Field Blur、柔和阴影Soft Shadows、柔和反射Soft Reflections）TCA（Twin Cache Architecture，双缓存结构）Transparency（透明状效果）Transformation（三角形转换）Trilinear Filtering（三线性过滤）Texture Modes（材质模式）TMIPM（Trilinear MIP Mapping，三次线性MIP材质贴图）UMA（Unified Memory Architecture，统一内存架构）Visualize Geometry Engine（可视化几何引擎）Vertex Lighting（顶点光源）Vertical Interpolation（垂直调变）VIP（Video Interface Port，视频接口）ViRGE（Video and Rendering Graphics Engine，视频描写图形引擎）Voxel（Volume pixels，立体像素，Novalogic的技术）VQTC（Vector-Quantization Texture Compression，向量纹理压缩）VSIS（Video Signal Standard，视频信号标准）v-sync（同步刷新）Z Buffer（Z缓存）计算机英语词汇---操作系统与DOS操作基础storage space存储空间Timer n.计时器subdirectory n.子目录Available a.可用的structure n.结构characteristic n.特征,特性hierarchical a.分层的Sophistication n.复杂性issue vt.发行，放出Standard n.标准backslash n.反斜杠Online n.联机the root directory根目录Job Management 作业管理perform vt.执行Sequence n.次序conjunction n.联合Assess vt.评估procedure n.过程Resource Management资源管理tree n.目录树Oversee vt.监督term n.术语Control of I/0 Operation I/0 操作控制startup vi.启动Allocation n. 分配TSRs内存驻留程序Undergo vt.经历，经受locate vt.定位Error Recovery错误恢复sector n.扇区Memory Management存储器管理partition n.分区interface n.界面booting n.自举streamlined a.流线型的cluster n.簇unleash vt.释放CMOS互补金属氧化物体unhamperer vt.解脱emergency disk应急磁盘spreadsheet n.电子表格partition table分区表Accessory n.附件FAT文件分配表Notepad n.记事薄GUI图形用户接口Macro Recorder n.宏记录器command line命令行Write n.书写器icon n.图标Paint-brush n.画笔manual n.手册modem n.调制解调器dialog boxes对话框Solitaire n.接龙mechanism n.机构，机械，结丰Reverse n.挖地雷clipboard n.剪贴板module n.模块DDE动态数据交换acronym n.缩写字clumsy a.笨拙的version n.版本hot linked映射的update vt.洲一级，更新real-mode n.实模式internal command 内部命令standard mode标准模式external command外部命令directory n.目录Pentium n.俗称586，奔腾芯片sign-on a.提示framework n.框架，结构extension name扩展名precedence n.优先document n.文档uppercase letter大写字母workspace n.工作lowercase letter小写字母File Manager文件管理volume label卷标menu n.菜单prompt n.提示符Program Manager程序管理器default n.缺省值，默认值folder n.卷宗symbol n.符号divider n.分配者cursor n.光标subdivide n.子分配者built-in a.内置的tutorial n.教程正文常见计算机英语词汇解释library 库,程序库linkage 连接to load 装入,寄存,写入,加载location 存储单元logger 登记器,记录器loop 循环machine language 机器语言magnetic storage 磁存储器magnetic tape 磁带matrix 矩阵memory 存储器message 信息,报文microcomputer 微型计算机module n.组件,模块monitor n.监视器,监督程序,管程nanosecond 毫微秒network n.网络,网numeric, numerical 数字的,数值的octet n.八位位组,八位字节operator 操作员optical character reader 光符阅读机optical scanner 光扫描器output 输出overflow 溢出,上溢panel 平板parameter 参数,参量perforator 穿孔机peripheral equipment 外围设备,外部设备personal computer 个人计算机printed circuit 印制电路printer 打印机printout 打印输出to process 处理processing unit 处理部件program 程序to program 程序编制programmer 程序设计员programming 程序设计,程序编制pulse 脉冲punch 穿孔to punch 穿孔punched card, punch card穿孔卡片punched tape, punch tape穿孔纸带punch hole 孔,穿孔random access 随机存取to read 读reader 阅读程序reading 阅读real time 实时record, register 记录redundancy 冗余routine 例行程序selector 选择器,选择符sentinel 标记sequence 序列,顺序sequential 顺序的serial 串行的.连续的shift 移位,移数signal 信号simulation 模拟simulator 模拟器,模拟程序software 软件,软设备sort 分类,排序sorter 分类人员,分类机,分类程序,排序程序storage 存储器to store 存储subroutine, subprogram 子程序switch 开关symbol 符号symbolic language 符号语言system 系统tabulator 制表机teleprinter 电传打字机terminal 终端terminal unit 终端设备timer 时钟,精密计时器time sharing 分时timing 定时track 磁道transducer 传感器,翻译机translator 翻译程序,翻译器to update 更新Winchester disk drive 温彻斯特磁盘机,硬盘机working storage 工作存储器计算机英语词汇---程序设计Program Design程序设计creep vi.爬，潜行writing program编写程序standardize vt.使标准化coding the program编程simplify vt.单一化，简单化programming程序revision n.校订，修正programmer n.程序员occupy vt.占领，住进logic n.逻辑，逻辑学BASIC初学者通用符号指令代码machine code机器代码teaching language教学语言debug n.DOS命令，调试simplicity n.单纯，简朴compactness a.紧凑的，紧密的timesharing system分时系统description n.描述，说明interactive language交互式语言break n.中断manufacturer n.制造业者structure chart结构图dialect n.方言，语调the program flow程序流expense n.费用，代价manager module管理模块uniformity n.同样，划一worder module工作模块archaic a.己废的，古老的mainmodule主模块sufficient a.充分的，足够的submodule子模块data processing数据处理modify v.修正，修改business application商业应用outline n.轮廓，概要scientific application科学应用compose分解lexical a.字典的，词汇的code 代码non-programmer n.非编程人员node vt改为密码notation n.记号法，表示法，注释pseudocode n.伪代码verbosity n.唠叨，冗长commas n.逗点逗号record n.记录documentation文档subrecord n.子记录flowchart/flow程表／流程data division数据部visual a.视觉的procedure division过程部represent vt.表现，表示，代表comprise vt.包含构成structured techniques结构化技术operator n.运算符，算子straightforward a.笔直的，率直的commercial package商业软件包subroutine n.子程序generator n.产生器，生产者driver module驱动模块mathematician n.专家line by line逐行operator n.作符translate vt.翻译，解释forerunner n.先驱modular adj摸块化ancestor n.祖宗cumbersome a.讨厌的，麻烦的teaching programming编程教学lengthy a.冗长的，漫长的alter vi./vt.改变flaw n.缺点裂纹devclop vt.发达separate a.各别的recompile v.编译assist n.帮助cycle n.循环technician n.技师remove vt.移动，除去straight line直线category n.种类，类项rectangle n.长方形,矩形P-code p代码virtrally ad.事实上symology n.象征学象征的使用register n.寄存器to summaries总之，总而言之by convention按照惯例cyptic n.含义模糊的，隐藏的diamond-shaped a,菱形的bracket n.括号decision n判断obviate除去，排除terminal n. a终端机，终端的keyword n.关键字card reader阅读器underline vt.下划线translator program译程序monadic a. monad(单位)的Programming程序设计dec/binary n.二进制source language源语shift变化，转移，移位machine language机器overflow n.溢出machine instruction机器指令arithmetic n.算术，算法computer language计算机语composite symbol复合型符号．assembly language汇编语assignment n.赋值floating point number浮点数proliferation n.增服high-level language高级语pointer n.指针natural language自然语言array n.数组矩阵，source text源文本subscript n.下标intermediate language中间语言type conversion类型转换software development软件开发address arithmetic地址运算map vt.映射，计划denote vt.指示，表示maintenance cost维护费用subprogram n.子程序legibility n.易读性，易识别separate compilation分离式编泽amend vt.修正，改善alphabetic a.照字母次序的consumer n.消费者digit n.数字位数enormous a.巨大的，庞大的numeric expression数值表达式reliability n.可信赖性，可信度tap n.轻打，轻敲，选择safety n.安全，安全设备print zone打印区property n.财产，所有权column n.列correctness n.正确，functionality n.机能semicolon n.分号portable a.叮携带的，可搬运的survey n.概观．altoggle n.肘节开关task n.作，任务declaration n.宣告说明source program源程序mufti-dimension array多维数组object program目标程序电脑显示器词汇大全ASIC: Application SpecificIntegrated Circuit（特殊应用积体电路）ASC（Auto-Sizing andCentering，自动调效屏幕尺寸和中心位置）ASC（Anti Static Coatings，防静电涂层）AGAS（Anti Glare Anti StaticCoatings，防强光、防静电涂层）BLA: Bearn Landing Area（电子束落区）BMC（Black Matrix Screen，超黑矩阵屏幕）CRC: Cyclical Redundancy Check（循环冗余检查）CRT（Cathode Ray Tube，阴极射线管）DDC：Display Data Channel，显示数据通道DEC（Direct Etching Coatings，表面蚀刻涂层）DFL（Dynamic Focus Lens，动态聚焦）DFS（Digital Flex Scan，数字伸缩扫描）DIC: Digital Image Control（数字图像控制）Digital Multiscan II（数字式智能多频追踪）DLP（digital Light Processing，数字光处理）DOSD: Digital On Screen Display（同屏数字化显示）DPMS（Display Power Management Signalling，显示能源管理信号）Dot Pitch（点距）DQL（Dynamic Quadrapole Lens，动态四极镜）DSP（Digital Signal Processing，数字信号处理）EFEAL（Extended Field Elliptical Aperture Lens，可扩展扫描椭圆孔镜头）FRC: Frame Rate Control（帧比率控制）HVD（High VoltageDifferential，高分差动）LCD（liquid crystal display，液晶显示屏）LCOS: Liquid Crystal OnSilicon（硅上液晶）LED（light emitting diode，光学二级管）L-SAGIC（Low Power-SmallAperture G1 wiht ImpregnatedCathode，低电压光圈阴极管）LVD（Low VoltageDifferential，低分差动）LVDS: Low VoltageDifferential Signal（低电压差动信号）MALS（Multi AstigmatismLens System，多重散光聚焦系统）MDA（Monochrome Adapter，单色设备）MS: Magnetic Sensors（磁场感应器）Porous Tungsten（活性钨）RSDS: Reduced SwingDifferential Signal（小幅度摆动差动信号）SC（Screen Coatings，屏幕涂层）Single Ended（单终结）Shadow Mask（阴罩式）TDT（Timeing DetectionTable，数据测定表）TICRG: TungstenImpregnated Cathode Ray Gun（钨传输阴级射线枪）TFT（thin film transistor，薄膜晶体管）UCC（Ultra Clear Coatings，超清晰涂层）VAGP: Variable AperatureGrille Pitch（可变间距光栅）VBI: Vertical Blanking Interval（垂直空白间隙）VDT（Video DisplayTerminals，视频显示终端）VRR: Vertical Refresh Rate（垂直扫描频率）机新词汇bluetooth：蓝牙技术（无线耳机接听）Wi-Fi：wireless Fidelity 无线保真（即“小灵通”所采用的技术）Hi-Fi：High Fidelity 高保真3-G：Generation Three 第三代PHS：Personal HandyphoneSystem 个人手提移动电话系统Walkie-Talkie：步话机Gotone：全球通------这个应该太熟悉不过了吧GPS：Global PositioningSystem 全球定位系统Monternet：Mobile+Internet移动梦网GPRS：General Packet Radio Service 通用分组无线业务--这个很重要哦，要掌握SMS：Short Message Service 短信服务---------最最流行的serviceMMS：Multi-media Messaging Service 多媒体信息服务SIM卡：Subscriber Identity Module 客户身份识别卡-------现在知道SIM的全称是什么了吧GSM：Global System For Mobile Communications 全球移动通信系统WAP：Wireless Application Protocol 无线应用协议（即使手机具有上网功能）PAS：Personal Access System 个人接入系统（如“小灵通”）CDMA：Code Division Multiple Access 码多分址-----------超级重点哦pre-paid Phone Card：储值卡Roaming：漫游Voice Prompt：语音提示WLANs：Wireless Local Area Networks 无线局域网DV：Digital Video 数码摄像机3-D：Three-Dimension 三维LCD：Liquid Crystal Display 液晶显示计算机英语词汇---计算机基础知识computer n.电脑，电子计算机arithmetic logic unit算术逻辑部件manipulate vt.操纵，操作keyboard n.键盘information n.消息，知识printer n.打印机hand-hold a.使携，手拿的skitter n.磁盘calculator n.计算器statistical a统计的system n.系统，体系joystick n.游戏棒，操纵杆scientific a.科学的，系统的software n.软件electronic a.电子的category n.种类machinery a.机器，机关，simulate n.模拟，模仿equipment n.装备，设备handle vt.控制dull a.单调的，呆滞的interpret vt.解释network n.网络feedback n.反馈circuit n.电路，一圈，巡回instrument n.工具switch n.开关，电闸manufacture vt.制造level n.水平，标准CAD计算机辅助设计status n.状态engineer n.工程师binary a.二进位的draft n.草稿store vt.储存，储藏graphics n.图形process n.程序，过程video n.影像character n.字符robotic a./n机器人学sound n.声音automation n.自动化image n.影像，图像word processing字处理programme n.程序，计划text n.文衣logic inference逻辑推理communication n.通讯aid vt.帮助，援助electronic-mail电子邮件instruction n.指令teleconferencing电话会议convert vt.转变telccommunicating远程通讯originality n.创造力database n.数据库operate vt.操作，运转CAI计算机辅助教学ENIAC电子数值积分计算机transistor n.晶体管vacuum真空DOS磁盘操作系统resistor n.电阻器RAM随机存取存储器capacitor n.电容器mouse n.鼠标interference n.干预intense妙n.强烈，紧张technology n.技术floppy a.松软的internal a.内部的fix a.牢固的symbolic n.代号write-protect写保护language n.语言drive n.驱动器span vt.跨越mechanics n.机械学reliable a.可靠的access vt.访问efficient a一有效率的byte n.比特magnetic a.一有磁性的、mega n.兆Auxiliary a./n.附加的，辅助物decimal n.十进制media n.媒体octal n.八进制storage n.存储器headecimal n.十六进制punched card tape n.磁带weight n.权memory n.记忆，存储code n.代码silicon n.硅，硅元素ASCII美国信息交换标准代chip n.芯片extended a.扩充的，长期的terminal n.终端机，终点，总站voltage n.伏特，device n.设备integer n.整数innovation n.改革，创新negative a.负的external a.外部的absence a.缺席feature n.特征convenience n.便利component n.元件，组件waveform n.波形combination n.联合，合并zone n.区microprocessor n.微处理器vendor n.厂商，自动售货机packed a.包装的implement n.工具，器具package n.包裹，套装软件quantity n.数量digital a.数字的rigid n.硬的analog a.模拟的fragile a.易脆的hybrid a.混合的susceptible a.易受影响的discrete a.离散的medium n.媒体Vital a.重要的，关键的shutter n.快门monitor n.显示器general-purpose通用overwhelm v.制服theory proving定理证明application n.应用information retrieval 信息检索wire n.电线，电报persona computer个人计算机model n.模型time-consuming a.费时的Versatility n.多种变化，变通routine task日常工作lump vt.使成块logical decision逻辑判断hardware n.硬件programmable a.可编程的stream n.流rewire vt.重新接线resource n.资源generation n.代desktop n.桌面unreliable a.不可靠的cabinet n.文件柜auxiliary storge 辅助存储器supercomputer n.超级计算机minicomputer n.小型计算机I/0 device输入／输出设备system unit系统部件cell n.单元floppy disk软盘consecutively a.连续的，连贯的fix disk硬盘CPU中央处理器transmission n.传送，传输计算机英语词汇---计算机新科学与新技术Outgrowth n.自然的发展，副产物compute vt.vi.n.计算Encompass vt.包含，包围diagnosis n.诊断Predictability a.可预言的prescription n.处方，命令，指示Object n.对象fuzzy a.模糊的，失真的Potential n.潜在性a.有潜力的voice-activated a.声音激活的Narrower n.较狭窄的部分accuracy n.精确，正确object-oriented面向对象的assumption n.假定，视为当然之事guidelines n.指导方针heuristic n.启发式教育法encapsulation n.封装性interview n.面谈访问接见subtyping n.子类型，次类型procedures n.程序generic a.一般的service-oriented a.服务导向的prolong v.延长preliminary n．初步行动，准备mature a.成熟的，充分考虑的molecular a.分子的，由分子组成coexistence n.共存，两立，并立spectrograph n.光谱摄制仪，摄谱仪non-object-oriented非面向对象的mainstream n.主流CASE 计算机辅助软件工程robot n.机械人，自动机械waterfall n.瀑布adaptable a.可修改的systematic a.有系统的，分类的broader a.宽广的，辽阔的detail n.细节，详情promote vt.促进升迁paradigm n.范例，模范transform vt.转换，改变undoubtedly ad.无疑地，确实地unpredictable a.不可预知的embed v.嵌入assembly n.集会，装配presumably ad.推测上，大概地shipping n.装运，航行explicitly a.外在的,清楚的multimedia n.多媒体patience n.耐性，忍耐designer n.设计者span n.跨距，径距,广度artificial a.人造的，武断的blunder n.大错，大失策approaches n.接近，门径disastrous a.损失惨重的，悲伤的document n.文件，公文vital a.重要的，生命的commercially a.商业的，商用的trillion n.百万的平方alignment n.结盟，队列yield n.生产量，投资收益domain n.领域，领土gain n.增益，获得bonding n.会接，搭接broaden vi.变宽motivate vt.给与动机，刺激audio a.成音频率的，声音的presumably ad.推测上，假定上automata n.自动操作，自动控制spectrograph n.质谱仪，摄谱仪abstract a.抽象的，深奥的virtual n．虚拟idealize vt.使理想化artificial intelligence人工智能symbols n.符号administration n.行政管理strings n.字符串autoscan v.自动扫描non-negative a.非负的packaging n.包装partial a.部分的，偏爱的adhere vi.依附，粘着alphabet n.字母etiquette n.礼仪，礼节，成规subset n.子集fashion n.流行，风尚，时样unique a.独一无二的，独特的dizzy a.晕眩的眼花缭乱的denote vt.指示，表示mute vt.减弱的声音roughly ad.概略地，粗糙地broadcaster n.播送者halting a.跋的，蹒跚的shipping n.运输bin n.DOS文件计算机英语高级词汇---基础篇PC（Personal Computer，个人计算机）IBM（International Business Machine，美国国际商用机器公司简称，最早的个人计算机品牌）Intel（美国英特尔公司，以生产CPU芯片著称）Pentium（Intel公司，X86 CPU 芯片，中文译名为“奔腾”）IT（Information T echnology，信息产业）E-Commerce Eelectronic Business（电子商务）B2C（Business To Customer，商家对顾客, 电子商务的一种模式，还有B2C、C2C模式）Y2K（2k year，两千年问题，千年虫）IC（Integrate Circuit，集成电路）VLSI（Very Large Scale Integration，超大规模集成电路）DIY（Do It Yourself，自己装配计算机）Bit（比特，一个二进制位，通信常用的单位）Byte（字节，由八个二进制位组成，是计算机中表示存储空间的最基本容量单位）K（千，存储空间的容量单位, kilobyte，1K=1024字节）M（兆，megabyte，1M=1024K）G（吉，gigabyte，1G=1024M）T（太，1T=1024G）Binary（二进制，计算机中用的记数制，有0、1两个数字）ASCII（American StandardCode for InformationInterchange，美国信息交换标准代码，成为了一个为世界计算机使用的通用标准）CAI（Computer-AssistedInstruction，计算机辅助教学）CAD（Computer-AidedDesign，计算机辅助设计）CAM（Computer-AidedManufacturing，计算机辅助制造）AI（Artificial Intelligence，人工智能）Program（程序，由控制计算机运行的指令组成）Driver（驱动程序或驱动器）Compatibility（兼容，指电脑的通用性）PnP（Plug and Play，即插既用，指计算机器件一装上就可以用）Hardware（硬件，构成计算机的器件）Software（软件，计算机上运行的程序）Courseware（课件，用于教学的软件）计算机英语词汇：硬件基础microelectronics n.微电子学adaptively a.适合的，适应的actuator n.主动器compensate偿还，补偿integrated a.集成的parasitic a.寄生的arithmetic n.算术,算法wobble n.摆动，不稳定crossroads n.交又路focal a.焦点的，在焦点上的ROM n.只读存储器eliminate Vt.排除，除去RAM n.随机存取存储器cornstalk n.串音permanently ad.永久的，不变的affinity n.密切关系，强烈的吸引Volatile a.可变的，不稳定的stem n．柄，堵塞物notepad n.记事本introspection n.内省，反省microprocessor n.微处理器mechanism n.机械，机理gateway n.门，通路portability n.一携带，轻便coprocessor n.协处理器configuration n.配置floating-point浮点flexibility n.适应性，弹性upgrade V.使升级algorithms n.运算法则optional a.选择的，随意的channel n．通道，频道bi-directional a.双向性keystroke n.键击simultaneous a.同时发生的typematic a.重复击键的cache n.高速缓冲存储器comprise Vi.包含，构成percentage n.百分比,部分precommendation n.预补偿controller n.控制器track n.磁轨intercept n.截取，妨碍boot v.启动significantly ad.重要地，有效地benchmark n.基准，评效migration n.移往，移动merit n.优点，价值compact a.紧凑的，紧密的restriction n.限制，限定，约束digitally n.数位intrinsic a.本质的，原有的dip n.双排直插封装Boolean n.布尔逻辑，布尔值distortion n.扭曲，变形imperative a.命令式的playback n.重现，录音再生nontrivial a.不平常的robustness a.健康的，强健的circumvent v.绕行，陷害reliability n.可靠性，可信赖性decentralize vt.使分散，排除集中resolvability n.可移动性intelligent a.智能的，聪明的counterpart n.副本，配对物automatically a.自动地，机械地archival a.关于档案的innovation n.改革，创新magneto n.磁发电机synonym n.同义字cylinder n.柱面prototype n.原型photodetector n.光感测器paradigm n.范例，模范predefined n.预先确定microchip n.微处理器split a.分散的core n.争论的核心tradeoff n.交换，协定extended memory扩充内存bootdevice引导设备picture processing图像处理reside vi.住，居留，属于sensor n.传感器optical disk光盘WS1晶片规模集成laser n.激光VLSI超大规模集成storage densities存储密度hiss n.嘶嘶声modulate vi.调整，调制unveil vt.揭开,揭幕multiassociative processing多关联处理技术workload n.工作负荷。
光学手术导航系统用于颅内和ENT手术ISO13485说明书
1. Surgical planning optimization
The Excelim-04 surgical navigation can present 2D tomographic patient image and 3D visualization of anatomical structure simultaneously. With navigation probe, operators can conveniently select any two points in the 2D tomographic pictures ( sagittal/coronal/axial) and measure the distance between them.
To m o g r a p h i c i m a g e s i n D I C O M a n d c a p t u r e d w i t h CT/C-arm/MRI/fMRI all are applicable in Excelim-04 surgical navigation system.
The intelligent software will help calibrate and compensate for unexpected anatomical-structure change and brain shift induced by removal of intracranial lesion area.
Surgical Application
Excelim-04 optical surgical navigation system can be used for all neurological and ENT surgeries,especially
城市规划专业英语词汇
城市规划专业英语词汇unban planning 城市规划town planning 城镇规划act of urban planning 城市规划法urban comprehensive planning 城市总体规划urban detailed planning 城市详细规划Residentiral district detailed planning 修建性详规regulatory detailed planning 控制性详规规划类的专业课程reginal planning 区域规划urban system planning 城镇体系规划urban sociology 城市社会学urban economic 城市经济学urban geograghy 城市地理学urban infrastructure planning 城市基础设施规划(water supply and drainage \electricity supply\road building)（城市供水、供电、道路修建）urban road system and transportation planning 城市道路系统和交通规划urban road cross-section 城市道路横断面RS=remote sensing 遥感Gardening==Landscape architecture 园林=营造景观学Urban landscape planning and design 城市景观规划和设计Urban green space system planning 城市绿地系统规划Urban design 城市设计Land-use planning 土地利用规划The cultural and historic planning 历史文化名城Protection planning 保护规划Urbanization 城市化Suburbanization 郊区化Public participation 公众参与Sustainable development(sustainability) 可持续性发展（可持续性）Over-all urban layout 城市整体布局Pedestrian crossing 人行横道Human scale 人体尺寸(sculpture fountain tea bar) （雕塑、喷泉、茶吧）Traffic and parking 交通与停车Landscape node 景观节点Brief history of urban planningArchaeological 考古学的Habitat 住处Aesthetics 美学Geometrical 几何学的Moat 护城河Vehicles 车辆，交通工具,mechanization 机械化merchant-trader 商人阶级urban elements 城市要素plazas 广场malls 林荫道The city and region Adaptable 适应性强的Organic entity 有机体Department stores 百货商店Opera 歌剧院Symphony 交响乐团Cathedrals 教堂Density 密度CapacityCirculation 循环Elimination of water 水处理措施In three dimensional form 三维的Condemn 谴责Rural area 农村地区Regional planning agencies 区域规划机构Service-oriented 以服务为宗旨的Frame of reference 参考标准Distribute 分类Water area 水域Alteration 变更Inhabitants 居民Motorway 高速公路Update 改造论文写作Abstract 摘要Key words 关键词Reference 参考资料Urban problemDimension 大小Descendant 子孙，后代Luxury 奢侈Dwelling 住所Edifices 建筑群<Athens Charter>雅典宪章Residence 居住Employment 工作Recreation 休憩Transportation交通Swallow 吞咽，燕子Urban fringes 城市边缘Anti- 前缀，反对……的；如：antinuclear反核的anticlockwise逆时针的Pro- 前缀，支持，同意……的；如：pro-American 亲美的pro-ed ucation重教育的Grant 助学金，基金Sewage 污水Sewer 污水管Sewage treatment plant 污水处理厂Brain drain 人才流失Drainage area 汇水面积Traffic flow 交通量Traffic concentration 交通密度Traffic control 交通管制Traffic bottleneck 交通瓶颈地段Traffic island 交通岛（转盘）Traffic point city 交通枢纽城市Train-make-up 编组站Urban redevelopment 旧城改造Urban revitalization 城市复苏Urban FunctionUrban fabric 城市结构Urban form 城市形体Warehouse 仓库Material processing center 原料加工中心Religious edifices 宗教建筑Correctional institution 教养院Transportation interface 交通分界面CBD=central business district 城市中心商业区Public agencies of parking 停车公共管理机构Energy conservation 节能Individual building 单一建筑Mega-structures 大型建筑Mega- 大，百万，强Megalopolis 特大城市Megaton 百万吨R residence 居住用地黄色C commercial 商业用地红色M manufacture 工业用地紫褐色W warehouse 仓储用地紫色T transportation 交通用地蓝灰色S square 道路广场用地留白处理U utilities 市政公共设施用地接近蓝灰色G green space 绿地绿色P particular 特殊用地E 水域及其他用地（除E外，其他合为城市建设用地）Corporate 公司的，法人的Corporation 公司企业Accessibility 可达性；易接近Service radius 服务半径Urban landscapeTopography 地形图Well-matched 相匹配Ill-matchedVisual landscape 视觉景观Visual environment 视觉环境Visual landscape capacity 视觉景观容量Tour industry 旅游业Service industry 服务业Relief road 辅助道路Rural population 城镇居民Roofline 屋顶轮廓线风景园林四大要素：landscape plantarchitecture/buildingtopographywaterUrban designNature reserve 自然保护区Civic enterprise 市政企业Artery 动脉，干道，大道Land developer 土地开发商Broad thorough-fare 主干道Water supply and drainageA water supply for a town 城市给水系统Storage reservoir 水库，蓄水库Distribution reservoir 水库，配水库Distribution pipes 配水管网Water engineer 给水工程师Distribution system 配水系统Catchment area 汇水面积Open channel 明渠Sewerage system 污水系统，排污体制Separate 分流制Combined 合流制Rainfall 降水Domestic waste 生活污水Industrical waste 工业污水Stream flow 河流流量Runoff 径流Treatment plant 处理厂Sub-main 次干管Branch sewer 支管City water department 城市供水部门UrbanizationSpatial structure 空间转移Labor force 劳动力Renewable 可再生* Biosphere 生物圈Planned citiesBlueprints 蓝图License 执照，许可证Minerals 矿物Hydroelectric power source 水利资源Monuments 纪念物High-rise apartment 高层建筑物Lawn 草地Pavement 人行道Sidewalk 人行道Winding street 曲折的路A view of VeniceMetropolis 都市Construction work 市政建设Slums 平民窟Alleys 大街小巷Populate 居住Gothic 哥特式Renaissance 文艺复兴式Baroque 巴洛克式land allocation拨地Land and Building Advisory Committee [LBAC]土地及建设谘询委员会land assembly汇集土地；征集土地land bank土地储备；土地备用区land classification土地分类；土地分等land cost土地成本land development土地发展Land Development Corporation [LDC]土地发展公司〔土发公司〕Land Development Corporation Managing Board土地发展公司管理局Land Development Corporation Ordinance [Cap. 15]《土地发展公司条例》〔第15章〕land disposal批地land disposal programme批地计划land drainage and flood path system土地排水及防洪道系统Land Drainage Ordinance [Cap. 446]《土地排水条例》〔第446章〕land extensive industry广占土地的工业land form地形land formation土地平整；土地开拓land freight transport陆上货运land grant批地land holding consolidation土地业权收集land index土地指数Land Information System [LIS]土地信息系统land intensive industry土地集约工业land law土地法land lease批地契约；土地契约land levelling土地平整land management土地管理land owner土地拥有人；土地业权人；地主land ownership土地拥有权；土地业权land policy土地政策land premium地价；土地补价land production增辟土地land readjustment土地规划调整land reclamation填海辟地Land Record土地记录land registration土地注册Land Registration Ordinance [Cap. 128]《土地注册条例》〔第12 8章〕land resource土地资源land resumption收回土地land revenue土地收益land right土地权land sales programme售地计划land status土地类别；土地性质Land Sub-committee [Land and Building Advisory Committee]土地小组委员会〔土地及建设谘询委员会〕land supply土地供应land surveying土地测量land tenure土地年期；土地批租期；土地租用权；土地保有权land transaction土地交易land transport陆上运输land use土地用途land use classification土地用途分类land use control土地用途管制land use performance土地用途效能land use plan土地用途图则；土地用途计划land use survey土地用途调查Land Use Transport Optimization Model [LUTO]土地及运输最佳配合模式land use zoning土地用途地带；土地用途地带区划land valuation土地估价land value地价landed property地产landfill堆填区；垃圾堆填区landlord业主；地主；房东landmark地界标志；地志Lands Tribunal土地审裁处Lands Tribunal Ordinance [Cap. 17]《土地审裁处条例》〔第17章〕landscape景观；风景；园景landscape appraisal景观评估landscape architecture景观建筑学；园林建筑学；园景设计学landscape buffer园景缓冲区landscape conservation area景观保育区landscape mounding景观土丘landscape plan景观设计图landscape planning景观规划landscape protection area景观保护区；风景保护区landscape reinstatement景观重整；园景修复landscape strategy景观策略landscape value景观价值landscaped area景观美化地方；园景美化地方landscaping景观美化；环境美化landscaping proposal美化环境计划书landside非禁区〔机场〕landslide山泥倾泻landslip山泥倾泻lane行车线；车道；小巷Lantau Link青屿干线Lantau Port and Western Harbour Development Studies大屿山港口及西部海港发展研究Lantau Port and Western Harbour Development Studies Final Report--Executive Summary《大屿山港口及西部海港发展研究最后报告──摘要》Lantau Port Development--Stage 1, Container Terminals 10 a nd 11 Ancillary Works (Design) Study大屿山港口发展──第一期工程十号及十一号货柜码头附属工程（设计）研究Lantau Port Development--Stage 1, Container Terminals 10 a nd 11 (Preliminary Design) Study大屿山港口发展──第一期工程十号及十一号货柜码头（初步设计）研究large site reduction factor大型地盘折减因素latrine厕所launderette自助洗衣店laundry洗衣店；洗衣房lay-by避车处；路旁停车处；停车湾layout布局设计；设计；规划图layout area蓝图区；详细规划区layout plan发展蓝图；详细蓝图leachate treatment works渗滤污水处理厂lead time筹建时间lease批约；租约；租契；契约lease conditions批约条件；契约条件；批地条件；租赁条件；批约条款lease enforcement强制执行批约条款lease modification契约修订lease modification premium契约修订补价lease restriction契约限制lease term契约年期；租赁年期leased area批租地区leased land已批租土地leasehold按租约而持有业权legend图例lessee承租人；租户lessor批租人；出租人Letter "A"甲种换地权益书Letter "B"乙种换地权益书letter of intent意向书letter of modification建筑牌照规约修订书；契约修订书；批地条款修订书level crossing平交道口；铁路公路交叉点level of confidence置信程度level of significance显著水平library图书馆lifeguard tower救生员了望塔light industrial area轻工业区light industry轻工业Light Rail Scheme reserve轻便铁路计划专用范围Light Rail System轻便铁路系统Light Rail Transit [LRT]轻便铁路〔轻铁〕Light Rail Transit reserve轻便铁路专用范围Light Rail Transit terminus轻便铁路总站light traffic交通稀疏light well天井light-controlled junction灯号控制的路口lighter趸船；驳船limited access road限制出入的通道；限制出入的通路linear analysis图线分析linear block相连长形大厦linear city带形城市linear correlation线性相关linear development线状发展linear programming线性规划linear regression线性回归link连接部分；连接线link road连接路linked development相关发展linked project相关计划；相关工程linked signal system联动式交通灯系统linked site相关地盘livability适居程度livestock upgrading area禽畜业发展改善区livestock waste treatment禽畜废物处理living density居住密度living quarters住所living quarters frame屋宇单位记录库living quarters size住所面积load bearing负荷；承重load factor负荷率loading/unloading area上落客货区loading/unloading bay上落客货处loading/unloading facility上落客货设施local access road区内通道local centre地区中心；乡区中心local development value地区性发展价值local distributor地区干路local open space邻舍休憩用地local plan地区规划图local public works地区性小工程；乡村工程local traffic地区交通；区内交通locality地区；地点location plan位置图location theory区位论；位置理论locational requirement位置需求lodging house旅馆Long Term Housing Strategy长远房屋策略Long Term Road Study长期道路研究longitudinal profile纵断面图longitudinal section纵剖面；纵切面long-term development长远发展long-term planning长远规划lookout area观景区lookout pavilion观景亭lookout point观景处；观景台loop road回旋路；环路lorry and car parking货车及汽车停放处lot地段lot amalgamation地段合并lot boundary地段界线lot number地段编号lot section地段分段low tide低潮low-density residential development低密度住宅发展lower catchment area下段集水区lowland低地lowland rural area低地乡郊地区low-rise building矮楼宇；层数较少的楼宇low-rise development低层建筑lump sum contract整笔付款合约MMa Wan Feasibility Study马湾发展可行性研究macro-analysis宏观分析magistracy裁判法院main elevation主立视面maintenance 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gardening种植商品果菜market rent市值租金；市面租金market stall街市档位market town墟镇；市镇market value市价；市值marsh沼泽marshalling yard调车场；编组场mart市场；贸易中心；交易会mass transit line集体运输路线Mass Transit Railway [MTR]地下铁路〔地铁〕Mass Transit Railway concourse地下铁路车站大堂Mass Transit Railway depot地下铁路厂房Mass Transit Railway (Land Resumption and Related Provisio ns) Ordinance [Cap. 276]《地下铁路（收回土地及有关规定）条例》〔第276章〕Mass Transit Railway Modified Initial System地下铁路修正早期系统Mass Transit Railway tunnel地下铁路隧道Mass Transit Railway works area地下铁路工程区mass transit system集体运输系统Mass Transit vent shaft地下铁路通风塔Mass Transit vent shaft and other structures above ground l evel other than entrances地下铁路通风塔及高出路面的其他构筑物（入口除外）massage establishment按摩院master landscape plan园景设计总图master layout plan总纲发展蓝图master plan总纲规划；总纲图master scheme总纲计划material change of use实质改变用途material considerations实质考虑因素matrix矩阵matshed theatre戏棚mature tree成长树木；成材树mausoleum多层式陵墓maxicab/public light bus stand专线小巴／公共小型巴士站maximum attainable level可达到的最高水平maximum building height最高建筑物高度maximum permissible level准许的最高限度maximum population capacity最多可容纳人口数目meadow草场mean平均数mean formation level地基平均水平线；平均地基面mean household size平均家庭人数；平均住户人数mechanism机制；制度median中位数median income收入中位数medical laboratory医疗化验室medium density中等密度megalopolis大都会memorial park纪念公园memorial stone纪念碑mental hospital精神病院merging intersection汇点merging lane合流车道merging traffic合流交通meter room电表房methane沼气metre above Principal Datum [mPd]主水平基准以上……米metro area都会区Metro District Planning Division [Planning Department]都会区规划部〔规划署〕Metro Group Section [Planning Department]都会组〔规划署〕Metro Planning Committee [MPC] [Town Planning Board]都会计划小组委员会〔城市规划委员会〕Metroplan都会计划Metroplan Study都会计划研究metropolis都会metropolitan area都会区mezzanine阁楼micro-analysis微观分析mid-stream operation中流作业migration迁移military area军事地区military camp军营military land军事用地military use军事用途mine矿场minibus小型巴士mining and quarrying采矿及采石业mini-soccer pitch小型足球场minor road次级道路minor supply gathering ground小水量集水区mitigation measure纾缓措施mixed rental/HOS estate租住公屋及居屋混合式屋mixed use building混合用途楼宇mixed woodland混合林地moat护城河；城壕mobile clinic流动诊所mobile labour流动劳动力mobility流动性mock-up flat示范单位modal split各类交通工具乘客率分析mode方式；模式；众数〔统计学〕model模式；模型model flat示范单位modification修订；更改modification of lease修订契约modification of lease conditions契约条件修订modular market标准型街市monastery寺院monastery belt寺院地带Monetized Letter "B"币值化的乙种换地权益书money exchange外币兑换店monitoring监察monorail单轨铁路monument纪念性建筑物；遗址；古mooring buoy系泊浮筒；系船浮泡moratorium延期履行；延期履行权；冻结；冻结期mortality rate死亡率mortuary殓房mosque清真寺motel时租旅店；汽车酒店motor vehicle assembly plant汽车装配厂motor vehicle showroom汽车陈列室motorway高速公路moulding装饰线条mud disposal area弃土倾卸场；卸泥场mudflats泥滩multi-disciplinary涉及多种学科multi-leg intersection多线道路交汇点multi-level junction多层路口multiple ownership共有业权multiple regression analysis复回归分析multi-purpose building多用途楼宇multi-purpose terminal多用途码头multi-service centre for the elderly老人服务中心multi-storey block多层大厦multi-storey building多层大厦multi-storey car park多层停车场multi-storey car/lorry park私家车／货车多层停车场multivariate analysis多元变量分析museum博物馆music 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星载光子计数激光雷达数据森林高度及林下地形反演研究进展
第 54 卷第 11 期2023 年 11 月中南大学学报(自然科学版)Journal of Central South University (Science and Technology)V ol.54 No.11Nov. 2023星载光子计数激光雷达数据森林高度及林下地形反演研究进展李毅，朱建军，付海强，高士娟，吴可夫(中南大学 地球科学与信息物理学院，湖南 长沙，410083)摘要：森林高度是衡量森林生物量、森林生态系统碳汇的重要参数，位于森林下的地形(林下地形)是支撑国家重大基础设施建设、灾害监测的战略信息资源。
新一代星载激光雷达ICESat-2/ATLAS 采用一种多波束微脉冲的光子计数技术，以10 kHz 的重复频率对地发射激光脉冲，从而导致出现间隔为0.7 m 、光斑半径为8.5 m 的重叠光斑。
相比于ICESat-1/GLAS ，ICESat-2/ATLAS 具有更高的空间采样率以及对坡度的不敏感性，是目前反演森林高度参数和林下地形的重要手段。
本文介绍了ICESat-2/ATLAS 的主要参数指标，总结了各类误差因素对ATL08官方产品的影响，分析了各种森林区光子点云滤波方法、ICESat-2林下地形反演方法及森林高度参数反演方法的适用性及面临的主要问题，展望了ICESat-2/ATLAS 光子点云滤波、林下地形及森林高度参数反演的发展趋势及应用前景。
关键词：星载光子计数激光雷达ICESat-2；光子点云滤波；林下地形；森林高度；研究进展中图分类号：P237 文献标志码：A 文章编号：1672-7207（2023）11-4380-11Research progress on retrieving forest canopy height and sub-canopy topography from spaceborne photon-counting LiDAR dataLI Yi, ZHU Jianjun, FU Haiqiang, GAO Shijuan, WU Kefu(School of Geosciences and Info-physics, Central South University, Changsha 410083, China)Abstract: Forest height is an important parameter to measure forest biomass and carbon sink of the forest ecosystem. The topography under the forest(sub-canopy topography) is a strategic information resource supporting national infrastructure construction and disaster monitoring. The new generation space-borne lidar ICESat-2/ATLAS adopts a multi-beam micro-pulse photon counting technology for the first time, with a repetition frequency收稿日期： 2023 −01 −12； 修回日期： 2023 −03 −25基金项目(Foundation item)：国家自然科学基金资助项目(41904004，42030112，62207032)；中南大学中央高校基础科研基金资助项目(506021729) (Projects(41904004, 42030112, 62207032) supported by the National Natural Science Foundation of China; Project(506021729) supported by the Fundamental Research Funds for the Central Universities of Central South University)通信作者：朱建军，博士，教授，从事测量平差与数据处理、复数平差理论及其在InSAR/PolInSAR 中的应用研究；E-mail ：***********.cnDOI: 10.11817/j.issn.1672-7207.2023.11.016引用格式： 李毅, 朱建军, 付海强, 等. 星载光子计数激光雷达数据森林高度及林下地形反演研究进展[J]. 中南大学学报(自然科学版), 2023, 54(11): 4380−4390.Citation: LI Yi, ZHU Jianjun, FU Haiqiang, et al. Research progress on retrieving forest canopy height and sub-canopy topography from spaceborne photon-counting LiDAR data[J]. Journal of Central South University(Science and Technology), 2023, 54(11): 4380−4390.第 11 期李毅，等：星载光子计数激光雷达数据森林高度及林下地形反演研究进展of 10 kHz to the ground. Compared with ICESat-1/GLAS, ICESat-2/ATLAS has a higher spatial sampling rate and insensitivity to slope and is currently important data for inverting the forest canopy height of forest ecosystems and sub-canopy topography. Some main indicators of ICESat-2/ATLAS were introduced and the influence of various errors on ATL08 products were summarized. The applicability of various photon point cloud filtering methods sub-canopy topography inversion and forest canopy height inversion were analyzed. The research progress and application prospects on photon point cloud filtering, sub-canopy topography inversion, and forest canopy height retrieval were put forward.Key words: space borne photon-countiong LiDAR ICESat-2; photon cloud filtering; sub-canopy topography;forest height; research progress森林生态系统是地球上最大的陆地碳库之一，拥有世界3/4以上的陆地生物[1]，通过“碳汇”和“固碳”的方式调节全球范围内二氧化碳的含量[2]，控制着全球碳循环。
复合式无人机机翼设计及其自适应优化
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收稿日期: ２０２３￣１０￣２２ꎻ 修回日期: ２０２３￣１２￣２６
第一作者简介: 张威(１９９４—) 男ꎬ 博士ꎬ 主要研究方向为气动设计、 气动优化等ꎮ Ｅ￣ｍａｉｌ: ｏｐｔｚ２０２３＠ １６３.ｃｏｍ
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SITH 商品说明书
Package‘SITH’October12,2022Type PackageTitle A Spatial Model of Intra-Tumor HeterogeneityVersion1.1.0Date2021-01-03Author Phillip B.NicolMaintainer Phillip B.Nicol<**********************>Description Implements a three-dimensional stochastic model of cancer growth and mutation simi-lar to the one described in Waclaw et al.(2015)<doi:10.1038/nature14971>.Allows for interac-tive3D visualizations of the simulated tumor.Provides a comprehensive summary of the spa-tial distribution of mutants within the tumor.Contains functions which create synthetic sequenc-ing datasets from the generated tumor.License GPL(>=2)Depends R(>=3.6.0)Imports Rcpp(>=1.0.4),scatterplot3d,stats,graphics,grDevicesSuggests rgl,igraph,knitr,rmarkdown,testthatLinkingTo RcppRoxygenNote7.1.1VignetteBuilder knitrEncoding UTF-8URL https:///phillipnicol/SITHBugReports https:///phillipnicol/SITH/issuesNeedsCompilation yesRepository CRANDate/Publication2021-01-0515:10:02UTC12SITH-package R topics documented:SITH-package (2)bulkSample (3)plotSlice (5)progressionChain (5)progressionDAG_from_igraph (6)randomBulkSamples (7)randomNeedles (8)randomSingleCells (9)simulateTumor (10)singleCell (12)spatialDistribution (13)visualizeTumor (14)Index15 SITH-package Visualize and analyze intratumor heterogeneity using a spatial modelof tumor growthDescriptionThe SITH(spatial model of intratumor heterogeneity)package implements a lattice based spatial model of tumor growth and mutation.Interactive3D visualization of the tumor are possible using rgl.Additional functions for visualization and investigating the spatial distribution of mutants are provided.SITH also provides functions to simulate single cell sequencing and bulk sampling data sets from the simulated tumor.BackgroundOn-lattice models of tumor growth and mutation are computationally efficient and provide a simple setting to study how spatially constrained growth impacts intratumor heterogeneity.While this model has been studied extensively in literature(see Waclaw(2015),Chkhaidze(2019),Opasic (2019)),existing software is either not publicly available or inconvenient to use with R.The motivation for creating the SITH package was to provide a spatial simulator that is both easy to use and can be used entirely with R.The core function in the package is simulateTumor(),which wraps a C++implementation of the model into R using the Rcpp package.Once the results of the simulation are saved as an R object,SITH provides several other useful functions for studying this model.See the package vignette for more information on the model and the algorithm used.Author(s)Phillip B.NicolReferencesB.Waclaw,I.Bozic,M.Pittman,R.Hruban,B.V ogelstein and M.Nowak.A spatial model pre-dicts that dispersal and cell turnover limit intratumor heterogeneity.Nature,pages261-264,2015.https:///10.1038/nature14971.K.Chkhaidze,T.Heide,B.Werner,M.Williams,W.Huang,G.Caravagna,T.Graham,and A.Sot-toriva.Spatially constrained tumour growth affects the patterns of clonal selection and neutral driftin cancer genomic data.PLOS Computational Biology,2019.https:///10.1371/journal.pcbi.1007243.L.Opasic,D.Zhou,B.Wener,D.Dingli and A.Traulsen.How many samples are needed to infertruly clonal mutations from heterogeneous tumours?BMC Cancer,https:///10.1186/s12885-019-5597-1.Examples#Simulate tumor growthout<-simulateTumor()#3d interactive visualization using rglvisualizeTumor(out)#or see regions with lots of mutantsvisualizeTumor(out,plot.type="heat")#get a summary on the spatial dist.of mutantssp<-spatialDistribution(out)#simulate single cell sequencingScs<-randomSingleCells(tumor=out,ncells=5,fnr=0.1)#simulate bulk samplingBulks<-randomBulkSamples(tumor=out,nsamples=5)bulkSample Simulate bulk samplingDescriptionSimulate bulk sequencing data by taking a local sample from the tumor and computing the variantallele frequencies of the various mutations.UsagebulkSample(tumor,pos,cube.length=5,threshold=0.05,coverage=0)Argumentstumor A list which is the output of simulateTumor().pos The center point of the sample.cube.length The side length of the cube of cells to be sampled.threshold Only mutations with an allele frequency greater than the threshold will be in-cluded in the sample.coverage If nonzero then deep sequencing with specified coverage is performed.DetailsA local region of the tumor is sampled by constructing a cube with side length cube.length aroundthe center point pos.Each cell within the cube is sampled,and the reported quantity is variant(ormutation)allele ttice sites without cells are assumed to be normal tissue,and thus thereported MAF may be less than1.0even if the mutation is present in all cancerous cells.If coverage is non-zero then deep sequencing can be simulated.For a chosen coverage C,it isknown that the number of times the base is read follows a P ois(C)distribution(see Illumina’swebsite).Let d be the true coverage sampled from this distribution.Then the estimated V AF isdrawn from a Bin(d,p)/d distribution.Note that cube.length is required to be an odd integer(in order to have a well-defined centerpoint).ValueA data frame with1row and columns corresponding to the mutations.The entries are the mutationallele frequency.Author(s)Phillip B.NicolReferencesK.Chkhaidze,T.Heide,B.Werner,M.Williams,W.Huang,G.Caravagna,T.Graham,and A.Sot-toriva.Spatially con-strained tumour growth affects the patterns of clonal selection and neutral driftin cancer genomic data.PLOS Computational Biology,2019.https:///10.1371/journal.pcbi.1007243.Lander ES,Waterman MS.(1988)Genomic mapping byfingerprinting random clones:a mathemat-ical analysis,Genomics2(3):231-239.Examplesset.seed(116776544,kind="Mersenne-Twister",normal.kind="Inversion")out<-simulateTumor(max_pop=1000)df<-bulkSample(tumor=out,pos=c(0,0,0))plotSlice5 plotSlice2D cross section of the simulated tumorDescription2D cross section of the simulated tumor.UsageplotSlice(tumor,slice.dim="x",level=0,plot.type="normal")Argumentstumor A list which is the output of simulateTumor().slice.dim One of"x","y"or"z",which denotes the dimension which will befixed to obtain a2D cross section.level Which value will the dimension given in slice.dim befixed at?plot.type Which type of plot to draw."Normal"assigns a random rgb value to each geno-type while"heat"colors cells with more mutations red and cells with fewermutations blue.This is exactly the same as plot.type in visualizeTumor.ValueNone.Author(s)Phillip B.NicolprogressionChain Create a linear chain graph to describe the order of mutationsDescriptionA helper function for simulateTumor()which returns to the user the edge list for a linear chain.UsageprogressionChain(n)Argumentsn Number of vertices in the chain6progressionDAG_from_igraph ValueA matrix with4columns and n-1rows which will be accepted as input to simulateTumor().Author(s)Phillip B.Nicol<**********************>ExamplesG<-progressionChain(3)progressionDAG_from_igraphDefine the progression of mutations from an igraph objectDescriptionA helper function for simulateTumor()which returns to the user the edge list for a DAG which isdefined as an igraph object.UsageprogressionDAG_from_igraph(iG)ArgumentsiG An igraph object for a directed acyclic graph.ValueA matrix with4columns which contains the edges of the graph as well as the rate of crossing eachedge and the selective advantage/disadvantage obtained by crossing each edge.Author(s)Phillip B.Nicol<**********************>randomBulkSamples7 randomBulkSamples Simulate multi-region bulk samplingDescriptionSimulate bulk sequencing data by taking a local sample from the tumor and computing the variant allele frequencies of the various mutations.UsagerandomBulkSamples(tumor,nsamples,cube.length=5,threshold=0.05,coverage=0)Argumentstumor A list which is the output of simulateTumor().nsamples The number of bulk samples to take.cube.length The side length of the cube of cells to be sampled.threshold Only mutations with an allele frequency greater than the threshold will be in-cluded in the sample.coverage If nonzero then deep sequencing with specified coverage is performed.DetailsThis is the same as bulkSample(),except multiple samples are taken with random center points. ValueA data frame with nsamples rows and columns corresponding to the mutations.The entries are themutation allele frequency.Author(s)Phillip B.NicolExamplesout<-simulateTumor(max_pop=1000)df<-randomBulkSamples(tumor=out,nsamples=5,cube.length=5,threshold=0.05)8randomNeedles randomNeedles Simulatefine needle aspirationDescriptionSimulate a sampling procedure which takes afine needle through the simulated tumor and reportsthe mutation allele frequency of the sampled cells.UsagerandomNeedles(tumor,nsamples,threshold=0.05,coverage=0)Argumentstumor A list which is the output of simulateTumor().nsamples The number of samples to take.threshold Only mutations with an allele frequency greater than the threshold will be in-cluded in the sample.coverage If nonzero then deep sequencing with specified coverage is performed.DetailsThis sampling procedure is inspired by Chkhaidze et.al.(2019)and simulatesfine needle aspira-tion.A random one-dimensional cross-section of the tumor is chosen,and the cells within this crosssection are sampled,reporting mutation allele frequency.Author(s)Phillip B.NicolReferencesK.Chkhaidze,T.Heide,B.Werner,M.Williams,W.Huang,G.Caravagna,T.Graham,and A.Sot-toriva.Spatially con-strained tumour growth affects the patterns of clonal selection and neutral driftin cancer genomic data.PLOS Computational Biology,2019.https:///10.1371/journal.pcbi.1007243.Examplesout<-simulateTumor(max_pop=1000)df<-randomNeedles(tumor=out,nsamples=5)randomSingleCells9 randomSingleCells Simulate single cell sequencing dataDescriptionSimulate single cell sequencing data by random selecting cells from the tumor.UsagerandomSingleCells(tumor,ncells,fpr=0,fnr=0)Argumentstumor A list which is the output of simulateTumor().ncells The number of cells to sample.fpr The false positive ratefnr The false negative rateDetailsThe procedure is exactly the same as singleCell()except that it allows multiple cells to be se-quenced at once(chosen randomly throughout the entire tumor).ValueA data frame with sample names on the row and mutation ID on the column.A1indicates that themutation is present in the cell and a0indicates the mutation is not present.Author(s)Phillip B.Nicol<**********************>Examplesout<-simulateTumor(max_pop=1000)df<-randomSingleCells(tumor=out,ncells=5,fnr=0.1)10simulateTumor simulateTumor Spatial simulation of tumor growthDescriptionSimulate the spatial growth of a tumor with a multi-type branching process on the three-dimensional integer lattice.UsagesimulateTumor(max_pop=250000,div_rate=0.25,death_rate=0.18,mut_rate=0.01,driver_prob=0.003,selective_adv=1.05,disease_model=NULL,verbose=TRUE)Argumentsmax_pop Number of cells in the tumor.div_rate Cell division rate.death_rate Cell death rate.mut_rate Mutation rate.When a cell divides,both daughter cell acquire P ois(u)genetic alterationsdriver_prob The probability that a genetic alteration is a driver mutation.selective_adv The selective advantage conferred to a driver mutation.A cell with k driver mutations is given birth rate bs k.disease_model Edge list for a directed acyclic graph describing possible transitions between states.See progressionChain()for an example of a valid input matrix.verbose Whether or not to print simulation details to the R console.DetailsThe model is based upon Waclaw et.al.(2015),although the simulation algorithm used is different.A growth of a cancerous tumor is modeled using an exponential birth-death process on the three-dimensional integer lattice.Each cell is given a birth rate b and a death rate d such that the time until cell division or cell death is exponentially distributed with parameters b and d,respectively.A cell can replicate if at least one of the six sites adjacent to it is unoccupied.Each time cell replication occurs,both daughter cells receive P ois(u)genetic alterations.Each alteration is a driver mutation with some probability du.A cell with k driver mutations is given birth rate bs k.The simulation begins with a single cell at the origin at time t=0.simulateTumor11 The model is simulated using a Gillespie algorithm.See the package vignette for details on how the algorithm is implemented.ValueA list with components•cell_ids-A data frame containing the information for the simulated cells.(x,y,z)posi-tion,allele ID number(note that0is the wild-type allele),number of genetic alterations,and Euclidean distance from origin are included.•muts-A data frame consisting of the mutation ID number,the count of the mutation within the population,and the mutation allele frequency(which is the count divided by N).•phylo_tree-A data frame giving all of the information necessary to determine the order of mutations.The parent of a mutation is defined to be the most recent mutation that precedes it.Since the ID0corresponds to the initial mutation,0does not have any parents and is thus the root of the tree.•genotypes-A data frame containing the information about the mutations that make up each allele.The i-th row of this data frame corresponds to the allele ID i−1.The positive numbers in each row correspond to the IDs of the mutations present in that allele,while a-1is simplya placeholder and indicates no mutation.The count column gives the number of cells whichhave the specific allele.•color_scheme-A vector containing an assignment of a color to each allele.•drivers-A vector containing the ID numbers for the driver mutations.•time-The simulated time(in days).•params-The parameters used for the simulation.Author(s)Phillip B.Nicol<**********************>ReferencesB.Waclaw,I.Bozic,M.Pittman,R.Hruban,B.V ogelstein and M.Nowak.A spatial model predictsthat dispersal and cell turnover limit intratumor heterogeneity.Nature,pages261-264,2015.D.Gillespie.Exact stochastic simulation of coupled chemical reactions.The Journal of PhysicalChemistry,volume81,pages2340-2361,1970.Examplesout<-simulateTumor(max_pop=1000)#Take a look at mutants in order of decreasing MAFsig_muts<-out$muts[order(out$muts$MAF,decreasing=TRUE),]#Specify the disease modelout<-simulateTumor(max_pop=1000,disease_model=progressionChain(3))12singleCell singleCell Simulate single cell sequencing dataDescriptionSimulate single cell sequencing data by selecting a cell at a specified positionUsagesingleCell(tumor,pos,noise=0)Argumentstumor A list which is the output of simulateTumor().pos A vector of length3giving the(x,y,z)coordinates of the cell to sample.noise The false negative rate.DetailsThis function selects the cell at pos(error if no cell at specified position exists)and returns the list of mutations present in the cell.Due to technological artifacts,the false negative rate can be quite higher(10-20percent).To account for this,the noise parameter introduces false negatives into the data set at the specified rate.ValueA data frame with1row and columns corresponding to the mutations present in the cell.A1indicates that the mutation is detected while a0indicates the mutation is not detected.Author(s)Phillip B.Nicol<**********************>ReferencesK.Jahn,J.Kupiers and N.Beerenwinkel.Tree inference for single-cell data.Genome Biology, volume17,2016.https:///10.1186/s13059-016-0936-x.Examplesset.seed(1126490984)out<-simulateTumor(max_pop=1000)df<-singleCell(tumor=out,pos=c(0,0,0),noise=0.1)spatialDistribution13 spatialDistribution Quantify the spatial distribution of mutantsDescriptionProvides a summary the spatial distribution of mutants within the simulated tumor.UsagespatialDistribution(tumor,N=500,cutoff=0.01,make.plot=TRUE) Argumentstumor A list which is the output of simulateTumor().N The number of pairs to sample.cutoff For a plot of clone sizes,all mutations with a MAF below cutoff are ignored.make.plot Whether or not to make plots.DetailsThe genotype of a cell can be interpreted as a binary vector where the i-th component is1if mutationi is present in the cell and is0otherwise.Then a natural comparison of the similarity between twocells is the Jaccard index J(A,B)=|I(A,B)|/|U(A,B)|,where I(A,B)is the intersection ofA andB and U(A,B)is the union.This function estimates the Jaccard index as a function ofEuclidean distance between the cells by randomly sampling N pairs of cells.ValueA list with the following components•mean_mutant-A data frame with2columns giving the mean number of mutants as a function of Euclidean distance from the lattice origin(Euclid.distance rounded to nearest integer).•mean_driver-The same as mean_mutant except for driver mutations only.Will be NULL if no drivers are present in the simulated tumor.•jaccard A data frame with two columns giving mean jaccard index as a function of Euclidean distance between pairs of cells(rounded to nearest integer).Author(s)Phillip B.NicolExamplesset.seed(1126490984)out<-simulateTumor(max_pop=1000,driver_prob=0.1)sp<-spatialDistribution(tumor=out,make.plot=FALSE)14visualizeTumor visualizeTumor Interactive visualization of the simulated tumorDescriptionInteractive visualization of the simulated tumor using the rgl package(if available).UsagevisualizeTumor(tumor,plot.type="normal",background="black",axes=FALSE)Argumentstumor A list which is the output of simulateTumor().plot.type Which type of plot to draw."Normal"assigns a random rgb value to each geno-type while"heat"colors cells with more mutations red and cells with fewermutations blue.background If rgl is installed,this will set the color of the backgroundaxes Will include axes(rgl only).DetailsIf rgl is installed,then the plots will be interactive.If rgl is unavailable,static plots will be cre-ated with scatterplot3d.Since plotting performance with scatterplot3d is reduced,it is strongly recommended that rgl is installed for optimal use of this function.ValueNone.Author(s)Phillip B.NicolIndexbulkSample,3,7plotSlice,5progressionChain,5,10progressionDAG_from_igraph,6 randomBulkSamples,7randomNeedles,8randomSingleCells,9simulateTumor,2,4–9,10,12–14singleCell,9,12SITH(SITH-package),2SITH-package,2spatialDistribution,13visualizeTumor,1415。
基于模态应变能的不同损伤指标对比
基于模态应变能的不同损伤指标对比郭惠勇;盛懋【摘要】为解决工程结构的多损伤识别问题，对基于模态应变能的不同损伤指标方法进行了对比分析和研究。
首先，描述了3种损伤指标，即模态应变能变化指标( MSECI)、模态应变能耗散率指标( MSECRI)和模态应变能基指标( MSEBI)；然后借鉴模态应变能耗散率指标的建立原理，通过对刚度矩阵的修正，建立相应的能量等效方程，并提取了一种模态应变能等效指标( MSEEI )；最后对4种应变能损伤指标进行了对比研究，并考虑了测量噪声的影响。
数值仿真结果表明，模态应变能基指标可以较好地识别结构的损伤位置，模态应变能等效指标则不仅可以有效地识别结构的损伤位置，而且可以较为精确地识别结构的损伤程度。
%In order to solve the problem of structural multi-damage identification, different modal strain energy damage index methods are studied and compared. First, three kinds of modal strain energy damage indices, the modal strain energy change index, the modal strain energy dissipation ratio index, and the modal strain energy based index, are described. Then, with consideration of the modal strain energy dissipation ratio index method, an improved equivalence equation is derived through improvement of the stiffness matrix, and a modal strain energy equivalence index is proposed. Finally, four kinds of modal strain energy damage indices are compared, and the impact of the measured noise is considered. Simulation results show that the modal strain energy-based index method can identify structural damage locations, and the proposed modal strain energyequivalence index can not only identify structural damage locations but can identify the damage extent with high accuracy.【期刊名称】《河海大学学报（自然科学版）》【年(卷),期】2014(000)005【总页数】7页(P444-450)【关键词】损伤识别;模态应变能;应变能变化率;应变能耗散率;应变能基指标【作者】郭惠勇;盛懋【作者单位】重庆大学土木工程学院，重庆 400045; 重庆大学山地城镇建设与新技术教育部重点实验室，重庆 400045;重庆大学土木工程学院，重庆 400045; 重庆大学山地城镇建设与新技术教育部重点实验室，重庆 400045【正文语种】中文【中图分类】TU312+.3工程结构的损伤识别研究是国际上的研究热点[1-5]。
空间数据分析的基本方法
• Example: Spatial Objects
– points: x, y coordinates
» cities, stores, crimes, accidents
– lines: arcs, from node, to node
» road network, transmission lines
• ESDA and Spatial Econometrics
What Is Special About Spatial Data?
• Location, Location, Location
– “where” matters
• Dependence is the rule
– spatial interaction, contagion, externalities, spill-overs, copycatting – First Law of Geography (Tobler)
» examples: location of disease, gang shootings
• Research question
– interest focuses on detecting absence of spatial randomness (cluster statistics) – clustered points vs dispersed points
Point Pattern Analysis
• Objective
– assessing spatial randomness
• Interest in location itself
– complete spatial randomness – clustering, dispersion
各种多处理器上的多线程运行性能提高解决方案
各种多处理器上的多线程运行性能提高解决方案概述、本文通过对五种提高多处理器上多线程运行性能的技术的介绍，综合应用五种方案来得到一种理论上的性能提高策略。
按照存储结构的层次来划分，五种方案分述如下：在L1缓存上增加线程相关信息来避免不同线程对资源的竞争，从而提高性能；将线程重新集簇来减少芯片间存储单元的通信，即将低级缓存通信转化为高级缓存通信来提高性能；在操作系统层面上将可运行线程分组，并按组调度来提高性能；同样地，按照线程的不同特性划分复合地通过分时策略给线程们提供资源区间更可以合理地安排资源的利用；最后，划分线程的工作集的时候，精心地挑选工作集粒度将会有意外的性能提升。
1.五种方案的简介、利用线程相联的存储结构也可以提高多线程运行的性能。
这种技术还可以节省能源。
在芯片上的存储结构上添加线程信息，避免了不同线程对资源的竞争问题。
额外的开关标志位还可以控制此法的开关。
这种消除了线程间的竞争，虽然会产生一定程度的线程内竞争，但是可以加以控制。
几经权衡之下，线程间竞争的消除占了性能提高的优势。
[2]由于分散的线程产生的线程间通信造成了许多时延，将线程集簇也是一种提高性能的办法。
同核的L1缓存通信时延在1～2时钟周期左右，同芯片的L2缓存通信时延在10～20周期左右，而芯片间的L3甚至更大容量存储单元的通信则可能消耗掉数百周期以上。
此法亦是通过操作系统的调度来实现，将频繁通信的线程集簇起来，减少它们之间的芯片际通信。
通过一个硬件部分PMU（performance monitoring unit）来实现数据特征采样，这样开销也比较小。
此法一定程度上避免了缓存的容量缺失与冲突缺失。
引入另一个硬件部分HPC（hardware performance counter）[8]。
步骤如下：首先HPC来监测让性能受限的缺失停顿；接着，通过PMU实现每个线程保存一个称为shMap的向量来记录缓存访问区域；然后将拥有高度相似的shMap的线程集簇起来；最后，在集簇过程中，运用线程迁移策略。
空间面板数据分析——R的splm包
空间面板数据分析——R的splm包（任建辉，暨南大学）The splm package provides methods for fitting spatial panel data by maximum likelihood and GM.安装R软件及其编辑器Rstudio网址：/下载好Rstudio以后，操作都可以Rstudio中完成了，包括命令的编写、命令运行、图形展示，最方便的要数查看数据了。
R界面Rstudio界面，形如matlab下面进入正题，了解splm包中的数据、命令及结果展示。
所有命令都写在编辑窗口（studio 左上区域），可以单独的运行每行命令，也可选取一段一起执行，点run按钮。
1、首先，安装splm包并导入，命令如下:intall.packages(“splm”)，选择最近的下载点library(splm)> library(splm)载入需要的程辑包：MASS载入需要的程辑包：nlme载入需要的程辑包：spdep载入需要的程辑包：sp载入需要的程辑包：Matrix载入需要的程辑包：plm载入需要的程辑包：bdsmatrix载入程辑包：‘bdsmatrix’下列对象被屏蔽了from ‘package:base’:backsolve载入需要的程辑包：Formula载入需要的程辑包：sandwich载入需要的程辑包：zoo载入程辑包：‘zoo’下列对象被屏蔽了from ‘package:base’:as.Date, as.Date.numeric载入需要的程辑包：spam载入需要的程辑包：gridSpam version 0.40-0 (2013-09-11) is loaded.Type 'help( Spam)' or 'demo( spam)' for a short introductionand overview of this package.Help for individual functions is also obtained by adding thesuffix '.spam' to the function name, e.g. 'help( chol.spam)'.载入程辑包：‘spam’下列对象被屏蔽了from ‘package:bdsmatrix’:backsolve下列对象被屏蔽了from ‘package:base’:backsolve, forwardsolve载入需要的程辑包：ibdreg载入需要的程辑包：car载入需要的程辑包：lmtest载入需要的程辑包：Ecdat载入程辑包：‘Ecdat’下列对象被屏蔽了from ‘package:car’:Mroz下列对象被屏蔽了from ‘package:nlme’:Gasoline下列对象被屏蔽了from ‘package:MASS’:SP500下列对象被屏蔽了from ‘package:datasets’:Orange载入需要的程辑包：maxLik载入需要的程辑包：miscToolsPlease cite the 'maxLik' package as:Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelih ood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s001 80-010-0217-1.If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:https:///projects/maxlik/Warning message:程辑包‘Matrix’是用R版本3.0.3 来建造的注意：在导入splm时，如果发现还有其他配套的包没有安装，需要先安装。
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Software data and modelling newsA spatial sampling optimization package using MSN theoryMao-Gui Hu,Jin-Feng Wang *State Key Laboratory of Resources &Environmental Information System,Institute of Geographical Science and Natural Resources Research,Chinese Academy of Sciences,A11,Datun Road,Beijing 100101,Chinaa r t i c l e i n f oArticle history:Received 10April 2010Received in revised form 21August 2010Accepted 6October 2010Available online xxx Keywords:Spatial samplingNonhomogeneous surface Variance of estimationParticle swarm optimizationa b s t r a c tThe density and distribution of spatial samples heavily affect the precision and reliability of estimated population attributes.An optimization method based on Mean of Surface with Nonhomogeneity (MSN)theory has been developed into a computer package with the purpose of improving accuracy in the global estimation of some spatial properties,given a spatial sample distributed over a heterogeneous surface;and in return,for a given variance of estimation,the program can export both the optimal number of sample units needed and their appropriate distribution within a speci fied research area.Ó2010Elsevier Ltd.All rights reserved.Software availabilitySoftware name:MSN Spatial Sampling Optimization Developers:Mao-Gui Hu and Jin-Feng Wang Contact:humg@Available since:December 2009Operating system:32-bit Windows Program language:C þþ,C#Availability:free from /msn 1.IntroductionSpatial sampling is one of the most basic methods of collecting information in spatial investigation and research,such as studies on natural resources,the environment,and ecosystems (Haining,2003).Dense samples usually bring high precision,while sparse ones cannot make as precise an estimate of the population (Cochran,1977).However,cost is another important factor to be considered carefully in practice.An optimized spatial sampling scheme would make a tradeoff between precision and cost.Mete-orological network design is a typical example.We will introduce and apply a spatial sampling optimization method using meteo-rological network design,but the method is not restricted to this application alone.A meteorological network collects data on a variety of meteo-rological elements that are commonly used in weather forecasting,environmental evaluation,and global climate change research,such as temperature,pressure,wind,humidity,and rainfall (Yu and Neil,1990;Pardo-Iguzquiza,1998;Hansen et al.,2006).Designing a network that provides a representative picture of the properties of the whole region under study is an important but dif ficult problem.Generally,a good meteorological network would consist of a minimal number of stations that can provide as much infor-mation as possible.Depending on the spatial correlation of obser-vation posts throughout a region,criteria based on Kriging variance are adopted by many researchers to optimize a meteorological network (Bastin et al.,1984;Gao et al.,1996;Ahmed,2004;Barca et al.,2008).To improve the accuracy of areal averages of meteo-rological elements within a covered region,the total number and distribution of observing stations can be optimized by minimizing the global estimation variance.This optimization is dynamic in that the process usually consists of adding,removing,and moving stations,and it performs well in many instances (Barca et al.,2008).When the goal is to estimate the population mean of some spatial property rather than to predict the value at unsampled locations,however,the minimization of Kriging variance methods cannot be used directly.On one hand,the Kriging method is based on the modeling assumption of surface homogeneity,which in practice is often not satis fied for extensive regions due to financial reasons or physical geography (Li et al.,2008).On the other hand,there is no quantitative relation between the precision of estimated*Corresponding author.E-mail address:wangjf@ (J.-F.Wang).Contents lists available at ScienceDirectEnvironmental Modelling &Softwarejournal homepage:w ww.elsevi er.c om/locate/envsoft1364-8152/$e see front matter Ó2010Elsevier Ltd.All rights reserved.doi:10.1016/j.envsoft.2010.10.006Environmental Modelling &Software xxx (2010)1e 3population mean and the best predicted value at some location,although the former could be affected by the latter (van Groenigen et al.,1999;Stein and Ettema,2003).Wang et al.proposed the so-called Mean of Surface with Non-homogeneity (MSN)method for those complicated surfaces so as to estimate the mean of some property and its variance (Wang et al.,2009).The method combines the merits of spatial strati fication and Kriging variance.It assumes that a nonhomogeneous surface could be converted to several small homogeneous surfaces by strati fica-tion (Wang et al.,2010),which is possible in most cases.It can estimate the population mean directly.The estimated result of MSN is proven to be the best linear unbiased estimator (BLUE)of the true value (Wang et al.,2009).In practice,a meteorological network often covers very large areas.It is very hard to guarantee that the distribution of a meteorological element follows the homogeneity assumption in the whole region.A spatial sampling optimization method based on the MSN theory would make global estimations and statistical inferences in complicated regions much more ef fi-cient and reliable.2.Software featuresA software package that performs spatial sampling optimiza-tions using MSN theory has been developed to run under the Microsoft Windows operation system.A flow chart outlining the algorithmic procedures involved is presented in Fig.1.The main goal of the package is to measure whether currently installed spatial samples within a region can meet the overall accuracy (variance of mean estimation)required by users.If not,the program determines the number of additional samples needed and where they should be located.The optimization operator is wrapped into a COM component using C þþ,and can be easily integrated into other systems.The main GUI is a basic GIS platform implemented using C#that makes it convenient for users to view the spatial data.The data format supported is ESRI Shape files,a popular format shared by most GIS software.The optimization process is completed by a wizard that consists of three pages,viz.,‘data source ’,‘set parameters ’,and ‘show results ’(Fig.1).The package is user friendly and can be understood by non-experts.The required input data include the number and location of current spatial samples (meteorological stations)and various strati fied regions.The ‘forbidden region ’file records those selected regions that are to be excluded from consideration as potential sites for samples.This option is of great practical use when regions are inaccessible for one reason or other.Variogram parameters for each stratum can either be imported from a text file or calculated according to observational data from stations.When a stratum ’s variogram parameters are known,it is then possible to allocate additional stations in the region after per-forming the optimization,and this can be done even if there were initially no stations.Another distinguishing feature of the package is its imple-mentation of a combined Monte Carlo and Particle Swarm Opti-mization (MC-PSO)algorithm to accelerate the optimization process.Finding the best sample locations from thousands of candidates is a combinational problem.It is infeasible and quite impracticable to trial each possible combination and compare them due to the number of stations involved.Given an expected esti-mated global mean standard deviation that is smaller than the current value,we can first estimate the possible maximum stations m needed with simple sampling theory.Second,a binary search method is used in the range [0,m].Finally,for a given search number,we then apply the MC-PSO algorithm to find the best candidate sites subject to minimum estimated mean variance criteria.3.Concluding remarksBecause many different factors are involved,meteorological network optimization is a dif ficult problem to resolve.The freely downloadable package we have developed for this task is the first version and addresses only the issue of improving data reliability and accuracy by ‘adding stations ’in an optimally distributionalNew added samplesFig.1.Architecture of the MSN spatial sampling optimization package.M.-G.Hu,J.-F.Wang /Environmental Modelling &Software xxx (2010)1e 32manner.It is not only useful for meteorological network optimi-zation,but also for other similar spatial sampling optimization.A newer version is being developed that will include other features with more complicated functions(Christakos,2005),such as opti-mally deleting and moving spatial samples.It will also be freely available to download from the website.AcknowledgmentsThis study was supported by CAS(KZCX2-YW-308),MOST (2009ZX10004-201;2008BAI56B00;2007DFC20180;2007AA12Z233; 2006BAK01A13),and NSFC(40471111,70571076)grants.The authors 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