Iterative linear regression by sector renormalization of cDNA microarray data and cluster a
linear regression知识点
linear regression知识点1.引言1.1 概述引言部分是文章的开头,用来介绍文章的背景和重要性。
在"概述"部分,我们可以对linear regression(线性回归)的基本概念和作用进行简单介绍。
概述:线性回归是机器学习领域中最简单且最常用的回归方法之一。
它是一种建立输入变量(自变量)和输出变量(因变量)之间线性关系的统计学模型。
线性回归可以帮助我们探索和理解数据,预测未知的因变量值,并在实际问题中做出决策。
线性回归的基本思想是基于已知的训练数据,通过拟合一条直线(或超平面)来近似描述输入和输出之间的关系。
这条直线可以用来做预测和回答各种问题。
线性回归的关键是通过最小化预测值与实际观测值之间的差距,找到最佳拟合直线。
线性回归不仅可以用于预测连续性数值型数据,还可以用于分类问题,例如将输出变量划分为两个或多个不同的类别。
尽管线性回归在实际问题中很常见,但它也有一些局限性,例如对于非线性关系的建模能力较弱。
为了克服这些局限性,研究人员还提出了各种改进方法。
本文将深入探讨线性回归的基本概念和原理,介绍线性回归模型的建立与求解过程,并探讨线性回归在实际应用中的场景和局限性,同时提出一些改进方法。
通过阅读本文,读者将能够全面了解线性回归的知识和应用,从而在实际问题中更好地应用和理解线性回归方法。
下面我们将详细介绍本文的结构和目的。
1.2 文章结构文章结构部分的内容可以描述整篇文章的组织和安排,可以按照以下内容进行阐述:在本篇文章中,我们将从引言、正文和结论三个部分来组织和阐述关于Linear Regression(线性回归)的知识点。
首先,在引言部分,我们将对线性回归进行概述,介绍其基本概念和原理。
同时,我们将阐明本篇文章的目的,即通过介绍线性回归的知识点,让读者对线性回归有一个全面的了解。
接着,在正文部分,我们将分为两个小节来详细讲解线性回归的知识点。
首先,我们将介绍线性回归的基本概念,包括线性回归的定义、特点以及模型表示等。
沈阳市勘察测绘研究院项目荣获2017年辽宁省测绘科学技术进步奖
132城市勘测2018年4月[10]杨鸿森.基于总体最小二乘的红外图像去噪[J].激光与红外,2008,38(9):961〜964.[11] Van Huffel S,Vandewalle J.Analysis and Solution of theNongeneric Total Least Squares Problem[J].Siam Jour^nalon Matrix Analysis &Applictions,1988,9(3):360〜372.[12] Lemmerling P,De Moor B,van Huffel S.On the Equivalence of Constrained Total Least Squares and Structured Total Least Squares[J].IEEE Transactions on Signal Process-ing,1996,44(11):2908〜2911.[13] Burk h ard Schaffrin,Andreas Wieser.On Weighted totalLeast-squares Adjustment for Linear Regression[J].Journal of Geodesy,2008,82:415〜421.[14] Zhang S L,Tong X H,Zhang K L,et al.A Solution to EIVModel with Inequality Constraints and its Geodetic Applications[J]. Journal of Geodesy,2013,87: 89 〜99.[15] Mahboub V.On Weighted Total Least-squares for GeodeticTransformations[J].Journal of Geodesy,2012, 86: 359 〜367.[16]陶叶青,高井祥,姚'一■飞.基于中位数法的抗差总体最小二乘估计[J].测绘学报,2016,45(3):297〜301.[17]周江文,黄幼才,杨元喜等.抗差最小二乘法[M].武汉:华中理工大学出版社,1997.Solution for Control Traverse with Additional Gyro -s ideAdjustment Based on Robust Total Least SquaresSi Yajun,He Yanlanr(Jiansu Geologic Surveying and Institute , Nanjing 211102, China)Abstract :Control network with additional gyro-side is useful for accuracy of underground traverse,traditional method for control traverse with additional gyro-side is to apply least squares estimation to compute parameters of model taking gyro azimuth and observation data of traverse as variables. However,the method based on least squares estimation can not take into account the fact that both observation vector and coefficient matrix have random error,and the work environment may lead that observation data has gross error which has influence on computing parameters of model. To overcome the shortage of traditional method,solution for control traverse based on total least squares is proposed in this paper. To take into account the condition that both observation vector and coefficient matrix of mathematical model have random er- ror,and to get over the influence that gross error existing in observation vector and coefficient matrix have,the iterative algorithm based on Lagrange function is presented to solve the problem,and Huber function is also applied to overcome the influence that gross error has on computing the parameters. At the last of paper,an instance of control traverse with gyro -side is used to demonstrate the proposed solution to be feasible,and differences of accuracy among the proposed solution and traditional algorithms are also discussed.Key words : gyroscopic orientation ; control traverse; robust estimation ; total least squares沈阳市勘察测绘研究院项目荣获2017年辽宁省测绘科学技术进步奖根据《辽宁省测绘科学技术进步奖评选办法》规定,辽宁省测绘地理信息学会开展了 2017年辽宁省测绘科学技术进 步奖评选工作。
英文单词
Synopsis:概要,大纲Macroscopic:宏观的,肉眼可见的Interconnected:连通的,有联系的Inter-dendritic:晶间Ingots:钢锭,铸块Globular:球状的Substantially:实质上,大体上,充分地Qualitatively:定性地Bulging:膨胀,凸出,打气,折皱(在连铸中是鼓肚的意思!)Hydrogen induced cracking:氢致裂纹(HIC)Correlated to:相互关联Perform:完成,执行Bulk concentration:体积浓度Introduction:引言Accordingly:因此,相应地Countermeasure:对策,对抗措施Equiaxed crystal:等轴晶Aggromerate:聚合Permeability:渗透性Slab:厚板Plate:薄板Contraction:收缩,紧缩Conventional:传统的Inconsistency:不一致Susceptibility:敏感性Resolve:解决,分解Morphology:形态Interpret:解释,解读Areal fraction:面积分数Quench:淬火Dendrite tips:枝晶尖端Specimen:试样,样品Proportional:成比例的Coarsening:晶粒粗大Coalescence:合并,联合Nevertheless:不过,虽然如此Planar:二维的,平面的Cellular:细胞的,多孔的Interface:界面,接触面Refer to :适用于Constant:常量Approximation:近似值,近似法Apparatus:仪器,装置Diagram:图表,图解Derive from:源自,来自Longitudinal:纵向的,长度的Section:截面Magnification:放大率schematic:图解的curvature:弯曲arrowed:标有箭头的in essence:本质上,其实lagged 延迟radial heat 辐射热transient:短暂的crucible:坩埚internal diam:内部直径chromel alumel thermocouple:铬镍-铝镍热电偶allumina:氧化铝agitated ice water:激冷冰水given:考虑到electropolish:用电解法抛光transverse:横向的,横断的metallographic:金相的diffusion:扩散,传播coefficient:系数,率undercooling/supercooling:过冷interdendritic:枝晶间,树枝晶间的intragranular:晶内的granular:颗粒的,粒状的isotherm:等温线arc-welded:弧焊deposit:沉积物,存款inversely:相反地geometry:几何学justification:理由,辩护,认为正当somewhat:有点gradient:梯度,倾斜度recognised:承认,辨别substitute:代替exponent:指数excluding:不包括,将。
基于内脏脂肪面积预测女性低骨量的风险切点
Chinese Journal of Tissue Engineering Research |Vol 25|No.35|December 2021|5577基于内脏脂肪面积预测女性低骨量的风险切点秦 迁1,杨 阳1,陈静锋1,王守俊2,丁素英1文题释义:定量CT :即定量X 射线计算机体层摄影技术,区别于传统CT 的特点是体模的运用,校正CT 值漂移,从而准确地测量出单位体积内的骨矿物质含量,弥补了CT 值只能代表相对骨密度的缺点,同时联合肺CT 测定,不增加辐射剂量。
生物电阻抗:文章采用生物电阻抗法测定人体体质成分,包含骨骼肌、脂肪、内脏脂肪面积等。
摘要背景:近几年肥胖尤其是腹型肥胖与骨密度的关系备受关注,有研究认为内脏脂肪面积是骨密度降低的危险因素,也有一些研究认为年龄和内脏脂肪面积及骨密度之间的关系存在交互作用。
目的:探讨人体成分各指标与骨密度的相关性,并预测低骨量的内脏脂肪面积风险切点。
方法:回顾性分析在郑州大学第一附属医院健康管理中心进行定量CT 和生物电阻抗测定的受检者的内脏脂肪面积及骨密度,采用Pearson 相关、多因素线性回归与Logistic 回归分析法分析人体成分各指标和骨密度及低骨量的相关性。
绘制受试者工作特征曲线,确定预测低骨量的内脏脂肪面积切点值。
结果与结论:①随着年龄的增加,骨密度逐渐下降,内脏脂肪面积逐渐增加,基础代谢率逐渐下降;②多因素线性回归分析显示年龄、内脏脂肪面积与骨密度呈负相关;③多因素Logistic 回归分析显示,绝经后女性内脏脂肪面积和低骨量相关;④受试者工作特征曲线预测绝经后女性低骨量的内脏脂肪面积切点值为117.85 cm 2;⑤提示内脏脂肪面积是除了年龄外预测低骨量的危险因子,有望在今后骨矿物质健康管理中发挥重要作用,应对达到内脏脂肪面积风险切点的绝经后女性早期加强减脂管理,以减少低骨量的发病率及医疗和经济负担。
关键词:低骨量;女性;骨密度;内脏脂肪面积;定量CT ;生物电阻抗缩略语:内脏脂肪面积:visceral fat area ,VFA ;受试者工作特征曲线:receiver operating characteristic curve ,ROCCut-off point of visceral fat area: predicting low bone mass in womenQin Qian 1, Yang Yang 1, Chen Jingfeng 1, Wang Shoujun 2, Ding Suying 11Health Management Center, 2Department of Endocrinology and Metabolic Diseases, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, ChinaQin Qian, Master, Physician, Health Management Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, ChinaCorresponding author: Ding Suying, Master, Chief nurse, Health Management Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, ChinaAbstractBACKGROUND: In recent years, concerns have been paid to the relationship between obesity, especially abdominal obesity, and bone mineral density. Studies have concluded that visceral fat area is a risk factor for reducing bone mineral density. There are also some studies suggesting an interaction between age, visceral fat area and bone mineral density.https:///10.12307/2021.283投稿日期:2020-07-25 送审日期:2020-07-28采用日期:2020-10-09在线日期:2021-04-02中图分类号: R459.9;R318;R683文章编号:2095-4344(2021)35-05577-05文献标识码:A郑州大学第一附属医院,1健康管理中心, 2内分泌与代谢病科,河南省郑州市 450052第一作者:秦迁,女,1989年生,河南省郑州市人,2016年郑州大学毕业,硕士,医师,主要从事骨质疏松方面的研究。
药品检验英语词汇对照学习
药品检验英语词汇对照学习药品标准质量标准标准品国际标准品参比标准对照品规格药品质量控制分析质量控制性状无臭异臭熔点熔距毛细管熔点测定热台熔点测定沸程凝点不溶性微粒颗粒细度膨胀度分子大小微晶结晶细度结晶性一般鉴别试验鉴别旋光计阿贝折射计浸入折射计限度检查有色杂质有关物质吸光度吸光系数吸光度比值澄清度氯化物硫酸盐重金属砷盐碘化物铁盐氰化物drug standard quality standard standard substance international standard substance reference standard reference substance specification drug quality control analytical quality control characteristics odorless foreign odor melting point melting range capillary melting point determi-nation hot stage melting point determi-nation boiling rangecongealing point particulate matter fineness of the particles swelling degree molecular size microcrystal crystal fineness crystallinity general identification test identification polarimeter Abbe refractometer immersion refractometer limit test foreign pigment related substance absorbance specific absorbance absorbance ratio clarity chloride sulfate heavy metal arsenic salt iodide iron salt cyanide 钡盐钙盐易碳化物易氧化物醚溶性酸性物酸中和容量热原异常毒性衰变放射性核纯度放射性浓度索氏[脂肪]抽提器丹氏浓缩器不皂化物羟值烷氧基测定范氏氨基氮测定法砷斑炽灼残渣蒸发残渣不挥发物总固体过氧化值干燥失重相溶解度分析装量差异最低装量含量测定含量均匀度溶出度崩解时限药物释放相对密度表观黏度比特性黏度运动黏度旋转黏度计落球黏度计稳定性试验总氮量比重瓶法韦氏比重秤法结冻试验抽针试验barium salt calcium salt readily carbonizable substance readily oxidizable substance ether-soluble acidic matter acid-neutralizing capacity pyrogen undue toxicity decay radionuclide purity radioactive concentration Soxhlet extractor Danish concentrator non-saponifiable matter hydroxyl value alkyloxy determination van Slyke method arsenic stain residue on ignition residue on evaporation non-volatile matter total solid peroxide value loss on drying phase solubility analysis content uniformity minimum fill assay uniformity of dosage units dissolution disintegration drug release relative density apparent viscosity specific intrinsic viscosity kinematic viscosity rotating cylinder viscometer falling sphere viscometer stability study nitrogen content pycnometric method hydrostatic method, Westphalbalancemethod freezing test consistence test 悬浮时间共沸法甲苯蒸馏法挥发油测定器滴定液标准比色液耐用性计算机辅助药物分析模式收敛特征特征选择试验集训练集校正集预示集岭回归响应面信号处理图象分析库检索变量校正多变量校正反向传播最优化方法窗口图解技术色谱响应函数色谱优化函数混合物设计统计技术叠加分辨率图示法数值分类法系统矩阵组合法双波长一元线性回归法多波长直线回归法系数倍率法最小二乘法自适应最小二乘偏最小二乘法非线性迭代偏最小二乘P矩阵法正交函数法卡尔曼滤波法suspension time azeotropic method toluene distillation method volatile oil determination apparatus volumetric solution, VS standard color solution ruggedness computational pharmaceutical analysis, computer-aided pharmaceutical analysis pattern convergence feature feature selection testset training set calibration set prediction set ridge regression response surface signal processing image analysis library searching variate calibration multivariate calibration back propagation optimization method window diagram technique chromatographic response function, CRF chromtographic optimization function, COF mixture design statistic technique overlapping resolution maps numerical taxonomy system matrix combination method dual-wavelength linear regression method multi-wavelength linear regression method K-ratio method, signal multiplier method least square method adaptive least square partial least square method nonlinear iterative partial least square P-matrix method orthogonal function method Kalman filtering method 适应卡尔曼滤波法卡尔曼增益线性学习机主成分分析主成分回归法逐步判别分析目标因子分析对应因子分析迭代目标转换因子分析法遗忘因子法特征向量投影非线性映射人工神经网络节点线性规划法共轭梯度法专家系统人工智能系统模型测量模型模型化与参数估计决策规则n维空间超平面相似性K最近邻域法快速傅里叶变换交互证实误差修正反馈法负反馈过程分析输出层输入层隐含层碘仿反应丙烯醛反应缩合反应异腈化苯N一溴丁二酰亚胺滴定法永停滴定法两相滴定剩余碱水解法催化热滴定adaptive Kalman filtering method Kalman gain linear learning machine principal component analysis, PCA principal component regression method stepwise discriminate analysis target factor analysis correspondence factor analysis iterative target transform factor analysis method forgetting factor method eigenvector projection nonlinear mapping artificial neural network node linear programming method conjugate gradientmethod expert system artificial intelligence system model measurement model modeling and parameterestimation decision rule n-dimensional space hyperplane similarity K-nearest neighbor method fast Fourier transform cross-validation error correct feed-back method negative feedback process analysis output layer input layer hidden layer iodoform reaction acrolein reaction condensation reaction phenyl isocyanide N-bromosuccinimide titration dead-stop titration diphasic titration residual basic hydrolysis method catalytic thermometric titration 非水催化热滴定提取重量法提取容量法四苯硼钠雷氏铵雷氏盐利伯曼试验马奎斯试验溴化氰试剂本内迪克特试剂茚三酮试剂酸性碘铂酸盐溶液曼德林试剂松柏醇试剂对氨基苯磺酸氨基磺酸氨基磺酸铵氨基蒽醌类染料苏丹蓝CN 苏丹绿4B 茜素青绿茜素亮紫3B 茜素亮紫R 氯胺T滴定法N- 溴代苯二甲酰亚胺滴定法席夫碱氧瓶燃烧法溴酸盐滴定法不可逆指示剂卡可基邻二氮菲本生阀二苯甲酮杜瓦瓶费林反应折光测定法铜刨花拓奎反应对萘酚苯甲醇溶剂蓝19 收敛酸锌收敛酸镉坚牢绿FCF 搔洛铬绿V150 nonaqueous catalytic thermometric titration extraction gravimetry extraction titration sodium tetraphenylborate ammonium reineckate Reinecke salt Liebermann test Marquis test cyanogen bromide reagent Benedict reagent ninhydrin reagent acidified iodoplatinate solution Mandelin reagent coniferyl alcohol reagent sulfanilic acid sulfamic acid ammonium sulfamate aminoanthraquinone dyes Sudan blue CN Sudan green 4B alizarin viridine alizarin brilliant violet 3B alizarin brilliant violet R chloramine-T titration, CAT titration N-bromophthalimide titration Schiff’s base oxygen flask combustion bromate titrationirreversible indicator cacodyl orthophenanthroline Bunsen valve benzophenone Dewar flask Fehling’s reaction refractometry copper turnings Thalleoquin reaction p-naphtholbenzein solvent blue 19 zinc styphnate cadmium styphnate fast green FCF solochrome green V150 热色效应呫吨硫色素反应拉尼镍2,6-二氯靛酚滴定法坂口试验麦芽酚反应莫利希试验缩合物埃光子能量外转换电子构型电子跃迁拉波特规律末端吸收肩峰曲折拐点桑德尔灵敏度深色效应浅色效应蓝移红移解线性方程组法等吸光点法二波长分光光度法三波长分光光度法导数分光光度法pH指示剂吸光度比值测定法多组分光谱分析电荷转移光谱荧光分析法分子荧光分析法流动吸收池微量吸收池长光程吸收池热谱带迈克尔森干涉仪荧胺丹酰氯猝灭荧光测定法化学发光免疫分析法荧光免疫分析thermochromism effectxanthene thiochrome reaction Raney nickel 2, 6--dichlorindophenol titration Sakaguchi test maltol reaction Molisch test condensation substance angstr?m, ? photon external conversion of energy electronic configuration electron transition Laport’s law end absorption shoulder peak deflection deflection point Sandell’s sensitivity hyperchromic effect hypsochromic effect blue shift red shift simultaneous equations method isosbestic point method dual wavelength spectrophotometry three wavelength spectrophotome-try derivative spectrophotometry pH indicator absorbance ratio method multicomponent spectrophotometry charge-transfer spectrum fluorimetry molecular fluorescent method flow cell, flow cuvette micro cell long path cell hot bands Michelson interferometer fluorescamine dansyl chloride, DANS-Cl quenchingfluorometry chemiluminescence immunoassay, CIA fluorescence immunoassay, FIA 酶免疫分析鲁米诺荧光偏振免疫分析胶束增敏荧光分析法伸缩振动弯曲振动变形振动对称伸缩振动不对称伸缩振动剪式振动平面摇摆振动非平面摇摆振动扭曲振动呼吸振动振动弛豫振动偶合基频吸收带倍频吸收带费米共振能斯特灯镍铬线圈硅碳棒双光束光学零位平衡系统电比率记录式电学零位平衡系统漫反射衰减全反射多次内反射光束聚焦装置聚苯乙烯薄膜波数压片法石蜡糊法膜法光管热电偶检测器戈雷检测器氘化三甘氨硫酸酯检测器碲化汞一碲化镉复合半导体检测器指纹区光谱检索谱线检索光谱差减法enzyme immunoassay, EIA luminol fluorescence polarization immuno-assay, FPIA micellar enhanced spectrofluoro-metric method stretching vibration bending vibration deformationvibration symmetrical stretching vibration asymmetrical stretching vibration scissoring vibration rocking vibration wagging vibration twisting vibration breathing vibration vibrational relaxation vibrational coupling basic frequency absorption band multiple frequency absorption band Fermi resonance Nernst glower nichrome coils globars double beam optical--null system ratio recording electric-null system diffuse-reflectance attenuated total reflectance, ATR multiple internal reflection, MIR beam condenser polystyrene film wave number [halide] disk method, wafer method, pellet method nujol mull method film method light pipe thermocouple detector Golay detector deuterated triglycine sulfate detector, DTGS detector mercury cadmium telluride detector, MCT detector finger print region spectral search spec-finder spectralsubtraction method 红外光吸收参比图谱光度滴定法酸性染料比色法氨基硫脲比色法钯离子比色法四氮唑比色法科伯试剂检测限系统适用性峰不对称度峰重叠分层胶束色谱法填料极性正相反相疏溶剂作用亲硅羟基作用硅胶氧化铝化学键合相十八烷基硅烷键合硅胶氨基硅烷键合硅胶离子交换纤维素微晶纤维素聚苯乙烯凝胶琼脂糖凝胶聚丙烯酰胺凝胶葡聚糖凝胶己烷磺酸钠十二烷基硫酸钠微量注射器柱超载积分仪色谱数据处理机色谱工作站高效薄层色谱法过压薄层色谱法带状色谱连续展开短床连续展开斑点再浓集infra-red reference spectra photometric titration acid-dye colorimetry thiosemicarbazide colorimetry palladium ion colorimetry tetrazoline colorimetry Kober reagent detectability system suitability peak asymmetry peak overlapping demixing micellar chromatography packing materialpolarity normal phase reversed phase solvophobic interaction silanophilic interaction silica gel alumina chemically bonded phase octadecylsilane chemically bonded silica amino chemically bonded silica ion-exchange cellulose microcrystalline cellulose polystyrene gel agarose gel polyacrylamide gel polydextran gel sodium hexanesulfonate sodium dodecylsulfate, SDS microsyringe column overload integrator chromatographic data processor chromatographic work station high performance thin--layer chromatography, HPTLC overpressure thin—layer chrom-atography strip chromatography continuous development short-bed continuous development spot reconcentration 原位定量法床外因素背材预制板硅胶H 硅胶G 荧光剂薄层板贮箱点样器微升毛细管模板热微量转移法试剂喷雾器紫外线灯线性扫描锯齿扫描干装柱法湿装柱法固定相涂布内标物科瓦茨保留指数官能团保留指数亚甲基单位有效碳数相特征常数罗尔施奈德相常数麦克雷诺兹相常数布朗三角图形法气体净化器戈雷柱熔融二氧化硅空心柱分流无分流尾吹气进样隔膜胶垫闪蒸进样法柱上进样器鸭嘴阀针导顶空浓缩进样器冷柱头进样器卡套聚氨酯卡套石墨卡套quantitation in situ extra--bed factor backing material precoated plate silica gel H silica gel G fluorescent agent plate storage rack sample applicator microcap template thermo micro-application sepa-ration, TAS reagent sprayer ultraviolet lamp linear scanning zigzag scanning dry packing method wet packing method stationary phase coating internal standard substance Kovats retention index functional retention index methylene unit, MU effective carbon number phase specific constantRohrschneider phase constant McReynolds phase constant Brown triangle method gas purifier Golay column fused-silica open tubular column,FSOT split splitless make-up gas injecting septum flash evaporating injection on-column injector duckbill valve needle guide head-space concentrating injector cold on-column injector ferrule polyurethane ferrule graphite ferrule 皂土一34 蒙脱土混合固定相高分子多孔小球热能分析器气[相色谱]-[傅里叶变换]红[外光谱]联用仪高分辨气相色谱法联接头流速程序高效液相色谱法再循环色谱法脱气在线脱气设备低压梯度泵往复隔膜泵齿轮泵注射泵单向阀进样阀柱切换质量分析检测器程序波长检测器光电二极管阵列检测器多维检测等高线色谱图三维色谱图二次化学平衡编辑对话衰减时间常数响应时间时间谱带展宽空间谱带展宽保留隘口重叠峰基线分离峰峰谷驼峰试样在线预处理手性固定相手性拆分离子抑制液[相色谱]-质[谱]联用仪bentone-34 montmorillonite clay mixed stationary phase porous polymer beads thermo-energy analyser, TEA gas chromatograph/ Fourier trans-form infrared spectrophotometer, GC/FTIR high resolution gas chromato-graphy, HRGC union flow programming high perfor-mance liquid chromatography recycle chromatography degassing on-line degasser low pressure gradient pump reciprocating diaphragm pump gear pump syringe pump check valve injection valve column switching mass analyser detector programmable wavelength detector photodiode array detector, DAD multidimensional detection contour chromatogram three-dimensional chromatogram second chemical equilibrium editing dialog attenuation time constant response time band broading intime band broading in space retention gap fused peaks, overlapped peaks baseline resolved peak peak valley rider peak sample on-line pretreatment chiral stationary phase, CSP chiral separation ion suppress liquid chromatograph/mass spectrometer, LC/MS 传送带接口直接液体进样热喷雾接口电喷雾接口液体离子蒸发单分散气雾形成接口柱前衍生化柱后衍生化衍生化反应小管重氮甲烷迁移时间平板电泳毛细管电泳胶束动电毛细管色谱法内电渗电迁移进样抽空进样静力进样柱上检测器示差脉冲极谱法电子电位计参比电极饱和甘汞电极氯化银电极指示电极银电极盐桥电解电流质谱图道尔顿相对强度极限前体全氟煤油离子一分子复合物双价离子麦氏重排拉曼效应棒图扇形磁场质谱仪名义质量准确质量磁阻moving belt interface direct liquid introduction, DLI thermospray interface, TSPinterfaceelectrospray interface, ESPinterface liquid ion evaporation, LIE monodisperse aerosol generation interface, MAGIC pre-column derivatization post-column derivatization derivatizing reaction vial diazomethane migration time disk electrophoresis capillary electrophoresis, CE micellar electrokinetic capillary chromatography, MECC,MEKC electroendosmosis electromigration injection vacuum injection hydrostatic injection on-column detector differential pulse polarography electronic potentiometer reference electrode saturated calomel electrode silver chloride electrode indicating electrode silver electrode salt bridge Faradaic current mass spectrum dalton relative intensity ultimate precursor polyfluoro kerosene, PFK ion-molecule complex doubly charged ion McLafferty rearrangement Raman effect bar graph magnetic sector mass spectrometernominal mass exact mass reluctance 叠片磁铁十倍程象电流分立二发射极软离子化方法灯丝准分子离子大气压离子化静电喷雾二次离子质谱法溅射现象近距碰撞等离子体解吸质谱法激光解吸质谱法粒子诱导质谱法直接进样杆加热贮槽进样器粒子束重建总离子流各向异性各向异性屏蔽软脉冲五重峰六重峰七重峰偕偶邻偶偏共振去偶选择去偶反门控去偶反磁性屏蔽J调制法谱编辑不灵敏核极化转移增益法无畸变极化转移增益法双量子转移实验laminated magnets decade image current discrete dynode soft ionization filament quasi-molecular ion atmospheric pressure ionization, API electrostatic spray secondary ion mass spectrometry,SIMS sputtering phenomenon near mass collision plasma desorption mass spectrometry, PDMS laser desorption mass spectrometry, LDMS particle induced================精选公文范文,管理类,工作总结类,工作计划类文档,欢迎阅读下载============== mass spectrometry direct inlet probe, DIP heatable reservoir inlet particle beam reconstruction total ion current,reconstruction TIC anisotropic anisotropic shielding soft pulse quintet sextet septet geminal coupling vicinal coupling off resonance decoupling selective decoupling inverted gated decoupling diamagnetic shielding J-modulation method spectral editing insensitive nucleus enhancementby polarization transfer, INEPT distortionless enhancement bypolarization transfer, DEPT double quantum transferexperiment--------------------精选公文范文,管理类,工作总结类,工作计划类文档,感谢阅读下载--------------------- ~ 21 ~。
英汉对照计量经济学术语
计量经济学术语A校正R2(Adjusted R-Squared):多元回归分析中拟合优度的量度,在估计误差的方差时对添加的解释变量用一个自由度来调整。
对立假设(Alternative Hypothesis):检验虚拟假设时的相对假设。
AR(1)序列相关(AR(1) Serial Correlation):时间序列回归模型中的误差遵循AR(1)模型。
渐近置信区间(Asymptotic Confidence Interval):大样本容量下近似成立的置信区间。
渐近正态性(Asymptotic Normality):适当正态化后样本分布收敛到标准正态分布的估计量。
渐近性质(Asymptotic Properties):当样本容量无限增长时适用的估计量和检验统计量性质。
渐近标准误(Asymptotic Standard Error):大样本下生效的标准误。
渐近t 统计量(Asymptotic t Statistic):大样本下近似服从标准正态分布的t 统计量。
渐近方差(Asymptotic Variance):为了获得渐近标准正态分布,我们必须用以除估计量的平方值。
渐近有效(Asymptotically Efficient):对于服从渐近正态分布的一致性估计量,有最小渐近方差的估计量。
渐近不相关(Asymptotically Uncorrelated):时间序列过程中,随着两个时点上的随机变量的时间间隔增加,它们之间的相关趋于零。
衰减偏误(Attenuation Bias):总是朝向零的估计量偏误,因而有衰减偏误的估计量的期望值小于参数的绝对值。
自回归条件异方差性(Autoregressive Conditional Heteroskedasticity, ARCH):动态异方差性模型,即给定过去信息,误差项的方差线性依赖于过去的误差的平方。
一阶自回归过程[AR(1)](Autoregressive Process of Order One [AR(1)]):一个时间序列模型,其当前值线性依赖于最近的值加上一个无法预测的扰动。
人工智能词汇
常用英语词汇 -andrew Ng课程average firing rate均匀激活率intensity强度average sum-of-squares error均方差Regression回归backpropagation后向流传Loss function损失函数basis 基non-convex非凸函数basis feature vectors特点基向量neural network神经网络batch gradient ascent批量梯度上涨法supervised learning监察学习Bayesian regularization method贝叶斯规则化方法regression problem回归问题办理的是连续的问题Bernoulli random variable伯努利随机变量classification problem分类问题bias term偏置项discreet value失散值binary classfication二元分类support vector machines支持向量机class labels种类标记learning theory学习理论concatenation级联learning algorithms学习算法conjugate gradient共轭梯度unsupervised learning无监察学习contiguous groups联通地区gradient descent梯度降落convex optimization software凸优化软件linear regression线性回归convolution卷积Neural Network神经网络cost function代价函数gradient descent梯度降落covariance matrix协方差矩阵normal equations DC component直流重量linear algebra线性代数decorrelation去有关superscript上标degeneracy退化exponentiation指数demensionality reduction降维training set训练会合derivative导函数training example训练样本diagonal对角线hypothesis假定,用来表示学习算法的输出diffusion of gradients梯度的弥散LMS algorithm “least mean squares最小二乘法算eigenvalue特点值法eigenvector特点向量batch gradient descent批量梯度降落error term残差constantly gradient descent随机梯度降落feature matrix特点矩阵iterative algorithm迭代算法feature standardization特点标准化partial derivative偏导数feedforward architectures前馈构造算法contour等高线feedforward neural network前馈神经网络quadratic function二元函数feedforward pass前馈传导locally weighted regression局部加权回归fine-tuned微调underfitting欠拟合first-order feature一阶特点overfitting过拟合forward pass前向传导non-parametric learning algorithms无参数学习算forward propagation前向流传法Gaussian prior高斯先验概率parametric learning algorithm参数学习算法generative model生成模型activation激活值gradient descent梯度降落activation function激活函数Greedy layer-wise training逐层贪心训练方法additive noise加性噪声grouping matrix分组矩阵autoencoder自编码器Hadamard product阿达马乘积Autoencoders自编码算法Hessian matrix Hessian矩阵hidden layer隐含层hidden units隐蔽神经元Hierarchical grouping层次型分组higher-order features更高阶特点highly non-convex optimization problem高度非凸的优化问题histogram直方图hyperbolic tangent双曲正切函数hypothesis估值,假定identity activation function恒等激励函数IID 独立同散布illumination照明inactive克制independent component analysis独立成份剖析input domains输入域input layer输入层intensity亮度/灰度intercept term截距KL divergence相对熵KL divergence KL分别度k-Means K-均值learning rate学习速率least squares最小二乘法linear correspondence线性响应linear superposition线性叠加line-search algorithm线搜寻算法local mean subtraction局部均值消减local optima局部最优解logistic regression逻辑回归loss function损失函数low-pass filtering低通滤波magnitude幅值MAP 极大后验预计maximum likelihood estimation极大似然预计mean 均匀值MFCC Mel 倒频系数multi-class classification多元分类neural networks神经网络neuron 神经元Newton’s method牛顿法non-convex function非凸函数non-linear feature非线性特点norm 范式norm bounded有界范数norm constrained范数拘束normalization归一化numerical roundoff errors数值舍入偏差numerically checking数值查验numerically reliable数值计算上稳固object detection物体检测objective function目标函数off-by-one error缺位错误orthogonalization正交化output layer输出层overall cost function整体代价函数over-complete basis超齐备基over-fitting过拟合parts of objects目标的零件part-whole decompostion部分-整体分解PCA 主元剖析penalty term处罚因子per-example mean subtraction逐样本均值消减pooling池化pretrain预训练principal components analysis主成份剖析quadratic constraints二次拘束RBMs 受限 Boltzman 机reconstruction based models鉴于重构的模型reconstruction cost重修代价reconstruction term重构项redundant冗余reflection matrix反射矩阵regularization正则化regularization term正则化项rescaling缩放robust 鲁棒性run 行程second-order feature二阶特点sigmoid activation function S型激励函数significant digits有效数字singular value奇怪值singular vector奇怪向量smoothed L1 penalty光滑的L1 范数处罚Smoothed topographic L1 sparsity penalty光滑地形L1 稀少处罚函数smoothing光滑Softmax Regresson Softmax回归sorted in decreasing order降序摆列source features源特点Adversarial Networks抗衡网络sparse autoencoder消减归一化Affine Layer仿射层Sparsity稀少性Affinity matrix亲和矩阵sparsity parameter稀少性参数Agent 代理 /智能体sparsity penalty稀少处罚Algorithm 算法square function平方函数Alpha- beta pruningα - β剪枝squared-error方差Anomaly detection异样检测stationary安稳性(不变性)Approximation近似stationary stochastic process安稳随机过程Area Under ROC Curve/ AUC Roc 曲线下边积step-size步长值Artificial General Intelligence/AGI通用人工智supervised learning监察学习能symmetric positive semi-definite matrix Artificial Intelligence/AI人工智能对称半正定矩阵Association analysis关系剖析symmetry breaking对称无效Attention mechanism注意力体制tanh function双曲正切函数Attribute conditional independence assumptionthe average activation均匀活跃度属性条件独立性假定the derivative checking method梯度考证方法Attribute space属性空间the empirical distribution经验散布函数Attribute value属性值the energy function能量函数Autoencoder自编码器the Lagrange dual拉格朗日对偶函数Automatic speech recognition自动语音辨别the log likelihood对数似然函数Automatic summarization自动纲要the pixel intensity value像素灰度值Average gradient均匀梯度the rate of convergence收敛速度Average-Pooling均匀池化topographic cost term拓扑代价项Backpropagation Through Time经过时间的反向流传topographic ordered拓扑次序Backpropagation/BP反向流传transformation变换Base learner基学习器translation invariant平移不变性Base learning algorithm基学习算法trivial answer平庸解Batch Normalization/BN批量归一化under-complete basis不齐备基Bayes decision rule贝叶斯判断准则unrolling组合扩展Bayes Model Averaging/ BMA 贝叶斯模型均匀unsupervised learning无监察学习Bayes optimal classifier贝叶斯最优分类器variance 方差Bayesian decision theory贝叶斯决议论vecotrized implementation向量化实现Bayesian network贝叶斯网络vectorization矢量化Between-class scatter matrix类间散度矩阵visual cortex视觉皮层Bias 偏置 /偏差weight decay权重衰减Bias-variance decomposition偏差 - 方差分解weighted average加权均匀值Bias-Variance Dilemma偏差–方差窘境whitening白化Bi-directional Long-Short Term Memory/Bi-LSTMzero-mean均值为零双向长短期记忆Accumulated error backpropagation积累偏差逆传Binary classification二分类播Binomial test二项查验Activation Function激活函数Bi-partition二分法Adaptive Resonance Theory/ART自适应谐振理论Boltzmann machine玻尔兹曼机Addictive model加性学习Bootstrap sampling自助采样法/可重复采样Bootstrapping自助法Break-Event Point/ BEP 均衡点Calibration校准Cascade-Correlation级联有关Categorical attribute失散属性Class-conditional probability类条件概率Classification and regression tree/CART分类与回归树Classifier分类器Class-imbalance类型不均衡Closed -form闭式Cluster簇/ 类/ 集群Cluster analysis聚类剖析Clustering聚类Clustering ensemble聚类集成Co-adapting共适应Coding matrix编码矩阵COLT 国际学习理论会议Committee-based learning鉴于委员会的学习Competitive learning竞争型学习Component learner组件学习器Comprehensibility可解说性Computation Cost计算成本Computational Linguistics计算语言学Computer vision计算机视觉Concept drift观点漂移Concept Learning System /CLS观点学习系统Conditional entropy条件熵Conditional mutual information条件互信息Conditional Probability Table/ CPT 条件概率表Conditional random field/CRF条件随机场Conditional risk条件风险Confidence置信度Confusion matrix混杂矩阵Connection weight连结权Connectionism 连结主义Consistency一致性/相合性Contingency table列联表Continuous attribute连续属性Convergence收敛Conversational agent会话智能体Convex quadratic programming凸二次规划Convexity凸性Convolutional neural network/CNN卷积神经网络Co-occurrence同现Correlation coefficient有关系数Cosine similarity余弦相像度Cost curve成本曲线Cost Function成本函数Cost matrix成本矩阵Cost-sensitive成本敏感Cross entropy交错熵Cross validation交错考证Crowdsourcing众包Curse of dimensionality维数灾害Cut point截断点Cutting plane algorithm割平面法Data mining数据发掘Data set数据集Decision Boundary决议界限Decision stump决议树桩Decision tree决议树/判断树Deduction演绎Deep Belief Network深度信念网络Deep Convolutional Generative Adversarial NetworkDCGAN深度卷积生成抗衡网络Deep learning深度学习Deep neural network/DNN深度神经网络Deep Q-Learning深度Q 学习Deep Q-Network深度Q 网络Density estimation密度预计Density-based clustering密度聚类Differentiable neural computer可微分神经计算机Dimensionality reduction algorithm降维算法Directed edge有向边Disagreement measure不合胸怀Discriminative model鉴别模型Discriminator鉴别器Distance measure距离胸怀Distance metric learning距离胸怀学习Distribution散布Divergence散度Diversity measure多样性胸怀/差别性胸怀Domain adaption领域自适应Downsampling下采样D-separation( Directed separation)有向分别Dual problem对偶问题Dummy node 哑结点General Problem Solving通用问题求解Dynamic Fusion 动向交融Generalization泛化Dynamic programming动向规划Generalization error泛化偏差Eigenvalue decomposition特点值分解Generalization error bound泛化偏差上界Embedding 嵌入Generalized Lagrange function广义拉格朗日函数Emotional analysis情绪剖析Generalized linear model广义线性模型Empirical conditional entropy经验条件熵Generalized Rayleigh quotient广义瑞利商Empirical entropy经验熵Generative Adversarial Networks/GAN生成抗衡网Empirical error经验偏差络Empirical risk经验风险Generative Model生成模型End-to-End 端到端Generator生成器Energy-based model鉴于能量的模型Genetic Algorithm/GA遗传算法Ensemble learning集成学习Gibbs sampling吉布斯采样Ensemble pruning集成修剪Gini index基尼指数Error Correcting Output Codes/ ECOC纠错输出码Global minimum全局最小Error rate错误率Global Optimization全局优化Error-ambiguity decomposition偏差 - 分歧分解Gradient boosting梯度提高Euclidean distance欧氏距离Gradient Descent梯度降落Evolutionary computation演化计算Graph theory图论Expectation-Maximization希望最大化Ground-truth实情/真切Expected loss希望损失Hard margin硬间隔Exploding Gradient Problem梯度爆炸问题Hard voting硬投票Exponential loss function指数损失函数Harmonic mean 调解均匀Extreme Learning Machine/ELM超限学习机Hesse matrix海塞矩阵Factorization因子分解Hidden dynamic model隐动向模型False negative假负类Hidden layer隐蔽层False positive假正类Hidden Markov Model/HMM 隐马尔可夫模型False Positive Rate/FPR假正例率Hierarchical clustering层次聚类Feature engineering特点工程Hilbert space希尔伯特空间Feature selection特点选择Hinge loss function合页损失函数Feature vector特点向量Hold-out 留出法Featured Learning特点学习Homogeneous 同质Feedforward Neural Networks/FNN前馈神经网络Hybrid computing混杂计算Fine-tuning微调Hyperparameter超参数Flipping output翻转法Hypothesis假定Fluctuation震荡Hypothesis test假定考证Forward stagewise algorithm前向分步算法ICML 国际机器学习会议Frequentist频次主义学派Improved iterative scaling/IIS改良的迭代尺度法Full-rank matrix满秩矩阵Incremental learning增量学习Functional neuron功能神经元Independent and identically distributed/独Gain ratio增益率立同散布Game theory博弈论Independent Component Analysis/ICA独立成分剖析Gaussian kernel function高斯核函数Indicator function指示函数Gaussian Mixture Model高斯混杂模型Individual learner个体学习器Induction归纳Inductive bias归纳偏好Inductive learning归纳学习Inductive Logic Programming/ ILP归纳逻辑程序设计Information entropy信息熵Information gain信息增益Input layer输入层Insensitive loss不敏感损失Inter-cluster similarity簇间相像度International Conference for Machine Learning/ICML国际机器学习大会Intra-cluster similarity簇内相像度Intrinsic value固有值Isometric Mapping/Isomap等胸怀映照Isotonic regression平分回归Iterative Dichotomiser迭代二分器Kernel method核方法Kernel trick核技巧Kernelized Linear Discriminant Analysis/KLDA核线性鉴别剖析K-fold cross validation k折交错考证/k 倍交错考证K-Means Clustering K–均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base 知识库Knowledge Representation知识表征Label space标记空间Lagrange duality拉格朗日对偶性Lagrange multiplier拉格朗日乘子Laplace smoothing拉普拉斯光滑Laplacian correction拉普拉斯修正Latent Dirichlet Allocation隐狄利克雷散布Latent semantic analysis潜伏语义剖析Latent variable隐变量Lazy learning懒散学习Learner学习器Learning by analogy类比学习Learning rate学习率Learning Vector Quantization/LVQ学习向量量化Least squares regression tree最小二乘回归树Leave-One-Out/LOO留一法linear chain conditional random field线性链条件随机场Linear Discriminant Analysis/ LDA 线性鉴别剖析Linear model线性模型Linear Regression线性回归Link function联系函数Local Markov property局部马尔可夫性Local minimum局部最小Log likelihood对数似然Log odds/ logit对数几率Logistic Regression Logistic回归Log-likelihood对数似然Log-linear regression对数线性回归Long-Short Term Memory/LSTM 长短期记忆Loss function损失函数Machine translation/MT机器翻译Macron-P宏查准率Macron-R宏查全率Majority voting绝对多半投票法Manifold assumption流形假定Manifold learning流形学习Margin theory间隔理论Marginal distribution边沿散布Marginal independence边沿独立性Marginalization边沿化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field马尔可夫随机场Maximal clique最大团Maximum Likelihood Estimation/MLE极大似然预计/极大似然法Maximum margin最大间隔Maximum weighted spanning tree最大带权生成树Max-Pooling 最大池化Mean squared error均方偏差Meta-learner元学习器Metric learning胸怀学习Micro-P微查准率Micro-R微查全率Minimal Description Length/MDL最小描绘长度Minimax game极小极大博弈Misclassification cost误分类成本Mixture of experts混杂专家Momentum 动量Moral graph道德图/正直图Multi-class classification多分类Multi-document summarization多文档纲要One shot learning一次性学习Multi-layer feedforward neural networks One-Dependent Estimator/ ODE 独依靠预计多层前馈神经网络On-Policy在策略Multilayer Perceptron/MLP多层感知器Ordinal attribute有序属性Multimodal learning多模态学习Out-of-bag estimate包外预计Multiple Dimensional Scaling多维缩放Output layer输出层Multiple linear regression多元线性回归Output smearing输出调制法Multi-response Linear Regression/ MLR Overfitting过拟合/过配多响应线性回归Oversampling 过采样Mutual information互信息Paired t-test成对 t查验Naive bayes 朴实贝叶斯Pairwise 成对型Naive Bayes Classifier朴实贝叶斯分类器Pairwise Markov property成对马尔可夫性Named entity recognition命名实体辨别Parameter参数Nash equilibrium纳什均衡Parameter estimation参数预计Natural language generation/NLG自然语言生成Parameter tuning调参Natural language processing自然语言办理Parse tree分析树Negative class负类Particle Swarm Optimization/PSO粒子群优化算法Negative correlation负有关法Part-of-speech tagging词性标明Negative Log Likelihood负对数似然Perceptron感知机Neighbourhood Component Analysis/NCA Performance measure性能胸怀近邻成分剖析Plug and Play Generative Network即插即用生成网Neural Machine Translation神经机器翻译络Neural Turing Machine神经图灵机Plurality voting相对多半投票法Newton method牛顿法Polarity detection极性检测NIPS 国际神经信息办理系统会议Polynomial kernel function多项式核函数No Free Lunch Theorem/ NFL 没有免费的午饭定理Pooling池化Noise-contrastive estimation噪音对照预计Positive class正类Nominal attribute列名属性Positive definite matrix正定矩阵Non-convex optimization非凸优化Post-hoc test后续查验Nonlinear model非线性模型Post-pruning后剪枝Non-metric distance非胸怀距离potential function势函数Non-negative matrix factorization非负矩阵分解Precision查准率/正确率Non-ordinal attribute无序属性Prepruning 预剪枝Non-Saturating Game非饱和博弈Principal component analysis/PCA主成分剖析Norm 范数Principle of multiple explanations多释原则Normalization归一化Prior 先验Nuclear norm核范数Probability Graphical Model概率图模型Numerical attribute数值属性Proximal Gradient Descent/PGD近端梯度降落Letter O Pruning剪枝Objective function目标函数Pseudo-label伪标记Oblique decision tree斜决议树Quantized Neural Network量子化神经网络Occam’s razor奥卡姆剃刀Quantum computer 量子计算机Odds 几率Quantum Computing量子计算Off-Policy离策略Quasi Newton method拟牛顿法Radial Basis Function/ RBF 径向基函数Random Forest Algorithm随机丛林算法Random walk随机闲步Recall 查全率/召回率Receiver Operating Characteristic/ROC受试者工作特点Rectified Linear Unit/ReLU线性修正单元Recurrent Neural Network循环神经网络Recursive neural network递归神经网络Reference model 参照模型Regression回归Regularization正则化Reinforcement learning/RL加强学习Representation learning表征学习Representer theorem表示定理reproducing kernel Hilbert space/RKHS重生核希尔伯特空间Re-sampling重采样法Rescaling再缩放Residual Mapping残差映照Residual Network残差网络Restricted Boltzmann Machine/RBM受限玻尔兹曼机Restricted Isometry Property/RIP限制等距性Re-weighting重赋权法Robustness稳重性 / 鲁棒性Root node根结点Rule Engine规则引擎Rule learning规则学习Saddle point鞍点Sample space样本空间Sampling采样Score function评分函数Self-Driving自动驾驶Self-Organizing Map/ SOM自组织映照Semi-naive Bayes classifiers半朴实贝叶斯分类器Semi-Supervised Learning半监察学习semi-Supervised Support Vector Machine半监察支持向量机Sentiment analysis感情剖析Separating hyperplane分别超平面Sigmoid function Sigmoid函数Similarity measure相像度胸怀Simulated annealing模拟退火Simultaneous localization and mapping同步定位与地图建立Singular Value Decomposition奇怪值分解Slack variables废弛变量Smoothing光滑Soft margin软间隔Soft margin maximization软间隔最大化Soft voting软投票Sparse representation稀少表征Sparsity稀少性Specialization特化Spectral Clustering谱聚类Speech Recognition语音辨别Splitting variable切分变量Squashing function挤压函数Stability-plasticity dilemma可塑性 - 稳固性窘境Statistical learning统计学习Status feature function状态特点函Stochastic gradient descent随机梯度降落Stratified sampling分层采样Structural risk构造风险Structural risk minimization/SRM构造风险最小化Subspace子空间Supervised learning监察学习/有导师学习support vector expansion支持向量展式Support Vector Machine/SVM支持向量机Surrogat loss代替损失Surrogate function代替函数Symbolic learning符号学习Symbolism符号主义Synset同义词集T-Distribution Stochastic Neighbour Embeddingt-SNE T–散布随机近邻嵌入Tensor 张量Tensor Processing Units/TPU张量办理单元The least square method最小二乘法Threshold阈值Threshold logic unit阈值逻辑单元Threshold-moving阈值挪动Time Step时间步骤Tokenization标记化Training error训练偏差Training instance训练示例/训练例Transductive learning直推学习Transfer learning迁徙学习Treebank树库algebra线性代数Tria-by-error试错法asymptotically无症状的True negative真负类appropriate适合的True positive真切类bias 偏差True Positive Rate/TPR真切例率brevity简洁,简洁;短暂Turing Machine图灵机[800 ] broader宽泛Twice-learning二次学习briefly简洁的Underfitting欠拟合/欠配batch 批量Undersampling欠采样convergence收敛,集中到一点Understandability可理解性convex凸的Unequal cost非均等代价contours轮廓Unit-step function单位阶跃函数constraint拘束Univariate decision tree单变量决议树constant常理Unsupervised learning无监察学习/无导师学习commercial商务的Unsupervised layer-wise training无监察逐层训练complementarity增补Upsampling上采样coordinate ascent同样级上涨Vanishing Gradient Problem梯度消逝问题clipping剪下物;剪报;修剪Variational inference变分推测component重量;零件VC Theory VC维理论continuous连续的Version space版本空间covariance协方差Viterbi algorithm维特比算法canonical正规的,正则的Von Neumann architecture冯· 诺伊曼架构concave非凸的Wasserstein GAN/WGAN Wasserstein生成抗衡网络corresponds相切合;相当;通讯Weak learner弱学习器corollary推论Weight权重concrete详细的事物,实在的东西Weight sharing权共享cross validation交错考证Weighted voting加权投票法correlation互相关系Within-class scatter matrix类内散度矩阵convention商定Word embedding词嵌入cluster一簇Word sense disambiguation词义消歧centroids质心,形心Zero-data learning零数据学习converge收敛Zero-shot learning零次学习computationally计算(机)的approximations近似值calculus计算arbitrary任意的derive获取,获得affine仿射的dual 二元的arbitrary任意的duality二元性;二象性;对偶性amino acid氨基酸derivation求导;获取;发源amenable 经得起查验的denote预示,表示,是的标记;意味着,[逻]指称axiom 公义,原则divergence散度;发散性abstract提取dimension尺度,规格;维数architecture架构,系统构造;建筑业dot 小圆点absolute绝对的distortion变形arsenal军械库density概率密度函数assignment分派discrete失散的人工智能词汇discriminative有辨别能力的indicator指示物,指示器diagonal对角interative重复的,迭代的dispersion分别,散开integral积分determinant决定要素identical相等的;完整同样的disjoint不订交的indicate表示,指出encounter碰到invariance不变性,恒定性ellipses椭圆impose把强加于equality等式intermediate中间的extra 额外的interpretation解说,翻译empirical经验;察看joint distribution结合概率ennmerate例举,计数lieu 代替exceed超出,越出logarithmic对数的,用对数表示的expectation希望latent潜伏的efficient奏效的Leave-one-out cross validation留一法交错考证endow 给予magnitude巨大explicitly清楚的mapping 画图,制图;映照exponential family指数家族matrix矩阵equivalently等价的mutual互相的,共同的feasible可行的monotonically单一的forary首次试试minor较小的,次要的finite有限的,限制的multinomial多项的forgo 摒弃,放弃multi-class classification二分类问题fliter过滤nasty厌烦的frequentist最常发生的notation标记,说明forward search前向式搜寻na?ve 朴实的formalize使定形obtain获取generalized归纳的oscillate摇动generalization归纳,归纳;广泛化;判断(依据不optimization problem最优化问题足)objective function目标函数guarantee保证;抵押品optimal最理想的generate形成,产生orthogonal(矢量,矩阵等 ) 正交的geometric margins几何界限orientation方向gap 裂口ordinary一般的generative生产的;有生产力的occasionally有时的heuristic启迪式的;启迪法;启迪程序partial derivative偏导数hone 怀恋;磨property性质hyperplane超平面proportional成比率的initial最先的primal原始的,最先的implement履行permit同意intuitive凭直觉获知的pseudocode 伪代码incremental增添的permissible可同意的intercept截距polynomial多项式intuitious直觉preliminary预备instantiation例子precision精度人工智能词汇perturbation不安,搅乱theorem定理poist 假定,假想tangent正弦positive semi-definite半正定的unit-length vector单位向量parentheses圆括号valid 有效的,正确的posterior probability后验概率variance方差plementarity增补variable变量;变元pictorially图像的vocabulary 词汇parameterize确立的参数valued经估价的;可贵的poisson distribution柏松散布wrapper 包装pertinent有关的总计 1038 词汇quadratic二次的quantity量,数目;重量query 疑问的regularization使系统化;调整reoptimize从头优化restrict限制;限制;拘束reminiscent回想旧事的;提示的;令人联想的( of )remark 注意random variable随机变量respect考虑respectively各自的;分其他redundant过多的;冗余的susceptible敏感的stochastic可能的;随机的symmetric对称的sophisticated复杂的spurious假的;假造的subtract减去;减法器simultaneously同时发生地;同步地suffice知足scarce罕有的,难得的split分解,分别subset子集statistic统计量successive iteratious连续的迭代scale标度sort of有几分的squares 平方trajectory轨迹temporarily临时的terminology专用名词tolerance容忍;公差thumb翻阅threshold阈,临界。
多元统计回归分析的流程
多元统计回归分析的流程Statistical regression analysis is a fundamental tool for understanding the relationships between variables in a dataset. It is particularly important in multivariate analysis, where the interactions between several variables must be examined simultaneously. 多元统计回归分析旨在理解数据集中变量之间的关系,尤其在多变量分析中,需要同时考虑几个变量之间的相互作用。
The first step in multivariate statistical regression analysis is to define the research question or problem that needs to be addressed. This step is critical because it determines the variables to be included in the analysis and the type of regression model to be used. 多元统计回归分析的第一步是界定需要解决的研究问题或问题。
这一步至关重要,因为它确定了要包括在分析中的变量以及要使用的回归模型的类型。
Once the research question or problem is defined, the next step is to gather the relevant data. This may involve collecting data from existing sources or designing a new study to collect the necessary information. 数据收集是多元统计回归分析的下一步,这可能涉及从现有来源收集数据,或设计一项新的研究来收集必要的信息。
机器学习英语词汇
目录第一部分 (3)第二部分 (12)Letter A (12)Letter B (14)Letter C (15)Letter D (17)Letter E (19)Letter F (20)Letter G (21)Letter H (22)Letter I (23)Letter K (24)Letter L (24)Letter M (26)Letter N (27)Letter O (29)Letter P (29)Letter R (31)Letter S (32)Letter T (35)Letter U (36)Letter W (37)Letter Z (37)第三部分 (37)A (37)B (38)C (38)D (40)E (40)F (41)G (41)H (42)L (42)J (43)L (43)M (43)N (44)O (44)P (44)Q (45)R (46)S (46)U (47)V (48)第一部分[ ] intensity 强度[ ] Regression 回归[ ] Loss function 损失函数[ ] non-convex 非凸函数[ ] neural network 神经网络[ ] supervised learning 监督学习[ ] regression problem 回归问题处理的是连续的问题[ ] classification problem 分类问题处理的问题是离散的而不是连续的回归问题和分类问题的区别应该在于回归问题的结果是连续的,分类问题的结果是离散的。
[ ]discreet value 离散值[ ] support vector machines 支持向量机,用来处理分类算法中输入的维度不单一的情况(甚至输入维度为无穷)[ ] learning theory 学习理论[ ] learning algorithms 学习算法[ ] unsupervised learning 无监督学习[ ] gradient descent 梯度下降[ ] linear regression 线性回归[ ] Neural Network 神经网络[ ] gradient descent 梯度下降监督学习的一种算法,用来拟合的算法[ ] normal equations[ ] linear algebra 线性代数原谅我英语不太好[ ] superscript上标[ ] exponentiation 指数[ ] training set 训练集合[ ] training example 训练样本[ ] hypothesis 假设,用来表示学习算法的输出,叫我们不要太纠结H的意思,因为这只是历史的惯例[ ] LMS algorithm “least mean squares” 最小二乘法算法[ ] batch gradient descent 批量梯度下降,因为每次都会计算最小拟合的方差,所以运算慢[ ] constantly gradient descent 字幕组翻译成“随机梯度下降” 我怎么觉得是“常量梯度下降”也就是梯度下降的运算次数不变,一般比批量梯度下降速度快,但是通常不是那么准确[ ] iterative algorithm 迭代算法[ ] partial derivative 偏导数[ ] contour 等高线[ ] quadratic function 二元函数[ ] locally weighted regression局部加权回归[ ] underfitting欠拟合[ ] overfitting 过拟合[ ] non-parametric learning algorithms 无参数学习算法[ ] parametric learning algorithm 参数学习算法[ ] other[ ] activation 激活值[ ] activation function 激活函数[ ] additive noise 加性噪声[ ] autoencoder 自编码器[ ] Autoencoders 自编码算法[ ] average firing rate 平均激活率[ ] average sum-of-squares error 均方差[ ] backpropagation 后向传播[ ] basis 基[ ] basis feature vectors 特征基向量[50 ] batch gradient ascent 批量梯度上升法[ ] Bayesian regularization method 贝叶斯规则化方法[ ] Bernoulli random variable 伯努利随机变量[ ] bias term 偏置项[ ] binary classfication 二元分类[ ] class labels 类型标记[ ] concatenation 级联[ ] conjugate gradient 共轭梯度[ ] contiguous groups 联通区域[ ] convex optimization software 凸优化软件[ ] convolution 卷积[ ] cost function 代价函数[ ] covariance matrix 协方差矩阵[ ] DC component 直流分量[ ] decorrelation 去相关[ ] degeneracy 退化[ ] demensionality reduction 降维[ ] derivative 导函数[ ] diagonal 对角线[ ] diffusion of gradients 梯度的弥散[ ] eigenvalue 特征值[ ] eigenvector 特征向量[ ] error term 残差[ ] feature matrix 特征矩阵[ ] feature standardization 特征标准化[ ] feedforward architectures 前馈结构算法[ ] feedforward neural network 前馈神经网络[ ] feedforward pass 前馈传导[ ] fine-tuned 微调[ ] first-order feature 一阶特征[ ] forward pass 前向传导[ ] forward propagation 前向传播[ ] Gaussian prior 高斯先验概率[ ] generative model 生成模型[ ] gradient descent 梯度下降[ ] Greedy layer-wise training 逐层贪婪训练方法[ ] grouping matrix 分组矩阵[ ] Hadamard product 阿达马乘积[ ] Hessian matrix Hessian 矩阵[ ] hidden layer 隐含层[ ] hidden units 隐藏神经元[ ] Hierarchical grouping 层次型分组[ ] higher-order features 更高阶特征[ ] highly non-convex optimization problem 高度非凸的优化问题[ ] histogram 直方图[ ] hyperbolic tangent 双曲正切函数[ ] hypothesis 估值,假设[ ] identity activation function 恒等激励函数[ ] IID 独立同分布[ ] illumination 照明[100 ] inactive 抑制[ ] independent component analysis 独立成份分析[ ] input domains 输入域[ ] input layer 输入层[ ] intensity 亮度/灰度[ ] intercept term 截距[ ] KL divergence 相对熵[ ] KL divergence KL分散度[ ] k-Means K-均值[ ] learning rate 学习速率[ ] least squares 最小二乘法[ ] linear correspondence 线性响应[ ] linear superposition 线性叠加[ ] line-search algorithm 线搜索算法[ ] local mean subtraction 局部均值消减[ ] local optima 局部最优解[ ] logistic regression 逻辑回归[ ] loss function 损失函数[ ] low-pass filtering 低通滤波[ ] magnitude 幅值[ ] MAP 极大后验估计[ ] maximum likelihood estimation 极大似然估计[ ] mean 平均值[ ] MFCC Mel 倒频系数[ ] multi-class classification 多元分类[ ] neural networks 神经网络[ ] neuron 神经元[ ] Newton’s method 牛顿法[ ] non-convex function 非凸函数[ ] non-linear feature 非线性特征[ ] norm 范式[ ] norm bounded 有界范数[ ] norm constrained 范数约束[ ] normalization 归一化[ ] numerical roundoff errors 数值舍入误差[ ] numerically checking 数值检验[ ] numerically reliable 数值计算上稳定[ ] object detection 物体检测[ ] objective function 目标函数[ ] off-by-one error 缺位错误[ ] orthogonalization 正交化[ ] output layer 输出层[ ] overall cost function 总体代价函数[ ] over-complete basis 超完备基[ ] over-fitting 过拟合[ ] parts of objects 目标的部件[ ] part-whole decompostion 部分-整体分解[ ] PCA 主元分析[ ] penalty term 惩罚因子[ ] per-example mean subtraction 逐样本均值消减[150 ] pooling 池化[ ] pretrain 预训练[ ] principal components analysis 主成份分析[ ] quadratic constraints 二次约束[ ] RBMs 受限Boltzman机[ ] reconstruction based models 基于重构的模型[ ] reconstruction cost 重建代价[ ] reconstruction term 重构项[ ] redundant 冗余[ ] reflection matrix 反射矩阵[ ] regularization 正则化[ ] regularization term 正则化项[ ] rescaling 缩放[ ] robust 鲁棒性[ ] run 行程[ ] second-order feature 二阶特征[ ] sigmoid activation function S型激励函数[ ] significant digits 有效数字[ ] singular value 奇异值[ ] singular vector 奇异向量[ ] smoothed L1 penalty 平滑的L1范数惩罚[ ] Smoothed topographic L1 sparsity penalty 平滑地形L1稀疏惩罚函数[ ] smoothing 平滑[ ] Softmax Regresson Softmax回归[ ] sorted in decreasing order 降序排列[ ] source features 源特征[ ] sparse autoencoder 消减归一化[ ] Sparsity 稀疏性[ ] sparsity parameter 稀疏性参数[ ] sparsity penalty 稀疏惩罚[ ] square function 平方函数[ ] squared-error 方差[ ] stationary 平稳性(不变性)[ ] stationary stochastic process 平稳随机过程[ ] step-size 步长值[ ] supervised learning 监督学习[ ] symmetric positive semi-definite matrix 对称半正定矩阵[ ] symmetry breaking 对称失效[ ] tanh function 双曲正切函数[ ] the average activation 平均活跃度[ ] the derivative checking method 梯度验证方法[ ] the empirical distribution 经验分布函数[ ] the energy function 能量函数[ ] the Lagrange dual 拉格朗日对偶函数[ ] the log likelihood 对数似然函数[ ] the pixel intensity value 像素灰度值[ ] the rate of convergence 收敛速度[ ] topographic cost term 拓扑代价项[ ] topographic ordered 拓扑秩序[ ] transformation 变换[200 ] translation invariant 平移不变性[ ] trivial answer 平凡解[ ] under-complete basis 不完备基[ ] unrolling 组合扩展[ ] unsupervised learning 无监督学习[ ] variance 方差[ ] vecotrized implementation 向量化实现[ ] vectorization 矢量化[ ] visual cortex 视觉皮层[ ] weight decay 权重衰减[ ] weighted average 加权平均值[ ] whitening 白化[ ] zero-mean 均值为零第二部分Letter A[ ] Accumulated error backpropagation 累积误差逆传播[ ] Activation Function 激活函数[ ] Adaptive Resonance Theory/ART 自适应谐振理论[ ] Addictive model 加性学习[ ] Adversarial Networks 对抗网络[ ] Affine Layer 仿射层[ ] Affinity matrix 亲和矩阵[ ] Agent 代理/ 智能体[ ] Algorithm 算法[ ] Alpha-beta pruning α-β剪枝[ ] Anomaly detection 异常检测[ ] Approximation 近似[ ] Area Under ROC Curve/AUC Roc 曲线下面积[ ] Artificial General Intelligence/AGI 通用人工智能[ ] Artificial Intelligence/AI 人工智能[ ] Association analysis 关联分析[ ] Attention mechanism 注意力机制[ ] Attribute conditional independence assumption 属性条件独立性假设[ ] Attribute space 属性空间[ ] Attribute value 属性值[ ] Autoencoder 自编码器[ ] Automatic speech recognition 自动语音识别[ ] Automatic summarization 自动摘要[ ] Average gradient 平均梯度[ ] Average-Pooling 平均池化Letter B[ ] Backpropagation Through Time 通过时间的反向传播[ ] Backpropagation/BP 反向传播[ ] Base learner 基学习器[ ] Base learning algorithm 基学习算法[ ] Batch Normalization/BN 批量归一化[ ] Bayes decision rule 贝叶斯判定准则[250 ] Bayes Model Averaging/BMA 贝叶斯模型平均[ ] Bayes optimal classifier 贝叶斯最优分类器[ ] Bayesian decision theory 贝叶斯决策论[ ] Bayesian network 贝叶斯网络[ ] Between-class scatter matrix 类间散度矩阵[ ] Bias 偏置/ 偏差[ ] Bias-variance decomposition 偏差-方差分解[ ] Bias-Variance Dilemma 偏差–方差困境[ ] Bi-directional Long-Short Term Memory/Bi-LSTM 双向长短期记忆[ ] Binary classification 二分类[ ] Binomial test 二项检验[ ] Bi-partition 二分法[ ] Boltzmann machine 玻尔兹曼机[ ] Bootstrap sampling 自助采样法/可重复采样/有放回采样[ ] Bootstrapping 自助法[ ] Break-Event Point/BEP 平衡点Letter C[ ] Calibration 校准[ ] Cascade-Correlation 级联相关[ ] Categorical attribute 离散属性[ ] Class-conditional probability 类条件概率[ ] Classification and regression tree/CART 分类与回归树[ ] Classifier 分类器[ ] Class-imbalance 类别不平衡[ ] Closed -form 闭式[ ] Cluster 簇/类/集群[ ] Cluster analysis 聚类分析[ ] Clustering 聚类[ ] Clustering ensemble 聚类集成[ ] Co-adapting 共适应[ ] Coding matrix 编码矩阵[ ] COLT 国际学习理论会议[ ] Committee-based learning 基于委员会的学习[ ] Competitive learning 竞争型学习[ ] Component learner 组件学习器[ ] Comprehensibility 可解释性[ ] Computation Cost 计算成本[ ] Computational Linguistics 计算语言学[ ] Computer vision 计算机视觉[ ] Concept drift 概念漂移[ ] Concept Learning System /CLS 概念学习系统[ ] Conditional entropy 条件熵[ ] Conditional mutual information 条件互信息[ ] Conditional Probability Table/CPT 条件概率表[ ] Conditional random field/CRF 条件随机场[ ] Conditional risk 条件风险[ ] Confidence 置信度[ ] Confusion matrix 混淆矩阵[300 ] Connection weight 连接权[ ] Connectionism 连结主义[ ] Consistency 一致性/相合性[ ] Contingency table 列联表[ ] Continuous attribute 连续属性[ ] Convergence 收敛[ ] Conversational agent 会话智能体[ ] Convex quadratic programming 凸二次规划[ ] Convexity 凸性[ ] Convolutional neural network/CNN 卷积神经网络[ ] Co-occurrence 同现[ ] Correlation coefficient 相关系数[ ] Cosine similarity 余弦相似度[ ] Cost curve 成本曲线[ ] Cost Function 成本函数[ ] Cost matrix 成本矩阵[ ] Cost-sensitive 成本敏感[ ] Cross entropy 交叉熵[ ] Cross validation 交叉验证[ ] Crowdsourcing 众包[ ] Curse of dimensionality 维数灾难[ ] Cut point 截断点[ ] Cutting plane algorithm 割平面法Letter D[ ] Data mining 数据挖掘[ ] Data set 数据集[ ] Decision Boundary 决策边界[ ] Decision stump 决策树桩[ ] Decision tree 决策树/判定树[ ] Deduction 演绎[ ] Deep Belief Network 深度信念网络[ ] Deep Convolutional Generative Adversarial Network/DCGAN 深度卷积生成对抗网络[ ] Deep learning 深度学习[ ] Deep neural network/DNN 深度神经网络[ ] Deep Q-Learning 深度Q 学习[ ] Deep Q-Network 深度Q 网络[ ] Density estimation 密度估计[ ] Density-based clustering 密度聚类[ ] Differentiable neural computer 可微分神经计算机[ ] Dimensionality reduction algorithm 降维算法[ ] Directed edge 有向边[ ] Disagreement measure 不合度量[ ] Discriminative model 判别模型[ ] Discriminator 判别器[ ] Distance measure 距离度量[ ] Distance metric learning 距离度量学习[ ] Distribution 分布[ ] Divergence 散度[350 ] Diversity measure 多样性度量/差异性度量[ ] Domain adaption 领域自适应[ ] Downsampling 下采样[ ] D-separation (Directed separation)有向分离[ ] Dual problem 对偶问题[ ] Dummy node 哑结点[ ] Dynamic Fusion 动态融合[ ] Dynamic programming 动态规划Letter E[ ] Eigenvalue decomposition 特征值分解[ ] Embedding 嵌入[ ] Emotional analysis 情绪分析[ ] Empirical conditional entropy 经验条件熵[ ] Empirical entropy 经验熵[ ] Empirical error 经验误差[ ] Empirical risk 经验风险[ ] End-to-End 端到端[ ] Energy-based model 基于能量的模型[ ] Ensemble learning 集成学习[ ] Ensemble pruning 集成修剪[ ] Error Correcting Output Codes/ECOC 纠错输出码[ ] Error rate 错误率[ ] Error-ambiguity decomposition 误差-分歧分解[ ] Euclidean distance 欧氏距离[ ] Evolutionary computation 演化计算[ ] Expectation-Maximization 期望最大化[ ] Expected loss 期望损失[ ] Exploding Gradient Problem 梯度爆炸问题[ ] Exponential loss function 指数损失函数[ ] Extreme Learning Machine/ELM 超限学习机Letter F[ ] Factorization 因子分解[ ] False negative 假负类[ ] False positive 假正类[ ] False Positive Rate/FPR 假正例率[ ] Feature engineering 特征工程[ ] Feature selection 特征选择[ ] Feature vector 特征向量[ ] Featured Learning 特征学习[ ] Feedforward Neural Networks/FNN 前馈神经网络[ ] Fine-tuning 微调[ ] Flipping output 翻转法[ ] Fluctuation 震荡[ ] Forward stagewise algorithm 前向分步算法[ ] Frequentist 频率主义学派[ ] Full-rank matrix 满秩矩阵[400 ] Functional neuron 功能神经元Letter G[ ] Gain ratio 增益率[ ] Game theory 博弈论[ ] Gaussian kernel function 高斯核函数[ ] Gaussian Mixture Model 高斯混合模型[ ] General Problem Solving 通用问题求解[ ] Generalization 泛化[ ] Generalization error 泛化误差[ ] Generalization error bound 泛化误差上界[ ] Generalized Lagrange function 广义拉格朗日函数[ ] Generalized linear model 广义线性模型[ ] Generalized Rayleigh quotient 广义瑞利商[ ] Generative Adversarial Networks/GAN 生成对抗网络[ ] Generative Model 生成模型[ ] Generator 生成器[ ] Genetic Algorithm/GA 遗传算法[ ] Gibbs sampling 吉布斯采样[ ] Gini index 基尼指数[ ] Global minimum 全局最小[ ] Global Optimization 全局优化[ ] Gradient boosting 梯度提升[ ] Gradient Descent 梯度下降[ ] Graph theory 图论[ ] Ground-truth 真相/真实Letter H[ ] Hard margin 硬间隔[ ] Hard voting 硬投票[ ] Harmonic mean 调和平均[ ] Hesse matrix 海塞矩阵[ ] Hidden dynamic model 隐动态模型[ ] Hidden layer 隐藏层[ ] Hidden Markov Model/HMM 隐马尔可夫模型[ ] Hierarchical clustering 层次聚类[ ] Hilbert space 希尔伯特空间[ ] Hinge loss function 合页损失函数[ ] Hold-out 留出法[ ] Homogeneous 同质[ ] Hybrid computing 混合计算[ ] Hyperparameter 超参数[ ] Hypothesis 假设[ ] Hypothesis test 假设验证Letter I[ ] ICML 国际机器学习会议[450 ] Improved iterative scaling/IIS 改进的迭代尺度法[ ] Incremental learning 增量学习[ ] Independent and identically distributed/i.i.d. 独立同分布[ ] Independent Component Analysis/ICA 独立成分分析[ ] Indicator function 指示函数[ ] Individual learner 个体学习器[ ] Induction 归纳[ ] Inductive bias 归纳偏好[ ] Inductive learning 归纳学习[ ] Inductive Logic Programming/ILP 归纳逻辑程序设计[ ] Information entropy 信息熵[ ] Information gain 信息增益[ ] Input layer 输入层[ ] Insensitive loss 不敏感损失[ ] Inter-cluster similarity 簇间相似度[ ] International Conference for Machine Learning/ICML 国际机器学习大会[ ] Intra-cluster similarity 簇内相似度[ ] Intrinsic value 固有值[ ] Isometric Mapping/Isomap 等度量映射[ ] Isotonic regression 等分回归[ ] Iterative Dichotomiser 迭代二分器Letter K[ ] Kernel method 核方法[ ] Kernel trick 核技巧[ ] Kernelized Linear Discriminant Analysis/KLDA 核线性判别分析[ ] K-fold cross validation k 折交叉验证/k 倍交叉验证[ ] K-Means Clustering K –均值聚类[ ] K-Nearest Neighbours Algorithm/KNN K近邻算法[ ] Knowledge base 知识库[ ] Knowledge Representation 知识表征Letter L[ ] Label space 标记空间[ ] Lagrange duality 拉格朗日对偶性[ ] Lagrange multiplier 拉格朗日乘子[ ] Laplace smoothing 拉普拉斯平滑[ ] Laplacian correction 拉普拉斯修正[ ] Latent Dirichlet Allocation 隐狄利克雷分布[ ] Latent semantic analysis 潜在语义分析[ ] Latent variable 隐变量[ ] Lazy learning 懒惰学习[ ] Learner 学习器[ ] Learning by analogy 类比学习[ ] Learning rate 学习率[ ] Learning Vector Quantization/LVQ 学习向量量化[ ] Least squares regression tree 最小二乘回归树[ ] Leave-One-Out/LOO 留一法[500 ] linear chain conditional random field 线性链条件随机场[ ] Linear Discriminant Analysis/LDA 线性判别分析[ ] Linear model 线性模型[ ] Linear Regression 线性回归[ ] Link function 联系函数[ ] Local Markov property 局部马尔可夫性[ ] Local minimum 局部最小[ ] Log likelihood 对数似然[ ] Log odds/logit 对数几率[ ] Logistic Regression Logistic 回归[ ] Log-likelihood 对数似然[ ] Log-linear regression 对数线性回归[ ] Long-Short Term Memory/LSTM 长短期记忆[ ] Loss function 损失函数Letter M[ ] Machine translation/MT 机器翻译[ ] Macron-P 宏查准率[ ] Macron-R 宏查全率[ ] Majority voting 绝对多数投票法[ ] Manifold assumption 流形假设[ ] Manifold learning 流形学习[ ] Margin theory 间隔理论[ ] Marginal distribution 边际分布[ ] Marginal independence 边际独立性[ ] Marginalization 边际化[ ] Markov Chain Monte Carlo/MCMC 马尔可夫链蒙特卡罗方法[ ] Markov Random Field 马尔可夫随机场[ ] Maximal clique 最大团[ ] Maximum Likelihood Estimation/MLE 极大似然估计/极大似然法[ ] Maximum margin 最大间隔[ ] Maximum weighted spanning tree 最大带权生成树[ ] Max-Pooling 最大池化[ ] Mean squared error 均方误差[ ] Meta-learner 元学习器[ ] Metric learning 度量学习[ ] Micro-P 微查准率[ ] Micro-R 微查全率[ ] Minimal Description Length/MDL 最小描述长度[ ] Minimax game 极小极大博弈[ ] Misclassification cost 误分类成本[ ] Mixture of experts 混合专家[ ] Momentum 动量[ ] Moral graph 道德图/端正图[ ] Multi-class classification 多分类[ ] Multi-document summarization 多文档摘要[ ] Multi-layer feedforward neural networks 多层前馈神经网络[ ] Multilayer Perceptron/MLP 多层感知器[ ] Multimodal learning 多模态学习[550 ] Multiple Dimensional Scaling 多维缩放[ ] Multiple linear regression 多元线性回归[ ] Multi-response Linear Regression /MLR 多响应线性回归[ ] Mutual information 互信息Letter N[ ] Naive bayes 朴素贝叶斯[ ] Naive Bayes Classifier 朴素贝叶斯分类器[ ] Named entity recognition 命名实体识别[ ] Nash equilibrium 纳什均衡[ ] Natural language generation/NLG 自然语言生成[ ] Natural language processing 自然语言处理[ ] Negative class 负类[ ] Negative correlation 负相关法[ ] Negative Log Likelihood 负对数似然[ ] Neighbourhood Component Analysis/NCA 近邻成分分析[ ] Neural Machine Translation 神经机器翻译[ ] Neural Turing Machine 神经图灵机[ ] Newton method 牛顿法[ ] NIPS 国际神经信息处理系统会议[ ] No Free Lunch Theorem/NFL 没有免费的午餐定理[ ] Noise-contrastive estimation 噪音对比估计[ ] Nominal attribute 列名属性[ ] Non-convex optimization 非凸优化[ ] Nonlinear model 非线性模型[ ] Non-metric distance 非度量距离[ ] Non-negative matrix factorization 非负矩阵分解[ ] Non-ordinal attribute 无序属性[ ] Non-Saturating Game 非饱和博弈[ ] Norm 范数[ ] Normalization 归一化[ ] Nuclear norm 核范数[ ] Numerical attribute 数值属性Letter O[ ] Objective function 目标函数[ ] Oblique decision tree 斜决策树[ ] Occam’s razor 奥卡姆剃刀[ ] Odds 几率[ ] Off-Policy 离策略[ ] One shot learning 一次性学习[ ] One-Dependent Estimator/ODE 独依赖估计[ ] On-Policy 在策略[ ] Ordinal attribute 有序属性[ ] Out-of-bag estimate 包外估计[ ] Output layer 输出层[ ] Output smearing 输出调制法[ ] Overfitting 过拟合/过配[600 ] Oversampling 过采样Letter P[ ] Paired t-test 成对t 检验[ ] Pairwise 成对型[ ] Pairwise Markov property 成对马尔可夫性[ ] Parameter 参数[ ] Parameter estimation 参数估计[ ] Parameter tuning 调参[ ] Parse tree 解析树[ ] Particle Swarm Optimization/PSO 粒子群优化算法[ ] Part-of-speech tagging 词性标注[ ] Perceptron 感知机[ ] Performance measure 性能度量[ ] Plug and Play Generative Network 即插即用生成网络[ ] Plurality voting 相对多数投票法[ ] Polarity detection 极性检测[ ] Polynomial kernel function 多项式核函数[ ] Pooling 池化[ ] Positive class 正类[ ] Positive definite matrix 正定矩阵[ ] Post-hoc test 后续检验[ ] Post-pruning 后剪枝[ ] potential function 势函数[ ] Precision 查准率/准确率[ ] Prepruning 预剪枝[ ] Principal component analysis/PCA 主成分分析[ ] Principle of multiple explanations 多释原则[ ] Prior 先验[ ] Probability Graphical Model 概率图模型[ ] Proximal Gradient Descent/PGD 近端梯度下降[ ] Pruning 剪枝[ ] Pseudo-label 伪标记[ ] Letter Q[ ] Quantized Neural Network 量子化神经网络[ ] Quantum computer 量子计算机[ ] Quantum Computing 量子计算[ ] Quasi Newton method 拟牛顿法Letter R[ ] Radial Basis Function/RBF 径向基函数[ ] Random Forest Algorithm 随机森林算法[ ] Random walk 随机漫步[ ] Recall 查全率/召回率[ ] Receiver Operating Characteristic/ROC 受试者工作特征[ ] Rectified Linear Unit/ReLU 线性修正单元[650 ] Recurrent Neural Network 循环神经网络[ ] Recursive neural network 递归神经网络[ ] Reference model 参考模型[ ] Regression 回归[ ] Regularization 正则化[ ] Reinforcement learning/RL 强化学习[ ] Representation learning 表征学习[ ] Representer theorem 表示定理[ ] reproducing kernel Hilbert space/RKHS 再生核希尔伯特空间[ ] Re-sampling 重采样法[ ] Rescaling 再缩放[ ] Residual Mapping 残差映射[ ] Residual Network 残差网络[ ] Restricted Boltzmann Machine/RBM 受限玻尔兹曼机[ ] Restricted Isometry Property/RIP 限定等距性[ ] Re-weighting 重赋权法[ ] Robustness 稳健性/鲁棒性[ ] Root node 根结点[ ] Rule Engine 规则引擎[ ] Rule learning 规则学习Letter S[ ] Saddle point 鞍点[ ] Sample space 样本空间[ ] Sampling 采样[ ] Score function 评分函数[ ] Self-Driving 自动驾驶[ ] Self-Organizing Map/SOM 自组织映射[ ] Semi-naive Bayes classifiers 半朴素贝叶斯分类器[ ] Semi-Supervised Learning 半监督学习[ ] semi-Supervised Support Vector Machine 半监督支持向量机[ ] Sentiment analysis 情感分析[ ] Separating hyperplane 分离超平面[ ] Sigmoid function Sigmoid 函数[ ] Similarity measure 相似度度量[ ] Simulated annealing 模拟退火[ ] Simultaneous localization and mapping 同步定位与地图构建[ ] Singular Value Decomposition 奇异值分解[ ] Slack variables 松弛变量[ ] Smoothing 平滑[ ] Soft margin 软间隔[ ] Soft margin maximization 软间隔最大化[ ] Soft voting 软投票[ ] Sparse representation 稀疏表征[ ] Sparsity 稀疏性[ ] Specialization 特化[ ] Spectral Clustering 谱聚类[ ] Speech Recognition 语音识别[ ] Splitting variable 切分变量[700 ] Squashing function 挤压函数[ ] Stability-plasticity dilemma 可塑性-稳定性困境[ ] Statistical learning 统计学习[ ] Status feature function 状态特征函[ ] Stochastic gradient descent 随机梯度下降[ ] Stratified sampling 分层采样[ ] Structural risk 结构风险[ ] Structural risk minimization/SRM 结构风险最小化[ ] Subspace 子空间[ ] Supervised learning 监督学习/有导师学习[ ] support vector expansion 支持向量展式[ ] Support Vector Machine/SVM 支持向量机[ ] Surrogat loss 替代损失[ ] Surrogate function 替代函数[ ] Symbolic learning 符号学习[ ] Symbolism 符号主义[ ] Synset 同义词集Letter T[ ] T-Distribution Stochastic Neighbour Embedding/t-SNE T –分布随机近邻嵌入[ ] Tensor 张量[ ] Tensor Processing Units/TPU 张量处理单元[ ] The least square method 最小二乘法[ ] Threshold 阈值[ ] Threshold logic unit 阈值逻辑单元[ ] Threshold-moving 阈值移动[ ] Time Step 时间步骤[ ] Tokenization 标记化[ ] Training error 训练误差[ ] Training instance 训练示例/训练例[ ] Transductive learning 直推学习[ ] Transfer learning 迁移学习[ ] Treebank 树库[ ] Tria-by-error 试错法[ ] True negative 真负类[ ] True positive 真正类[ ] True Positive Rate/TPR 真正例率[ ] Turing Machine 图灵机[ ] Twice-learning 二次学习Letter U[ ] Underfitting 欠拟合/欠配[ ] Undersampling 欠采样[ ] Understandability 可理解性[ ] Unequal cost 非均等代价[ ] Unit-step function 单位阶跃函数[ ] Univariate decision tree 单变量决策树[ ] Unsupervised learning 无监督学习/无导师学习[ ] Unsupervised layer-wise training 无监督逐层训练[ ] Upsampling 上采样Letter V[ ] Vanishing Gradient Problem 梯度消失问题[ ] Variational inference 变分推断[ ] VC Theory VC维理论[ ] Version space 版本空间[ ] Viterbi algorithm 维特比算法[760 ] Von Neumann architecture 冯· 诺伊曼架构Letter W[ ] Wasserstein GAN/WGAN Wasserstein生成对抗网络[ ] Weak learner 弱学习器[ ] Weight 权重[ ] Weight sharing 权共享[ ] Weighted voting 加权投票法[ ] Within-class scatter matrix 类内散度矩阵[ ] Word embedding 词嵌入[ ] Word sense disambiguation 词义消歧Letter Z[ ] Zero-data learning 零数据学习[ ] Zero-shot learning 零次学习第三部分A[ ] approximations近似值[ ] arbitrary随意的[ ] affine仿射的[ ] arbitrary任意的[ ] amino acid氨基酸[ ] amenable经得起检验的[ ] axiom公理,原则[ ] abstract提取[ ] architecture架构,体系结构;建造业[ ] absolute绝对的[ ] arsenal军火库[ ] assignment分配[ ] algebra线性代数[ ] asymptotically无症状的[ ] appropriate恰当的B[ ] bias偏差[ ] brevity简短,简洁;短暂[800 ] broader广泛[ ] briefly简短的[ ] batch批量C[ ] convergence 收敛,集中到一点[ ] convex凸的[ ] contours轮廓[ ] constraint约束[ ] constant常理[ ] commercial商务的[ ] complementarity补充[ ] coordinate ascent同等级上升[ ] clipping剪下物;剪报;修剪[ ] component分量;部件[ ] continuous连续的[ ] covariance协方差[ ] canonical正规的,正则的[ ] concave非凸的[ ] corresponds相符合;相当;通信[ ] corollary推论[ ] concrete具体的事物,实在的东西[ ] cross validation交叉验证[ ] correlation相互关系[ ] convention约定[ ] cluster一簇[ ] centroids 质心,形心[ ] converge收敛[ ] computationally计算(机)的[ ] calculus计算D[ ] derive获得,取得[ ] dual二元的[ ] duality二元性;二象性;对偶性[ ] derivation求导;得到;起源[ ] denote预示,表示,是…的标志;意味着,[逻]指称[ ] divergence 散度;发散性[ ] dimension尺度,规格;维数[ ] dot小圆点[ ] distortion变形[ ] density概率密度函数[ ] discrete离散的[ ] discriminative有识别能力的[ ] diagonal对角[ ] dispersion分散,散开[ ] determinant决定因素[849 ] disjoint不相交的E[ ] encounter遇到[ ] ellipses椭圆[ ] equality等式[ ] extra额外的[ ] empirical经验;观察[ ] ennmerate例举,计数[ ] exceed超过,越出[ ] expectation期望[ ] efficient生效的[ ] endow赋予[ ] explicitly清楚的[ ] exponential family指数家族[ ] equivalently等价的F[ ] feasible可行的[ ] forary初次尝试[ ] finite有限的,限定的[ ] forgo摒弃,放弃[ ] fliter过滤[ ] frequentist最常发生的[ ] forward search前向式搜索[ ] formalize使定形G[ ] generalized归纳的[ ] generalization概括,归纳;普遍化;判断(根据不足)[ ] guarantee保证;抵押品[ ] generate形成,产生[ ] geometric margins几何边界[ ] gap裂口[ ] generative生产的;有生产力的H[ ] heuristic启发式的;启发法;启发程序[ ] hone怀恋;磨[ ] hyperplane超平面L[ ] initial最初的[ ] implement执行[ ] intuitive凭直觉获知的[ ] incremental增加的[900 ] intercept截距[ ] intuitious直觉[ ] instantiation例子[ ] indicator指示物,指示器[ ] interative重复的,迭代的[ ] integral积分[ ] identical相等的;完全相同的[ ] indicate表示,指出[ ] invariance不变性,恒定性[ ] impose把…强加于[ ] intermediate中间的[ ] interpretation解释,翻译J[ ] joint distribution联合概率L[ ] lieu替代[ ] logarithmic对数的,用对数表示的[ ] latent潜在的[ ] Leave-one-out cross validation留一法交叉验证M[ ] magnitude巨大[ ] mapping绘图,制图;映射[ ] matrix矩阵[ ] mutual相互的,共同的[ ] monotonically单调的[ ] minor较小的,次要的[ ] multinomial多项的[ ] multi-class classification二分类问题N[ ] nasty讨厌的[ ] notation标志,注释[ ] naïve朴素的O[ ] obtain得到[ ] oscillate摆动[ ] optimization problem最优化问题[ ] objective function目标函数[ ] optimal最理想的[ ] orthogonal(矢量,矩阵等)正交的[ ] orientation方向[ ] ordinary普通的[ ] occasionally偶然的P[ ] partial derivative偏导数[ ] property性质[ ] proportional成比例的[ ] primal原始的,最初的[ ] permit允许[ ] pseudocode伪代码[ ] permissible可允许的[ ] polynomial多项式[ ] preliminary预备[ ] precision精度[ ] perturbation 不安,扰乱[ ] poist假定,设想[ ] positive semi-definite半正定的[ ] parentheses圆括号[ ] posterior probability后验概率[ ] plementarity补充[ ] pictorially图像的[ ] parameterize确定…的参数[ ] poisson distribution柏松分布[ ] pertinent相关的Q[ ] quadratic二次的[ ] quantity量,数量;分量[ ] query疑问的R[ ] regularization使系统化;调整[ ] reoptimize重新优化[ ] restrict限制;限定;约束[ ] reminiscent回忆往事的;提醒的;使人联想…的(of)[ ] remark注意[ ] random variable随机变量[ ] respect考虑[ ] respectively各自的;分别的[ ] redundant过多的;冗余的S[ ] susceptible敏感的[ ] stochastic可能的;随机的[ ] symmetric对称的[ ] sophisticated复杂的[ ] spurious假的;伪造的[ ] subtract减去;减法器[ ] simultaneously同时发生地;同步地[ ] suffice满足[ ] scarce稀有的,难得的[ ] split分解,分离[ ] subset子集[ ] statistic统计量[ ] successive iteratious连续的迭代[ ] scale标度[ ] sort of有几分的[ ] squares平方T[ ] trajectory轨迹[ ] temporarily暂时的[ ] terminology专用名词[ ] tolerance容忍;公差[ ] thumb翻阅[ ] threshold阈,临界[ ] theorem定理[ ] tangent正弦U[ ] unit-length vector单位向量V[ ] valid有效的,正确的[ ] variance方差[ ] variable变量;变元[ ] vocabulary词汇[ ] valued经估价的;宝贵的[ ] W [1038 ] wrapper包装。
3-芯片数据的基本处理和分析
王丹 蒋 琰 阮陟
浙江加州国际纳米技术研究院(ZCNI)
课程内容
实习一 实习二 基因组数据注释和功能分析 核苷酸序列分析
基因组学 系 统 生 物 学
实习三
实习四 实习五 实习六
芯片数据的基本处理和分析
蛋白质结构与功能分析 蛋白质组学数据分析
转录物组学
蛋白质组学
待状态栏显示“Converting is successful”后, 格式转换完 成。此时在原genepix存放的文件夹中会出现文件名相同 但扩展名不同的.mev和.ann的文件。
input
output
程序运行前
程序运行结果
MEV文件:MEV格式的芯片数据
MEV注释文件(后缀名为.ann)
课堂练习
系统生物学软件实习
芯片数据分析的一般流程:
1. 芯片杂交实验 ,芯片数据采集(读取扫描图) 2. 数据基本处理 3. 数据提交公共数据库 4. 数据生物信息学分析
实习内容:
• TIGR TM4 软件的介绍和使用 • GenMAPP软件的介绍和使用 • GEO数据库的介绍
常见的双通道(dual channel)实验流程:
GenMAPP基本概念
• MAPP:描述了模式生物的代谢途径图。 目 前 MAPP 数 据 库 中 包 含 了 人 (H.sapiens) 、 小 鼠 (M.musculus)、大鼠 (R.norvegicus)、酵母 (S.cerevisiae)、 线虫 (C.elegans)、狗 (C.familiaris)、鸡 (G.gallus)、牛 (B.taurus)、果蝇 (D.melanogaster)和斑马鱼 (D.rerio)等 模式生物。
英汉对照计量经济学术语
英汉对照计量经济学术语A校正R2〔Adjusted R-Squared〕:多元回归剖析中拟合优度的量度,在估量误差的方差时对添加的解释变量用一个自在度来调整。
统一假定〔Alternative Hypothesis〕:检验虚拟假定时的相对假定。
AR〔1〕序列相关〔AR(1) Serial Correlation〕:时间序列回归模型中的误差遵照AR〔1〕模型。
渐近置信区间〔Asymptotic Confidence Interval〕:大样本容量下近似成立的置信区间。
渐近正态性〔Asymptotic Normality〕:适当正态化后样本散布收敛到规范正态散布的估量量。
渐近性质〔Asymptotic Properties〕:当样本容量有限增长时适用的估量量和检验统计量性质。
渐近规范误〔Asymptotic Standard Error〕:大样本下失效的规范误。
渐近t 统计量〔Asymptotic t Statistic〕:大样本下近似听从规范正态散布的t 统计量。
渐近方差〔Asymptotic Variance〕:为了取得渐近规范正态散布,我们必需用以除估量量的平方值。
渐近有效〔Asymptotically Efficient〕:关于听从渐近正态散布的分歧性估量量,有最小渐近方差的估计量。
渐近不相关〔Asymptotically Uncorrelated〕:时间序列进程中,随着两个时点上的随机变量的时间距离添加,它们之间的相关趋于零。
衰减偏误〔Attenuation Bias〕:总是朝向零的估量量偏误,因此有衰减偏误的估量量的希冀值小于参数的相对值。
自回归条件异方差性〔Autoregressive Conditional Heteroskedasticity, ARCH〕:静态异方差性模型,即给定过去信息,误差项的方差线性依赖于过去的误差的平方。
一阶自回归进程[AR〔1〕]〔Autoregressive Process of Order One [AR(1)]〕:一个时间序列模型,其以后值线性依赖于最近的值加上一个无法预测的扰动。
热分析动力学三因子求算的比较法及其在光电聚合物材料中的应用
摘 要 本文概述了两类典型光电聚合物材料的发展现状介绍了热分析动力学研究非等温固相热分解反应的数学处理方法使计算数据更为准确在一定程度上拓宽了热分析动力学在该领域的应用已渐渐的被研究者所淘汰然而经典的等转化率法在求算中大多都引入了积分近似并且所得结果不涉及可能的机理函数用迭代法求算出较为准确的活化能并用其制约经典的积微分法所得的结果进而判断出反应动力学的模型实验证明该法合理可行并具有一定的广普性根据不同温度速率下转化率利用SPSS软件测试证明S形函数是其较为理想的拟合函数Y拟合所得线性回归的相关系数更为理想3. 利用热分析技术研究了含不同间隔基的卟啉聚合物和聚(4-乙烯基吡啶)与木质素共混薄膜的两个系列光电聚合物的具有特色的热行为及其热分解过程关键词热分析动力学热行为ABSTRACTIn this paper the present situation of the two typical photoelectric polymers and the application of the thermal analysis technique in the study of the nonlinear optical polymers were introduced. The mathematical method of studying non-isothermal decomposition reaction with thermal analysis kinetics was described and we wrote and improved the computer program of computation for computing the kinetic triplets to get the reliable results. The thermal behavior and decomposition kinetic of two series of photoelectric polymers were studied by using the comparative method we proposed.1. The conventional single scan method, which cannot detect the complex nature of the solid state reaction, has been replaced by multiple scan method or iso-conversional method. But the most classical iso-conversional methods are based on the assumption concerning the temperature integral, which will bring the homologous error and cannot detect the complex nature of the solid state reaction. We described a comparative method to investigate the reliable kinetic triplets. The kinetic triplets can be evaluated from the results obtained by differential and integral equations at only one heating rate with the constraint of the activation energy calculated by iterative method. The calcium oxalate monohydrate and ammonium oxalate monohydrate wereselected to be studied by the proposed method. Results show that this method is feasible, conventional with good reproducibility and can be used broadly.2. We have written the computer program of the thermal analysis kinetic. The plot of the conversion á for differential temperatures with the corresponding temperatures T was a S shape. The function of this S shape was tested by using SPSS software that it was a better fitting function YPVP第一章 两类光电聚合物材料的研究历史及应用 第一节 高性能二阶非线性光学聚合物材料的研究进展 非线性光学也就是强光光学介质中束缚较弱的价电子为强光电场所极化介质的电极化强度p与入射光的光强E有以下关系(1)E0(3)E*E*E0为真空介电常数(1)为线性极化率(2)(3)(n)分别为二阶对于普通光源而在激光作用下非线性不能忽略称为非线性光学效应(2)项不为零时所表现出来的效应便称之为二阶非线性光学效应 70年代末极化聚合物概念的提出开辟了二阶非线性光学材料研究的全新领域将具有大的微观一阶超极化率的有机分子 (又称生色团)通过掺杂或化学键合在聚合物之中并加以强直流电场实现非中心对称经此处理后的聚合物称为极化聚合物极化聚合物体系从首例报道距今已经十年多了从化学组成上看主要有聚酰亚胺类聚氨酯类从结构特点来看又可大致分为主侧链型交联型四类通过将非线性光学系数较高的有机分子和聚合物进行混合由于有机分子含量相对较少 最早的掺杂体系是1982年文献报道的染料DANS和液晶共聚物(2)为 31992年Valley等人[3]制备以了聚酰亚胺LQ2200为主体此后利用掺杂型PI制得了第一个全PI的Mach Zehnder干涉仪 文献[5]报道了利用介电松弛光谱来研究铁电侧链液晶高聚物(FLCP)与NLO染料的主客体混合物体系的介电松弛行为利用差示扫描量热法X射线衍射法测定了这些主客体物质的液晶相2. 侧链型 为了克服主客体型聚合物材料的缺点这种体系的优点较明显可以实现高浓度非线性光学基团接枝相分离和在聚合物中形成浓度梯度含有相同浓度的生色基团的侧链型聚合物体系Tg明显高于主客体体系如何将高非线性光学特性的NLO生色分子键接到Tg的聚合物骨架上 2.1 聚酰亚胺类IBM公司采用先合成非线性光学生色团功能化了的二胺单体制出了可承受数小时350大大改善了聚合物非线性光学特性的稳定性[6]DR1用旋涂法制备出了高聚物薄膜并用电晕技术使其在200极化后其非线性光学系数d33接近60pm/V发现改性聚酰亚胺在100中国科学院感光化学研究所和南开大学化学系合作报道了八种含偶氮类非线性光学活性侧基的聚酰亚胺材料的合成与表征聚合物中的发色团含量最高接近100﹪[8]用UV-Vis可见吸收光谱法测定了该体系的取向稳定性并用一维刚性取向气体模型估算了二阶非线性光学系数结果表明该极化聚合物具有较好的高温取向稳定性 48150后变化仅为 12% 文献[10]报道了有关侧链型含有DR1或NPP基团的聚酰亚胺(PI-DR1和PI-NPP)的合成与表征一般的Tg如以4,46FDA-二(3-氨基-4-羟基-苯基)六氟丙烷为含氟单体的羟基聚酰亚胺通过发色团上的共价键与聚酰亚胺主链相连[11]m处测定)南京大学成功地合成出了一种侧链含非线性光学单元的主侧链型共聚酯将刚性介晶基元引入高分子主链以提高Tg以期改善NLO单元的取向和提高取向的稳定性[12]所合成的含偶氮基团与介晶基团的聚酯型高分子均为结晶性无规共聚物而熔融温度则随聚合物中偶氮含量的增加而降低各聚合物均具有很好的热稳定性谢洪泉等人[14]合成了含不同分子超级化率的偶氮发色团作为侧链的四种聚氨酯NLO发色团的极化PU的且发色团的不同其玻璃化转变温度不同Ki Hong Park等人[15]成功的合成了含有一个该单体作为非线性光学发色团具有很好的热稳定性这些聚氨酯的热稳定性通过热重分析和IR光谱分析进行表征并与其他含有一般悬吊的含氮发色团的聚氨酯进行比较 2.4 侧链液晶聚合物并与甲基丙烯酸甲酯共聚谌东中等人[17]用熔融缩聚法合成了含非线性光学活性硝基偶氮苯液晶基元的聚丙二酸酯侧链液晶聚合物NMR对其结构进行了表征该聚合物为近晶型液晶聚合物-二羟基联苯二溴己烷氯代乙醇为原料合成了两种单体完成了单体的聚合和共聚对其结构进行了表征3. 主链型 如果将发色团引入聚合物主链中从而提高取向极化的稳定性发色团的转动需要牵动大链段用柔性链连接起来的发色团有更好的柔顺性和良好的力学性能中山大学高分子研究所合成了一类含双羟基的偶氮染料PURa Y S等人[20]在分散红DR19的硝基邻位引入一个羟甲基的偶极单体DRTO将氰基亚撑-2-甲基-5-(4-二甲基氨基苯乙烯基)-4H-吡喃(DCM)单元引入到刚性聚氨脂后 对于功能化的二阶非线性光学聚酰亚胺的研究的范围在205-224 文献[23]报道了含有用氰基磺酰基功能化了的偶氮发色团的聚酰亚胺(PI-SOT)的制备以及在非线性光学中的应用DSCTGA其玻璃化转变温度T为186时开始发生降解150 pm/V28pm/V该值在150 4. 交联型 关于交联型的极化聚合物目前在国内外已有很多的报道生成多羟基低聚物生成相应的交联型二阶 NLO聚合物发现从硝基联苯胺出发的低聚物和聚合物Tg 相对较高 对于聚氨酯与分散红19的交联体系的光极化极化后样品的诱导磁化率d33为46pm/V实验证实据报道羟基的甲基丙烯酸酯共聚物Ki Hong Park等人制备了[27]一类新型的自交联非线性光学共聚物(PGBz)并将这些共聚物的热性能与含1,2-二苯乙烯发色团的共聚物 (PGSt) 作了比较 范围内在热处理后PGBz共聚物的热稳定性有所提高交联的PGBz的热稳定性比交联的PGSt好 利用差示扫描量热法和热重分析法测定发现一些新型发色团用双马来酰亚胺联苯甲烷进行热处理后可以提供一系列交联的并具有改性发色团的双马来酰亚胺树脂即使升温到300将互穿网络BMI树脂两端的反应基团与发色团相连可提高物质的热稳定性[28]在二阶非线性光学极化聚合物实用化方面提高NLO材料的热稳定性是目前人们大量研究工作的目的具有高Tg温度的高分子材料所构成的电光器件将有较高的取向稳定性第二节 导电高分子在光电材料领域中的研究进展 传统的有机化合物由于分子间的相互作用弱因而过去一直只注重高分子材料的力学性能和化学性能聚乙炔 (PA)化学掺杂后电导率急剧增加这一研究成果为有机高分子材料的应用开辟了一个全新的领域一个新型多学科交叉的领域并成为20世纪后期材料科学领域的研究热点[29]使其由绝缘体转变为导体的一类高分子的统称它们具有导电性同时还具有一系列光学性能电致变色性因此[30]如光信号处理电致荧光显示等1. 高分子材料导电机理 根据能带理论形成分子轨道最高占有能带与最低占有能带之间的能量空间被称为能带间隙EEg是价带与g导带之间的能量差异导带层没有电子但通过掺杂可大大提高其导电能力从而产生空穴阳离子自由基其能量介于价带层和导带层之间一个极化子的自旋为1/2但若这个第二个电子是从极化子上夺取的 极化子和双极化子可通过双键迁移沿共轭链传递就产生越多的极化子或双极化子高浓度掺杂物可以促进极化子的移动能力同时可以增加载流子的数目从而使材料导电[31]有机非线性光学材料作为电光器件的新材料得到迅速发展优良的器件制备性质而且因为品种类别众多快速响应和高的三阶非线性光学系数(3)目前2.1 聚乙炔及其衍生物类 众所周知而且是一种典型的NLO共轭聚合物但近年来有关PA非线性光学性能的报道也趋增多m的激光测定了厚度为100nm 的无规反式PA的三次谐波(THG)(3)比PDA还大随后他们对全反式和全顺式PA在一较宽的频率范围内 (0.5(3)至少比顺式高 1个数量级[33]1.5e V之间的THG测定结果,在0.91e V处观察到了双光子吸收峰并观察到-9esu(共振值)了在0.65eV处整个测定范围内最大的1 0 实验证明PA的其二阶分子超级化率 (且同时他们还发现导电高分子的聚合度大于25后(3)将不再增加[35]在光电子技术中光学双稳态光计算和光纤通讯等方面引起人们极大的兴趣聚噻吩10-9esu)用Maker条纹的方法测得在1.907(3)为3.5而利用Kerr效应法研究发现PTh齐聚物的三阶非线性光学效应最大值可达到4.3除此之外采用四波混频法技术对PTh衍生物聚异硫茚 (PITN)的NLO性能进行研究) 最近聚叔丁基异硫茚 (PTBITN)其具有很高的文献[40]采用三次谐波技术结果发现(3)值提高的幅度并不大因此应设法改善共轭高分子的主链结构(3)值达到4 由聚(3-[2-((S)-2-甲基丁氧基)乙基]噻吩)和硬脂酸以不同分子比率构成的混合单层制备而成的手性HT-P(S)MBET/ SALB膜的电导率为10-4 ̄10-5S当谐波波长在吸收范围内时(3)值会快速的提高10-7esu(3)值要大的多[42]PANI理论研究表明聚苯胺具有较大的三阶非线性光学系数[43]优异的物理化学性能热稳定性,且价格低廉使之成为最有希望在实际中应用的共轭高分子材料(3))的影响在入射光波长为1.86PEMB10-11esu(3)的关系可以看出其文献[45]报道了PAn溶液及其在硅胶基质中的10-11esu和4.8国内研究人员用四波混频法系统地研究了PANI在NMP溶剂中的三阶非线性效应与溶液浓度掺杂程度及掺杂剂的性质等的依赖关系PANI链上醌环上的光激发是PANI/NMP溶液三阶非线性光学效应的主要原因[46]窄分布高取向且性能稳定的PPV聚合物其三阶非线性光学性能实验证明PPV沿着拉伸方向可得到相当高的10-10esu)垂直于拉伸方向时(3)值最小人们还发现当在苯环上引入一个甲氧基(3)值为79高于PPV增加了利用Wessling前聚物法合成的含有非线性光学发色团甲氧基亚硝基菧的PPV衍生物(3)值为1.7利用了锍聚合电解质法合成的分子结构为PPV溴4BrPPVClPPV(3)值10-10esu10-10esu10-10esu[49]MEH-PPVDO-PPV(聚-10-(2,5-二辛氧基-1,4-聚对苯乙烯撑))在非共振条件下的10-10和4.1这些PPV的衍生物都具有很好的NLO性能PPV的三阶非线性光学性质来自于PPV的相对较短的因此对进一步开发和优化有光学应用价值的PPV材料国内有关专家合成出了两种长链三苯环OPVs基团通过将长链基团连接到PPV上可以大大提高PPV的溶解性有望成为新的环保型材料[51]信息虽然无机的发光理论及制备工艺较成熟高驱动电压稳定性较差以及难以解决短波长 (蓝光)发光材料等诸多问题1990年聚苯撑乙炔作为高分子(有机)发光二极管的发光[52]材料在电场的作用下发出了亮丽的黄绿光与无机半导体电致发光器体相比,高分子电致发光材料具有价廉启动电压较低效率较高绿这些突出的优点无疑使高分子聚合物将成为最具有商业化前景的电致发光首选材料电子传输层下面分别介绍比较典型导电高分子在EL中的应用对于电子传输能力强的发光材料制备的器件需引入空穴传输(HTL)光稳定性和较好的成膜能力以外PPV及其衍生物是目前导电高分子电发光研究的重点因而在EL装置中又可充当空穴运输层以 PPV为空穴传输层制成了双层LEDs而采用单层装置 利用新的n-型的导电高聚物聚(p-吡啶乙撑基)(PPyV)和PPV作为电致发光装置中的空穴传输层 3.2 电子传输材料 常用的高分子材料的空穴迁移率远大于电子迁移率人们不断设计并合成出新的高分子电子传输材料其量子效率为0.25%[55](1) 400(2)具有较高的电导率(4)材料稳定 聚对苯炔 (PPV)是第一个被用作发光层的聚合物电致发光及三阶非线性光电性能剑桥大学用聚对苯乙炔 (PPV)作发光层实现了红绿多色发光显示[57]以Mg制备了发红橙光的LEDs随着烷基链长的增加实验发现在反向电压下他们以P3OT为发光层正向启动电压为3V研究人员以PCHTPTOPT等噻吩的衍生物为电致发光材料获得了蓝光橙光和红光器件[60] 图1-1 几种噻吩的衍生物材料的结构图 二甲锡烷基噻吩与其二溴苯取代的衍生物在钯催化的发生下发生缩合反应生成噻吩基通过旋涂法在铟锡氧化物(ITO)上形成均匀的薄膜LED发出橙红色的光且具有很高的电流整流率103 Fujii等人[62]对PPV和含有甲氧基取代基的可溶性PPV乙烯撑)(OOPPV)和聚(2,5-二壬氧基-p-苯乙烯撑)(NOPPV)进行了研究3.4 导电高分子正负极材料 1992年其结果表明电致发光器件的性能提高了许多50%30%开发了PAn在发光材料中的应用新前景并研究了导电高聚物厚度对(EL)装置的开启电压4. 发光聚合物 自从发现聚对苯撑乙烯具有电致发光特性并制备出聚合物发光二极管[65]以来发光聚合物材料和聚合物电致发光器件受到了广泛的重视它的掺杂态具有导电性因此也属于导电聚合物材料的一种已制备出多种具有不同结构特征的发光聚合物材料这些发光聚合物中最具代表性的有PPV及其多种可溶性衍生物PTh红光PPP蓝光PFO绿光到蓝光发出蓝-绿色光且强于用聚(二甲氧基-p-苯乙烯撑)(ROPPV)和蒽形成的共聚物构成同类装置发出的光5. 展望 尽管导电高分子研究仅有20余年的历史掺杂和导电机理加工性和稳定性以及在技术上的应用探索等方面均已取得了长足的进展然而导电高分子面临着在应用基础和技术应用方面纳米化和实用化的挑战必将成为21世纪材料科学的研究前沿测量物质的物理性质与温度关系的一种技术相态变化和吸附等)和化学变化 (脱水氧化和还原等)分析和选择随着信息时代的到来光处理和光计算机等领域取得了飞速发展同时也提出了更高的要求这种弛豫与决定聚合物链运动的玻璃化转变温度Tg直接关联的[68]而且Tg也是高聚物作为结构材料的一个最基本的参数随之极化温度也必然要高生色团必须具有更高的分解温度Td人们采用各种方法来获得更大的非线性光学系数和提高生色基团极化取向的热稳定性1提高聚合物的玻璃化转变温度热分析手段具有简单精确等特点热稳定性与液晶极化聚合物相转变行为研究中的应用1. 热分析法在提高聚合物物材料Tg以及热稳定性方面的应用 Rong-Ho Lee等人[69]将非线性光学的活性三-氧化磷和三-氧化磷发色团分别与聚羟基苯乙烯掺杂(PSOH)组成具有二阶非线性光学性能的体系这些NLO活性氨基氧化膦和高聚物基体在400nm下都具有光透性以及良好的热稳定性 (T 345由于发色团良好的热稳定性使其在不断升温的极化过程中不易发生分解以引入较大刚性单体为发色团来提高聚合物的Tg使聚合物薄膜在玻璃化转变温度附近被电场极化该聚合物兼具PU溶解性和成膜性能好以及 PI玻璃化转变温度较高 [71]选取了一种综合性能 (包括热稳定性透明性)相对较隋郁等人优的三嗪环类发色团分子作为二胺单体研究了其热稳定性及发色团分子含量对材料性能的影响它们的 5%热失重温度 (T5)都比相应聚合物的Tg高出100K以上 谌东中等人[72]将刚性介晶基元引入高分子主链以提高Tg并创造各向异性介质环境合成了一种侧链含非线性光学单元的主侧链型共聚酯4-氨基苯基NSTDAPI利用DSC测定了该聚合物的T达到304生色团单体与聚合物主链的连接不含柔性链段的提高TGA其有利于TTGA曲线证明了聚酰亚胺骨架上含有多芳结构噻吩环使聚合物具有较好的热性能从DSC曲线来看186这主要源于NLO发色团和进一步发生的功能化作用说明了所有的高聚物都是无定形的TGA测定结果发现由于偶氮基团的裂解使得高聚物在温度为190间开始发生降解时这是因为高聚物结构中有高芳香性的环利用热重分析和差示扫描量热法对NLO聚酰亚胺的热稳定性进行了测定一般在161之间/min的条件下测定的处失重是由于NLO生色团的分解所引起的以上这类聚合物的热化学稳定性是利用TGA手段来检测的其失重5%时的温度大大的超过了300Hwan Kyu Kim [77]将通过缩聚法制成了聚酰胺酰亚胺的非线性光学活性发色团TGADSC其Tg范围在142之间以上仍具有相当的热稳定性以上才发生分解该聚合物在升温过程中仅能在两吸热峰间观察到结晶性高聚物典型的岩粒状织构样品熔化并成为各向同性液体而无液晶相存在而高温吸热峰则是由样品结晶熔融吸热引起的而结晶熔融温度则随偶氮基团含量的增加而降低随着聚合物中偶氮基团含量的增加聚合物链刚性增强另一方面且随着聚合物中偶氮基团含量的增加因而引起结晶熔融温度随偶氮基团含量的增加而降低NLO染料的掺杂程度低于10wt﹪时没有相的变化吸热峰或放热峰峰尖处对应的就是热转化随着样品中NLO染料量的增多 梁旭霞等人[80]研究了光引发共聚合然后再分别与4-硝基-4制备了两种侧链含偶氮苯生色团的液晶聚合物TGA对聚合物介晶相转变温度结果表明液晶相转变温度较低聚合物热稳定性较好以上-甲氧基联苯-4-氧基)戊基]酯 (M5 MPP)并完成了单体的聚合和共聚利用DSC法研究了M5MPP结果表明PMMEANB属于非晶性高分子并证实了分子间吸电子与给电子基团相互作用有利于提高液晶高分子热稳定性3. 热分析法在高聚物交联体系中的应用 山东大学孟凡青等人[82]合成了双芪唑盐的衍生物作为生色团与2 ,4-二异氰酸酯甲苯及三乙醇胺反应并生成了交联的聚氨酯体系说明生色团参与聚合反应形成了无定形的聚合物这是因为分子的交联抑制了链段运动高温下体系只会受热分解将这些共聚物的热性能与含1,2-二苯乙烯发色团的共聚物 (PGSt) 作了比较 范围内高聚物中苯并唑发色团的增加这种现象可以解释为如果反应活性高的甲基丙烯酸缩水甘油酯单体含量减少从热重分析的结果来看这是因为在热处理过程中发生了交联高聚物的互穿网络Luo Jingdong 等人[84]合成出了一些在分子一端或两端连接烯炳基的含氮发色团,并利用DSC和TG法测定发现这些发色团用双马来酰亚胺联苯甲烷进行热处理后可以生成一系列交联的并具有改性的发色团的双马来酰亚胺树脂该树脂也不会发生分解. Xie Hong-Quan 等人[85]研究了含偶氮苯并噻唑发色团的两种非线性光学交联高聚物的热性能第四节 选题思想 近年来因为它们具有良好的电光特性低廉的价格易于发展新的器件和产品材料的热稳定性和其他热性能也是相当重要的参数热分析方法以其精确简易的特点扮演了重要的角色测量物质的物理性质与温度关系的一种技术相态变化和吸附等)和化学变化 (脱水氧化和还原等)分析和选择热稳定性以及该类聚合物相转变行为的研究热分析动力学是通过对物质热分解反应动力学进行分析从而得到相应的动力学和热力学参数热稳定等热性能提供有用的参考数据它们热性能和热行为对其性能的判定有着重要的意义得到热焓H等热力学参数和活化能n来推断键的性质和键的相对强弱从而对材料的进一步应用提供有价值的数据探讨了一种新的合理该方法最主要的特点是1因而尽可能的减小了积分法中因为引入积分近似而带来的误差值与迭代法2应用传统的积分法和微分法计算出的E或KAS法求出的值最接近用这种新的方法对经典样品草酸钙的脱水反应的动力学进行了求取因而可以说这种方法是可行的由实验得出热分析数据从而为该聚合物的热性能键能等提供有用的信息参考文献 [1] 罗敬东等.极化聚合物电光材料研究进展[J].高分子通报, 2000,1:9 [2] Meredith ,et al. Synthesis and Optical characterization of Liquid Crystalline Polymers for Electro-optical Applications[J]. Polymer Preprints, 1982 , 2 (23):149 [3] Valley J F, et al. Thermoplasticity and parallel-plate poling of electro-optic polyimide host thin films[J]. Appl Phy Lett, 1992 , 2 (60):160 [4] Hua Shu Wu, et al. Real time poling vapor co-deposition of dye doped second order nonlinear optical polymer thin films[J]. Macromolecules, 1997, 30 :4410 [5] Ging-Ho Hsiue, Rong-Ho Lee, Ru-Jong Jeng et al. Dielectric study of a ferroelectric side-chain liquid crystalline polysiloxane with a broad temperature range of the chiral smectic C phase: 2. Doping effect of a non-linear optically active dye[J].Polymer,1997, 38 (4): 887-895 [6] Verbiest T, burland D M, Cjurich M, et al. Exceptionally thermally stable polyimides for second-order nonlinear optical applications[J]. Science, 1995, 268:1604 ̄1606 [7] Marestin C, Mercier R, Sillion B et al. High glass transition temperature electro-optic side-chain polymers[J]. Synthetic Metal,1996,81: 14-146 [8] 麻洪张志谦甘湘萍高分子材料科学与工程.2000, 16 (4): 36et al. Nonlinear optical polymers with novel benzoxazole chromophores II. Synthesis of polyurethanes with good thermal stability[J]. Reative & Functional Polymers,1996,30: 375-383 [16] 胡权芳蔡兴贤. 侧链液晶丙烯酸酯聚合物的合成与表征[J]. 四川大学学报姜旭卫赵晓光740-743 [20] Ra Y S, Mao S S H, Dalton L R D, et al. Thermoset second-order NLO material from a rifunctionalized chromophore[J]. Polymer Preprints, 1997,38(1): 926 [21] Chong-Bok Yoon, Byung-Jun Jung, Hong-Ku Shim. Synthesis and second-harmonic generation study of DCM-containing polyurethane[J]. Synthetic Metals, 2001,117:233-235 [22] Elke Gubbelmans, Thierry Verbiest, Marcel Van Beylen, et al. Chromophore-functionalised polyimides with high-poling stabilities of the nonlinear optical effect at elevated temperature[J]. Polymer, 2002, 43:1581-1585. [23] Tae-Dong Kima, Kwang-Sup Leea, Youn Hong Jeong et al. Nonlinear optical properties of a processable polyimide having azo-dye functionalized with cyanosulfonyl group[J]. Synthetic Metals, 2001, 117: 307-309 [24] 王全伏王化滨等. 从多羟基低聚物出发合成交联型二阶非线性光学聚合物[J].Chinese Journal of Applied Chemistry, 1997, 6(14): 54-56 [25] Gang Xu, Jinhai Si, Xuchun Liu, et al. Permanent optical poling in polyurethane via thermal crosslinking[J]. 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外文翻译--通过热量误差补偿来改善数控机床的精确度
外文原文IMPROVING ACCURACY OF CNC MACHINETOOLS THROUGH COMPENSATIONFOR THERMAL ERRORSAbstract: A method for improving accuracy of CNC machine tools through compensation for the thermal errors is studied. The thermal errors are obtained by 1-D ball array and characterized by an auto regressive model based on spindle rotation speed. By revising the workpiece NC machining program , the thermal errors can be compensated before machining. The experiments on a vertical machining center show that the effectiveness of compensation is good.Key words : CNC machine tool Thermal error Compensation0 INTRODUCTIONImprovement of machine tool accuracy is essential to quality cont rol in manufacturing processes. Thermally induced errors have been recognized as the largest cont ributor to overall machine inaccuracy and are probably the most formidable obstacle to obtaining higher level of machine accuracy. Thermal errors of machine tools can be reduced by the st ructural improvement of the machine tool it self through design and manufacturing technology. However , there are many physical limitations to accuracy which can not be overcome solely by production and design techniques. So error compensation technology is necessary. In the past several years , significant effort s have been devoted to the study. Because thermal errors vary with time during machining ,most previous works have concent rated on real-time compensation. The typical approach is to measure the thermal errors and temperature of several representative point s on the machine tools simultaneously in many experiment s , then build an empirical model which correlates thermal errors to the temperature statues by multi-variant regression analysis or artificial neural network.During machining , the errors are predicted on-line according to the pre-established model and corrected by the CNC cont roller in real-time by giving additional signals to the feed-drive servo loop.However , very few practical cases of real-time compensation have been reported to be applied to commercial machine tools today. Some difficulties hinder it s widespread application. First , it is tedious to measure thermal errors and temperature of many point s on the machine tools. Second ,the wires of temperature sensors influence the operating of the machine more or less. Third , thereal-time error compensation capability is not available on most machine tools.In order to improve the accuracy of production-class CNC machine tools , a novel method is proposed. Although a number of heat sources cont ribute to the thermal errors , the f riction of spindle bearings is regarded as the main heat source. The thermal errors are measureed by 1-D ball array and a spindle-mounted probe. An auto regressive model based on spindle rotation speed is then developed to describe the time-variant thermal error. Using this model , thermal errors can be predicted as soonas the workpiece NC machining program is made. By modifying the program , the thermal errors are compensated before machining. The effort and cost of compensation are greatly reduced. This research is carried on a JCS2018 vertical machining center.1 EXPERIMENTAL WORKFor compensation purpose , the principal interest is not the deformation of each machine component , but the displacement of the tool with respect to the workpiece. In the vertical machining center under investigation , the thermal errors are the combination of the expansion of spindle , the distortion of the spindle housing , the expansion of three axes and the distortion of the column.Due to the dimensional elongation of leadscrew and bending of the column , the thermal errors are not only time-variant in the time span but also spatial-variant over the entire machine working space.In order to measure the thermal errors quickly , a simple protable gauge , i. e. , 1-D ball array , is utilized. 1-D ball array is a rigid bar with a series of balls fixed on it with equal space. The balls have the same diameter and small sphericity errors. The ball array is used as a reference for thermal error measurement . A lot of pre-experiment s show that the thermal errors in z-axis are far larger than those in x-axis and y-axis , therefore major attention is drawn on the thermal errors in z-axis. Thermal errors in the other two axes can be obtained in the same way.The measuring process is shown in Fig.1. A probe is mounted on the spindle housing and 1-D ball array is mounted on the working table. Initially , the coordinates of the balls are measured under cold condition. Then the spindle is run at a testing condition over a period of time to change the machine thermal status. The coordinates of the balls are measured periodically. The thermal drift s of the tool are obtained by subt racting the ball coordinates under the new thermal status f rom the reference coordinates under initial condition. Because it takes only about 1 min to finish one measurement , the thermal drifts of the machine under different z coordinates can be evaluated quickly and easily. According to the rate of change , the thermal errors and the rotation speed are sampled by every 10 min. Since only the drift s of coordinates deviated from the cold condition but not the absolute dimensions of the gauge are concerned , accuracy and precise inst rument such as a laser interferometer is not required. There are only four measurement point s z 1 ,z 2 , z 3 , z 4 to cover the z-axis working range whose coordinates are - 50 , - 150 , - 250 , - 350 respectively. Thermal errors at other coordinates can be obtained by an interpolating function.Previous experiment s show that the thermally induced displacement between the spindle housing and the working table is the same with that between the spindle and table. So the thermal errorsΔz measured reflect those in real cutting condition with negligible error.In order to obtain a thorough impression of the thermal behavior of the machine tool andidentify the error model accurately , a measurement strategy is developed. Various loads of the spindle speed are applied. They are divided into three categories as the following : (1) The constant speed ; (2) The speed spect rum ; (3) The speedsimulating real cutting condition. The effect of the heat generated by the cutting process is not taken into account here. However , the influence of the cutting process on the thermal behaviour of the total machine structure is regarded to be negligible in finishing process.In this machine , the most significant heat sources are located in the z-axis. Thermal errors in z direction on different x and y coordinates are approximately the same. It implies that the positions of x-carriage and y-carriage have no strong influence on the z-axis thermal errors.Fig.1(L)Thermal error measurement 1.Spindle mounted probe 2.1-D ball arrayFig.2 (R)Thermal errors at different z coordinates 1. z = - 50 2. z = - 150 3. z = - 250 4. z = - 350Fig.2 plot s the time-history of thermal drift Δz at different z coordinates under a test . Itshows that the resultant thermal drift s are obvious position-dependent . The thermal drift s at z 1 ,z 2 , z 3 , z 4 are coincident initially but separate gradually as time passes and temperature increases.The reason is that , initially most of thermal drift s result f rom the position-independent thermal growth of the spindle housing which would rise fast and go to thermal-equilibrium quickly compared to other machine component s with longer thermal-time-constant s. However , as time passes , those position-dependent thermal errors such as the lead screw and the column cont ribute to the resultant thermal drift s of the tool more and more. As a result , the thermal drifts at different z coordinates have different magnitude and thermal characteristics. However , the thermal errors at different coodinates vary with z coordinate continuously.2 AR MODEL FOR THERMAL ERRORPrecise prediction of thermal errors is an important step for accurate error compensation.Since the knowledge of the machine structure , the heat source and the boundary condition are insufficient , a precise quantitative prediction based on theoretical heat transfer analysis is quite difficult . On the other hand , empirical-based error models using regression analysis and neural networks have been demonst rated to predict thermal errors with satisfactory accuracy in much application.Thermal errors are caused by various heat sources. Only the influence of the heat caused by the fiction of spindle which is the most significant heat source is considered. The influence of external heat source on machining accuracy can be diminished by environment temperature control.From the obtained data , it is found that thermal errors vary continuously with time. Thevalue of error at one moment is influenced by that of the previous moment and the rotation speed of spindle. So a model representing the behavior of the thermal errors as written is the formwhere Δz ( t) ———Thermal error at time tk , m ———Order of the modelai , bi ———Coefficient of the modeln ( t - i) ———Spindle rotation speed at time t - iThe order k and m are determined by the final prediction-error criterion. The coefficients aiand bi are estimated by artificial neural network technique. A neural network is a multiple nonlinear regression equation in which the coefficient s are called weight s and are t rained with an iterative technique called back propagation. It is less sensitive than other modeling technique to individual input failure due to thresholding of the signals by the sigmoid functions at each node. The neural network for this problem is shown in Fig.3. ( k = 1 , m = 0) . The number of hidded nodes is determined by a trial-and error procedure.Using the data obtained (thermal errors and correspondence speed) , four models for the errors at z 1 , z 2 , z 3 and z 4 are established. Thermal errors at positions other than z 1 , z 2 , z 3 , z 4 are calculated by an interpolating function. So the errors at any z coordinates can be obtained.In order to verify the prediction accuracy of the model , a number of new operation conditions are used. Fig14 shows an example of predicted result on a new condition. It shows that the auto regressive model based on speed can descibe thermal errors well in a relative stable environment .Fig.3 A neural network for thermal errors Fig.4 Thermal error predicting1.Measuring results 2Predicting results3 PRE-COMPENSATION FOR THERMAL ERRORSThe principle of pre-compensation for thermal errors is shown in Fig.5. The spindle rotation speed and the z coordinates are known as soon as the workpiece NC machining program is made.By , for example , every 10 min , the thermal errors Δz are calculated by the model. Then the program is corrected by adding the calculated Δz to the original z . So the thermal errors are compensated before machining.The effectiveness of the error compensation is verified by many cutting test s. Several surfaces are milled under cold start and after 1 h run with varying speeds. As shown in Fig.6 , the depth difference of the milled surface is used to evaluate the compensation result of the thermal errors in z direction. It shows that the difference is reduced from 7μm to 2μm.Fig.5 Compensation for thermal errors by revising machining programFig.6 The effectiveness of compensation4 CONCLUSIONSA novel method for improving the accuracy of CNC machine tools is discussed. The core of the study is an error model based on spindle rotation speed but not on temperature like conventional approach. By revising the NC workpiece machining program , the thermal errors can be compensated before machining but not in real-time. By using the method , the accuracy of machine tools can be increased economically.1 Chen J S , Chiou G. Quick testing and modeling of thermally-induced errors of CNC machine tools. InternationalJournal of Machine Tools and Manufacture , 1995 , 35(7) ∶1 063~1 0742 Chen J S. Computer-aided accuracy enhancement for multi-axis CNC machine tool. International Journal of Machine Tools and Manufacture , 1995 , 35(4) ∶593~6053 Donmez M A. A general methodology for machine tool accuracy enhancement by error compensation. Precision Engineering , 1986 , 8 (4) ∶187~1964 Lo C H. An application of real-time error compensation on a turning center. International Journal of Machine Tools and Manufacture , 1995 , 35(12) ∶1 669~1 682.5 Yang S. The Improvement of thermal error modeling and compensation on machine tools by CMAC neural network. International Journal of Machine Tools and Manufacture , 1995 , 36(4) ∶527~5376 李书和1 数控机床误差补偿的研究∶[博士学位论文]1 天津∶天津大学,19961通过热量误差补偿来改善数控机床的精确度摘要:通过热量误差补偿来改变数控机床的精度是一种可行的方法。
分段最小二乘法 英文
分段最小二乘法英文Segmented Least Squares RegressionThe field of data analysis and modeling has seen a significant evolution over the years, with various techniques and methods being developed to uncover patterns and relationships within complex datasets. One such technique is the segmented least squares regression, which has become increasingly popular in recent years due to its ability to capture non-linear trends and identify structural changes within data.Segmented least squares regression, also known as piecewise linear regression, is a statistical technique used to fit a set of linear models to different segments of a dataset. This approach is particularly useful when the underlying relationship between the dependent and independent variables is not linear, but can be approximated by a series of linear segments. By dividing the data into multiple segments and fitting a separate linear model to each segment, the segmented least squares regression can effectively capture the non-linear trends and identify any structural changes or breakpoints in the data.The key advantage of segmented least squares regression is its ability to provide a more accurate and flexible representation of the underlying data patterns. In contrast to a single linear regression model, which assumes a constant slope throughout the entire range of the data, the segmented approach allows for the identification of multiple linear segments, each with its own unique slope and intercept. This can be particularly useful in situations where the relationship between the variables changes over different ranges of the data, such as in economic or financial time series, or in the analysis of biological or environmental phenomena.To implement a segmented least squares regression, the first step is to identify the appropriate number of segments and the location of the breakpoints, which separate the different linear segments. This can be done through various techniques, such as visual inspection of the data, exploratory data analysis, or the use of statistical algorithms designed to detect structural changes. Once the segmentation is determined, the next step is to fit a separate linear regression model to each segment, ensuring that the models are continuous at the breakpoints.The mathematical formulation of the segmented least squares regression can be expressed as follows:y = β₀₁ + β₁₁x, for x ≤ k₁y = β₀₂ + β₁₂x, for k₁ < x ≤ k₂...y = β₀ₙ + β₁ₙx, for kₙ₋₁ < xwhere:- y is the dependent variable- x is the independent variable- β₀ₙ and β₁ₙ are the intercept and slope parameters for the nth segment, respectively- kₙ are the breakpoints that separate the different segmentsThe parameters of the segmented regression model are typically estimated using an iterative optimization process, such as the Levenberg-Marquardt algorithm or the Nelder-Mead method, which aim to minimize the sum of squared residuals between the observed and predicted values.One of the key considerations in the application of segmented least squares regression is the choice of the number of segments and the location of the breakpoints. This can be a challenging task, as the optimal segmentation may not be known a priori and may require a combination of statistical analysis, domain knowledge, and iterative exploration. Various model selection criteria, such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC), can be used to help determine the appropriate number ofsegments and the locations of the breakpoints.Another important aspect of segmented least squares regression is the interpretation of the results. The fitted linear models for each segment can provide valuable insights into the underlying relationships and the structural changes within the data. The slope and intercept parameters of each segment can be interpreted in the context of the specific problem or domain, and the breakpoints can be used to identify the points at which the data exhibits a significant change in behavior.In conclusion, segmented least squares regression is a powerful statistical technique that allows for the modeling of non-linear trends and the identification of structural changes within a dataset. By dividing the data into multiple linear segments, this approach can provide a more accurate and flexible representation of the underlying relationships, making it a valuable tool in a wide range of data analysis and modeling applications.。
ACCA P3考试必考知识点:Linear regression
ACCA P3 考试必考知识点:Linear regression 为帮助大家更好复习ACCA 考试,今天yjbys 小编为大家带来ACCA P3 考试Linear regression 里面的知识点,由于全都是英文,大家不认识的可以去查阅相关字典工具,方便理解!Least squares linear regression is a method of fitting a straight line to a set of points on a graph. Typical pairs of graph axes could include:• total cost v volume produced• quantity sold v selling price• quantity sold v advertising spend.The general formula for a straight line is y = ax +b. So, y could be total cost and x could be volume. a gives the slope or gradient of the line (eg how much the cost increases for each additional unit), and b is the intersection of the line on the y axis (the cost that would be incurred even if production were zero).You must be aware of the following when using linear regression:• The technique guarantees to give the best straight line possible for any set of points. You could supply a set of peoples ages and their telephone numbers and it would purport to a straight-line relationship between these. It is, therefore, essential to investigate how good the relationship is before relying on it. See later when the coefficients of correlation and determination are discussed.• The more points used, the more reliable the results. It is easy to draw a straight line through two points, but if you can draw a straight line through 10 points you might be on to something.• A good association between two variables does not prove。
机器学习笔记04(LogisticRegression)
机器学习笔记04(LogisticRegression)Logistic Regression思路:1、逻辑回归 vs 线性回归(Logistics Regression VS Linear Regression )2、⽣成模型 vs 判别模型(Generative Model VS Discriminative Model)3、逻辑回归 vs 深度学习(Logistics Regression VS Deep Learning)前⾯已经学过 Regression ,其建⽴的 model 是 Linear model,现在与Logistics Regression对⽐学习。
此节与上节内容联系⽐较紧密,可回头复习⼀下。
1、逻辑回归 vs 线性回归(Logistics Regression VS Linear Regression ) 什么是逻辑回归:逻辑回归是解决分类问题的⼀种算法它与linear regression 形式上有点像(本质上是在线性模型外⾯“裹”⼀个sigmoid激活函数,来表⽰概率的函数)它是⼀种判别模型,与前⾯说的⽣成模型不同它是深度学习的基础1)model 不同model 对⽐:2)Loss Function 不同回顾我们线性回归的Loss函数中是跟训练数据(x1,y^1)中的y^1的差值平⽅和,那么逻辑回归是否也要建⽴与y^的联系呢。
下⾯就要开始拼凑了!Loss function对⽐:为什么不是⽤平⽅差?3)step 3 是类似的先算左边(红⾊框)的偏导,再算右边红⾊框的偏导,再整理式⼦:对⽐:2、⽣成模型 vs 判别模型(Generative Model VS Discriminative Model)两种不同的model,得到的 w,b 的参数不同。
但⼀般来说,判别模型表现得会⽐⽣成模型好,为什么?举例 ⽣成模型是基于假想的概率模型的,如果样本不平衡的话,计算出来的概率是会有误差的 但是⽣成模型也有优点: 1、样本量少的时候表现⽐判别模型好,因为它能⾃⼰脑补出⼀个假想模型 2、噪声对它影响较⼩,因为它没有过分依赖数据,它是按照⾃⼰假想模型⾛的3、逻辑回归 vs 深度学习(Logistics Regression VS Deep Learning)逻辑回归是解决分类问题的,实际中的问题⼤多是多分类的问题,多分类问题会⽤到softmax 以下⾯三分类问题为例:逻辑回归是有它的局限性的,这时候就需要深度学习了!举例:我们要⽤逻辑回归⽅法分类出下⾯的红点与蓝点,是需要⽤特征⼯程的⽅法的,⽽特征⼯程是需要我们⼈为地去建⽴⼀个特征函数去把这些点转化,实际上是⽐较难的,或者说⽐较费⼯夫的。
机器学习几种方式
机器学习⼏种⽅式学习⽅式根据数据类型的不同,对⼀个问题的建模有不同的⽅式。
在机器学习或者⼈⼯智能领域,⼈们⾸先会考虑算法的学习⽅式。
在机器学习领域,有⼏种主要的学习⽅式。
将算法按照学习⽅式分类是⼀个不错的想法,这样可以让⼈们在建模和算法选择的时候考虑能根据输⼊数据来选择最合适的算法来获得最好的结果。
监督式学习:在监督式学习下,输⼊数据被称为“训练数据”,每组训练数据有⼀个明确的标识或结果,如对防垃圾邮件系统中“垃圾邮件”“⾮垃圾邮件”,对⼿写数字识别中的“1“,”2“,”3“,”4“等。
在建⽴预测模型的时候,监督式学习建⽴⼀个学习过程,将预测结果与“训练数据”的实际结果进⾏⽐较,不断的调整预测模型,直到模型的预测结果达到⼀个预期的准确率。
监督式学习的常见应⽤场景如分类问题和回归问题。
常见算法有逻辑回归(Logistic Regression)和反向传递神经⽹络(Back Propagation Neural Network)⾮监督式学习:在⾮监督式学习中,数据并不被特别标识,学习模型是为了推断出数据的⼀些内在结构。
常见的应⽤场景包括关联规则的学习以及聚类等。
常见算法包括Apriori算法以及k-Means算法。
半监督式学习:在此学习⽅式下,输⼊数据部分被标识,部分没有被标识,这种学习模型可以⽤来进⾏预测,但是模型⾸先需要学习数据的内在结构以便合理的组织数据来进⾏预测。
应⽤场景包括分类和回归,算法包括⼀些对常⽤监督式学习算法的延伸,这些算法⾸先试图对未标识数据进⾏建模,在此基础上再对标识的数据进⾏预测。
如图论推理算法(Graph Inference)或者拉普拉斯⽀持向量机(Laplacian SVM.)等。
强化学习:在这种学习模式下,输⼊数据作为对模型的反馈,不像监督模型那样,输⼊数据仅仅是作为⼀个检查模型对错的⽅式,在强化学习下,输⼊数据直接反馈到模型,模型必须对此⽴刻作出调整。
常见的应⽤场景包括动态系统以及机器⼈控制等。
一种新的正弦波频率估计算法
一种新的正弦波频率估计算法张刚兵;钱显毅【摘要】研究了高斯白噪声条件下大样本点单一正弦波信号的频率估计方法.首先利用离散傅里叶变换确定频率粗估计,然后以该值为参考频率构造本地信号,将原信号下变频至基带,对基带信号分段求和,能得到一个新的正弦波信号,该信号的频率为真实频率与参考频率之差,最后利用最小二乘法估计新信号的载频,修正粗估计值就能得到原信号频率的最优估计.推导了算法的渐近方差与克拉美-罗限之间的关系.仿真结果表明,本算法能适用于整个频段范围,频率估计的精度接近正弦波频率估计的克拉美-罗限.%A novel algorithm for frequency estimation of sinusoid with many samples in the complex white Gaussian noise was proposed. A coarse frequency estimate was obtained through discrete Fourier transform and a sequence was constructed with the coarse frequency estimate being a reference frequency. Then the original sequence was converted into baseband by down-conversion. A new sinusoid was acquired after correlation accumulation, of which the frequency was the difference between the original frequency and reference one. Linear regression made it possible to get the optimal frequency difference estimate. The relationship between the asymptotic error variance (AEV) and the Cramer-Rao lower bound(CRLB) was derived. Simulation results show that the performance of the proposed algorithm approaches the CRLB of the sinusoidal signal when the signal-to-noise ratio(SNR) is higher than the SNR threshold.【期刊名称】《华中师范大学学报(自然科学版)》【年(卷),期】2012(046)001【总页数】5页(P40-44)【关键词】大样本;离散傅里叶变换;相关积累;频率估计【作者】张刚兵;钱显毅【作者单位】常州工学院电子信息与电气工程学院,江苏常州231002;常州工学院电子信息与电气工程学院,江苏常州231002【正文语种】中文【中图分类】TN911正弦波频率估计算法广泛应用于雷达、通信以及电子对抗等信号处理领域.Rife[1]最先提出了被加性复高斯白噪声污染的正弦波信号频率估计算法——最大似然(Maximum Likelihood,ML)估计,虽然其性能接近正弦波频率估计的克拉美-罗限(Cramer-Rao Lower Bound,CRLB),但需要进行一维频率搜索,计算量太大,不利于工程采纳.多位学者对正弦波信号频率估计问题作了进一步研究,相继提出了多种频率估计算法.这些算法大致可分为两类,一类是基于谱线插值,另一类是基于相位信息.文献[2]利用信号的最大两根谱线插值进行频率估计,即Rife算法.当信号频率位于量化频率附近时,由于插值方向错误,会导致频率估计性能下降.针对这一问题,文献[3]先对信号进行频移,使新信号的频率位于两个相邻量化频率点的中心区域,提出了修正Rife算法,再以该算法的估计值为初始值进行一次牛顿迭代(Sinusoid Frequency Estimation Based on Newton's Method,SFENM).当初始值位于收敛区域时,迭代收敛,其性能稳定,否则会导致频率估计精度下降.文献[4]提出了基于DFT相位的正弦波频率和初相估计方法(Based on Phase of DFT,BPDFT),利用分段DFT频谱的相位差消除了初相对频率估计的影响且避免了相位测量模糊问题.文献[5]指出,当信号真实频率与DFT量化频率差为某一范围时Rife算法精度不高,并研究了噪声背景中插值FFT估计正弦信号频率估计的问题.文献[6]利用与最大谱线对应的量化频率点相差半个量化频率的两根谱线进行插值,提出了一种迭代插值((Iterative Frequency Estimation by Interpolation on Fourier Coefficients,IFEIFC))频率估计算法,性能接近克拉美-罗限.Tretter[7]将加性复测量噪声等效为加性实相位噪声,利用最小二乘法对展开的相位估计信号的频率.高斯噪声条件下的最小二乘估计等价于最大似然估计,因此,文献[7]的算法在高斯噪声条件下的性能接近正弦信号频率估计的克拉美-罗限,但该算法模型成立的信噪比门限约为10dB,降低了其在低信噪比条件下的性能.为了避免相位展开,文献[8]利用相位平均和相位加权平均提出了基于相位差分的频率估计算法,在高信噪比条件下的性能与最小二乘法相当,也接近克拉美-罗限,但该算法也不适用于信噪比低于10dB的场合.文献[9-11]相继提出了3种基于信号自相关函数的频率估计算法,虽然改善了信噪比门限附近的性能,使之不会急剧恶化,但高信噪比条件下的性能却无法接近克拉美-罗限,甚至还会缩小频率估计范围[11].文献[12]反复利用低通滤波、抽取,线性预测以及数字差拍变频进行频率估计(Iterative Lin-ear prediction,ILP),大大降低了算法的信噪比门限,信噪比门限以上的性能最多超过克拉美-罗限0.7dB.文献[13]多次利用自相关函数进行频率估计(Autocorrelation-based algorithm,AA),当信号样本长度为1 024时,其频率估计的均方根误差比克拉美-罗限大0.14dB.当信号样本点数较大时,受硬件条件的限制,难以一次性对全部接收信号进行处理.如果先将信号分成等长的L段,再对每段信号分别进行频率估计,最后对各频率估计值取算术平均,那么只能得到非相干频率估计值,其估计方差仅为各段方差的L分之一,与整段信号频率估计的克拉美-罗限(为各段频率估计方差的L3分之一)相差甚远,此时的估计精度可能难以满足系统的性能指标.为了能估计大样本点信号的频率参数,同时保证参数估计的精度满足系统设计要求,必须研究新的频率估计方法.受文献[3-6]的启发,本文利用离散傅里叶变换(Discrete Fourier Transform,DFT)的信噪比增益作用降低信噪比门限,结合高斯噪声条件下最小二乘等价最大似然的特性,提出了一种正弦波频率估计算法.首先利用离散傅里叶变换得到频率粗估计值,然后以该值作为参考频率将原信号下变频至基带,分段求和后能得到一个新的正弦波信号.以该正弦波的频率修正参考频率就能得到原信号的频率估计值.与基于相位信息的算法相比,本算法的信噪比门限更低、均方误差更接近克拉美-罗限,与基于谱线插值的算法相比,计算效率更高.设信号模型为式中,A为信号的幅度,φ0为初始相位,f为信号的频率,ts为采样间隔,N为信号样本点数.ε是均值为0、方差为σ2的复高斯白噪声,定义信号的信噪比(signal-to-noise ratio,SNR)为SNR=A2/σ2.对式(1)定义的正弦波采样序列x(n),假定已得到信号频率f的粗估计值^f0,现构造序列将(1)式和(2)式共轭相乘式中,ε′是均值为0、方差为σ2的复高斯白噪声,Δf0 =f-为粗估计的误差.将式(3)表示的信号z(n)按M点分为一组并求和,能得到一个点数为L=N/M的新序列,假设N/M为整数,有式(4)中的S(m)是一个频率为Δf0、采样间隔为Mts、样本点数为L点的正弦波序列,对S(m)进行频率估计可得到频偏Δf0的估计值Δ^f0,修正粗估计能得到信号频率f的精确估计.无模糊估计频率要求频偏Δf0满足奈奎斯特采样定理,即序列S(m)的信噪比SNRout≃MA2/σ2=M×SNR,与正弦波采样序列x(n)相比,此时的信噪比增益提高了10lg MdB.累加点数M越多,信噪比增益越大,频偏Δf0估计的信噪比门限越低.Tretter在文献[7]中指出,当复正弦序列的输入信噪比远远大于1时,加性复测量噪声可近似为等效的加性实相位噪声,且相位噪声的方差为正弦波输入信噪比倒数的一半.当原测量信号信噪比不太小、信号样本点数较大时,经过求和积累后,式(4)的信噪比SNRout有可能远远大于1.因此,式(4)可近似为式中,ζm是均值为0、方差为1/(2SNRout)的实高斯白噪声.高斯白噪声条件下的最小二乘估计等价于最大似然估计,如果能得到序列S(m)无模糊的相位值φm,就能用最小二乘估计得到频偏Δf0的最优估计.设式(8)中X的最小二乘估计为利用频偏的估计值修正频率粗估计就能得到信号频率的精确估计,综合以上分析,本算法的实现步骤为1)确定最大谱线对应的量化频率点,得到粗估计;2)按照公式(2)、(3)、(4)得到序列S(m),m=0,1,…,L-1;3)利用文献[7]的算法对S(m)进行载频估计,得到频偏Δf0的估计值Δ4)利用频偏Δf0的估计值得到频率估计值5)以频率估计值作为粗估计值,重复2)、3)、4)进行一次迭代,可以得到更精确的频率估计值^f.现从渐近方差(Asymptotic Error Variance,AEV)以及计算量对本算法进行定量分析.利用最大谱线对应的量化频率点作为频率粗估计,在不出现频率模糊的条件下,粗估计误差满足对式(3)表示的信号z(n)进行M 点累加以后,新序列的信噪比增加了10lg (M)dB,M 越大,信噪比增益越大,影响频偏估计的信噪比门限就越低.累加之后要求信号S(m)有两个以上的样本点,M 的取值必须满足M ≤N/2,利用式(10),有因此,累加之后能满足式(5)对无模糊频率估计的要求.频率估计的精度由频偏Δf0的估计精度决定,而频偏估计是无偏估计,估计的均方误差为[7]式中,E(·)表示取数学期望.正弦波信号频率估计^f的克拉美-罗限(CRLB)为[1]由式(12)和式(13)有从式可以看出,累加点数M越大,性能损失越大,当M=N/2时,性能下降最严重,此时的性能与克拉美-罗限相差1.25dB.但当M分别为N/4和N/8时,累加求和后的信号样本分别为4点和8点,性能相对克拉美-罗限分别下降0.28dB和0.07dB.在M≪N的条件下,本算法的均方误差与克拉美-罗限相等.现分析本算法需要的计算量,假设N是2的整数次幂.在频率粗估计时利用快速傅里叶变换(Fast Fourier Transform,FFT)确定信号的频谱,需要N/2·log2N次复数乘法、Nlog2N次复数加法,确定最大谱线的位置需要N次复数乘法,式(4)需要N次复数乘法,式需要N(M-1)/M复数加法,获得相位需要N/M次反正切运算、N/M次相位展开,估计频偏则需要N/M次实数乘法和N/M-1次实数加法,作一次迭代需要N次复数乘法、N(M-1)/M复数加法、N/M 次反正切运算、N/M 次相位展开、N/M次实数乘法和N/M-1次实数加法.为了验证本算法的测频性能,对其进行计算机仿真,信号样本点数为1 024,采样频率为100MHz,信噪比为0dB,累加点数M为128,信号频率从25MHz开始,到25MHz+1/(2 NΔt)结束,将频率变化范围等分20份,每个离散频率点上各仿真2 000次.其均方根误差与克拉美-罗限之比随频率变化的性能曲线如图1所示.图1的仿真结果表明,基于自相关函数相位信息的频率估计算法的均方根误差最大,基于谱线迭代插值和以修正Rife算法估计值为初值进行一次牛顿迭代的频率估计算法性能相当,都能在整个频段范围内进行频率估计.下面验证本算法在不同信噪比条件下的性能.采样频率为100MHz,信号的频率为f=21.123 42MHz,信号样本点数N为1 024,累加点数M为128.对本算法的性能进行仿真,给出了各算法在相同条件下的性能对比,每种条件下各进行2 000次独立的仿真.均方根误差(RMSE)与信噪比(SNR)之间的关系如图2所示.图中给出了各算法的均方根误差及克拉美-罗限,图中横轴为线性坐标,纵轴为对数坐标.图2的仿真结果表明,本算法与文献[6]算法一样,具有相同的信噪比门限,且都低于文献[3-4-12-13]算法.因此,本算法对信噪比要求更低,能估计较低信噪比条件下信号的频率.在较低信噪比条件下,牛顿迭代的初始值位于收敛区域之外,迭代不收敛,此时的估计性能不如本算法.随着信噪比的增加,初始值进入收敛区域,迭代后的性能接近克拉美-罗限.文献[12-13]都是基于迭代自相关函数估计信号的频率,其信噪比门限均高于基于DFT的频率估计算法.在整个信噪比变化区间内,本算法的均方根误差都接近克拉美-罗限,表明本算法性能稳定,对信噪比变化不敏感.本文提出了一种适用于单一信号的频率估计算法,本算法具有较低的信噪比门限,其性能均匀分布在整个测频范围内.当累加求和后的信号样本点数为8时,频率估计的均方误差相对克拉美-罗限下降0.07dB.当累加后的信号样本点数远小于原测量信号样本点数时,本算法的均方误差与克拉美-罗限相同.考虑信噪比门限、测频范围以及估计性能,本算法都优于基于相位和自相关函数的频率估计器,因此,本算法具有一定的工程应用价值.【相关文献】[1]Rife D C,Boorstyn R R.Single-tone parameter estimation from discrete-time observations[J].IEEE Trans on Information Theory,1974,20(5):591-598.[2]Rife D C,Bowstyn R R.Multiple tone parameter estimation from discrete rime observation[J].Bell Syst Tech 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Iterative linear regression by sector: renormalization of cDNA microarray data and cluster analysis weighted by cross homology.David B. Finkelstein1, Jeremy Gollub1, Rob Ewing1, Fredrik Sterky1, Shauna Somerville1, J. Michael Cherry21 Department of Plant Biology, Carnegie Institution of Washington, Stanford CA2 Department of Genetics, Stanford University, Stanford CAAbstractTwo-color DNA microarray data has proven valuable in high-throughput expression profiling. However microarray expression ratios (log2ratios) are subject to measurement error from multiple causes. Transcript abundance is expected to be a linear function of signal intensity (y = x) where the typical gene is non-responsive. Once linearity is confirmed, applying the model by fitting log-scale data with simple linear regression reduces the standard deviation of the log2ratios. After which fewer genes are selected by filtering methods. Comparing the residuals of regression to leverage measures can identify the best candidate genes. Spatial bias in log2ratio, defined by printing pin and detected by ANOVA, can be another source of measurement error. Independently applying the linear normalization method to the data from each pin can easily eliminate this error. Less easily addressed is the problem of cross-homology which is expected to correlate to cross-hybridization. Pair-wise comparison of genes demonstrate that genes with similar sequences are measured as having similar expression. While this bias cannot be easily eliminated, the effect this probable cross-hybridization can be minimized in clustering by weighting methods introduced here.Introduction to the Iterative MethodEmpirical observations, validated by statistical tests, indicate that distinct classes of measurement error alter cDNA microarray data. When these measurement errors are detectable and conform to defined models, corrections can be applied during renormalization. However, supporting biological evidence may be required to validate any normalization method. For the Spellman and Sherlock cell cycle data [3], the spatial and signal intensity dependent measurement errors were corrected through renormalization. Re-analysis of the Spellman and Sherlock cell cycle data set begins with a new method of normalization that more accurately reduces the effects of outliers and spatial variation on the arrays. First, all background-corrected signal intensity values are log transformed. Then linear regression is performed where one the signal intensities of channel is predicted by the signals from the other channel. Spatial error is corrected by performing this regression independently for each sector. Slotted printing pins produced these sectors. The microarrays used in the Spellman experiments had four sectors printed with four distinct pins.The result is four linear intensity equations, one for each sector. Next residuals are calculated for each of the four regression lines. Outliers (those residuals where |e| > 2 x std dev of e) are temporarily removed and the four regression functions are recalculated. If the difference in fit between the new and old regression lines is less than .001, as measured by r-squared, then no further residuals are removed. Else, outliers are removed by the same test as above and the iterations continue. Once completely determined, the slope and intercept values are applied as correction factors to the log transformed channel 2 values. The result is that the function of log channel 1 and log channel 2 closely approximates y = x. Then these values are exponentiated, a new ratio is calculated and this ratio is put on the familiar log base2 scale. This renormalization alone has been demonstrated to substantially reduce the standard deviation of log2 ratios.Next, the detection of cross-hybridization was examined. Unlike sector bias, which is directly measurable, cross-hybridization is inferred from expression patterns. The yeast genome is fully sequenced, thus the sequences of PCR fragments were known. Therefore it is possible, with some error, to determine the likely number of transcripts that could cross-hybridize to a given PCR fragment. The correlation between the likelihood of cross-hybridization and the frequency of transcripts with cross-homology is difficult to assess without empirical evidence. It is important to note that modeling the molecular events during hybridization has proven difficult. Therefore, no analysis can be used to correct data. However, a technique can be applied as an informed post hoc method. In this way, such analysis may indicate where biological confirmation experiments are warranted, rather than supply a mathematical solution.Applying Linear NormalizationApplying a linear model presumes that the data is or should be linear with respect to intensity. The assumptions that the typical gene is non-responsive and that rare genes are no more likely to respond than abundant genes leads to the adoption of the linear model y = x. This assumption is probably valid for large-scale genome wide arrays, but not for small specialized arrays. Where y is expression under stress condition and x is the expression of genes under the control condition. Only a small class of genes is expected to respond to any given test. So this equation is valid for the typical case and invalid for the biologically responsive genes. We further presume that biologically responsive genes are sufficiently responsive to be distinguished from the typical gene using statistical tests. For these genes of biological interest the residuals of regression should be especially large.For cases where intensity functions are nonlinear (Roger Bumgarner, MGED3 , Stanford, CA)[5] then nonlinear lowess regression methods may be required in stead of the simple linear regression method employed here. In these cases, however, it is still those genes with the highest residuals from the fitted function that are of the greatest interest.In all tested cases, applying a linear model of error combined with the iterative removal of outlying residuals reduces the standard deviation of the final log2ratios. The range of the data is not substantially altered. However, the distribution of the data may change. Frequently, the kurtosis increases, meaning that the tails are more widely dispersed with respect to the standard deviation. and the skew may change in scale and indirection. Filtering iteratively normalized data without considering spatial bias, increased the number of genes that are consistently changed at the |log2_ratio|> 2 for 1 of 11 Elutriation arrays by 4.3% (an increase of 9 genes) when compared to data normalized by the SMD default method. When the iterative method is applied each sector to correct spatial problems the number of genes that pass filtering criterion actually decreases. In both cases the overall standard deviation of the data is reduced. Only independent empirical methods can determine whether the differences in analysis methods are removing false positives.Spatial MethodsObservation based on a spatial display tool developed for microarrays indicated that spatial problems may exist for several Spellman and Sherlock arrays. Renormalization by sector requires 4 parallel normalizations and assumes that functional groups of genes are not printed together. For many arrays the net result of spatial linear normalization is marginal. However, significant spatial effects have been detected in other cDNA arrays and therefore it is worth testing arrays for the effect.Spatial bias is detectable with a simple ANOVA (y = log2ratio and X = grid #) that yields an F-test and r-squared value. Non-parametric methods such as the Kruskal-Wallis test also serve this function [1]. Our current best estimate is that, if r-squared values are below .05, then spatial error is not significant. Best practice may indicate repeating experiments that are substantially altered, rather than applying sector specific normalization methods, which are post hoc and may only partially repair the effects.Applying the Linear Method by SectorFor each of the four independent sectors of each DNA microarray, the iterative simple linear regression technique is applied. As expected many arrays, are not substantially altered by this approach. However, in instances, where outlier sectors are significant, by ANOVA F-tests, differences in normalization are visually evident (Figure 1). Note that the four sectors each have independent patterns with respect to background corrected channel 2 intensity (CH2D). The differences between the SMD method and Iterative method are consistently greater at low intensities: below 150. Each pattern is at a minimum where the linear regression equation for a given sector is equal to the SMD global mean. In this case, there is a clear difference in the minimum of one pattern, which may indicate spatial bias in that sector.Figure 1.The absolute value of the difference between log2_ratio calculated by the SMD method and the Iterative method is plotted on the y-axis. The background-corrected channel 2 intensity is plotted on the x-axisFiltering resultsFiltering parameters: all spots that have an average intensity of 100 in each channel and a |log2_ratio|>2 in at least 1 array were selected.TABLE I.SMD Method Iterative Method Proportional Change α-Factor:3342690.805Elutriation:1791350.754CDC:120410990.913Note that the Iterative method consistently reduces the number of genes that pass the filters. It also consistently lowers the standard deviation of the log2_ratios in these studies. It does not, however, consistently improve the global correlation between the log2_ratios of any two arrays.Examples of Changed ArraysColumn1:SMD Method Column2:Iterative MethodRow AElut.expt. ID57Row BElut.expt.ID56Figure 2. The plots below show the spatial pattern of log2_ratios on two Elutriation arrays (SMD EXPID 56 (row B) and 57(row A) normalized by the SMD method on the left and by the Iterative method on the right. All spots with a log2_ratio greater than 1 appear in red. All spots with a ratio below 1 appear in green. Black spots indicate a flagged spot, white spots have a ratio of 1. Note that the iterative method (Column 2) partially corrects the spatial bias seen in the SMD method (Column1)for both expt. 56 and 57.Selecting Biologically Significant GenesOnce normalization is completed, significantly differential genes are identified by their expression ratios. Because the log 2ratios are rarely distributed normally, there are often more candidate genes identified than standard deviation methods would predict. When kurtosis is high, the proportion of differential genes at the tails of the distribution will also be high. Determining which ratios are false positives may be critical to reducing cost and effort of confirmation experiments [4].The analysis of residuals from simple linear regression affords a screen for these outliers. A linear regression function is subject to the influence individual measurements in direct proportion to their distance from the mean. That is, genes at the extremes have greater leverage (influence) on the predicted regression line then those near the mean of x. Measures of leverage will select a distinct group of genes from a line when compared to those found by residuals. Residuals that are distant from the predicted line may have little leverage if they are especially close to the mean. Since most genes are neitherespecially rare nor abundant, we expect most biological outliers to be near the mean of x (where x is the control state) and therefore have a high residual and a low leverage [2] If we plot leverage versus the square of the residual we can see a much smaller class of genes are significantly (by standard deviation measures) different by residual measures but not by leverage. Genes that are high in leverage and low in residuals are likely false positives, if they have a differential ratio. Genes that are high in both categories areambiguous, some can be reasonably expected to be authentically differential. Discerning between these genes is beyond the scope of this simple test.L e v e r a g eNormalized residual squared.000084.001343Figure 3. Leverage vs. residual squared plot of microarray data (from lvr2plotprocedure in STATA 6.0 Stata Corp, College Station, TX) .Sequence Similarity in Yeast ArraysThe degree to which cross-hybridization might influence microarray expression data was also examined. First, a preliminary analysis was performed that related sequence similarity to the degree of correlation between expression profiles. Several assumptions are made. First, it was assumed that the full length ORFs available from SGD (Saccharomyces Genome Database) approximate the targets actually used on the microarray. This assumption is deemed reasonable, as yeast primer pairs were designed to include as much of the ORFs as possible (Gavin Sherlock, pers. comm.). Second, it was assumed that the degree of sequence similarity between a pair of sequences, as measured by an alignment program such as BLASTN, would approximate the degree of cross-hybridization between those sequences.First, 2,690 ORFS were selected from the original 6,178 yeast ORFs. The selected ORFS were those with the fewest missing expression data values (that is ORFs with greater than 8 missing values across the 62 experiments were excluded). For all pairs of the 2,690 ORFs, the correlation coefficient between the expression profiles was calculated and a BLASTN alignment of the sequences created. For all pairs of ORFs with some degree of homology, the correlation coefficients were extracted and are plotted as two histograms in Figure 2. ORF pairs are divided according to their BLASTN e-values. Correlation coefficients for ORF pairs with BLASTN e-value greater than 1 X 10-4 are shown in white and those with BLASTN e-value less than 1 X 10-4 are in red.Relatively few ORF pairs showed significant sequence similarity. 1991 ORF pairs had e-values greater than 1 X 10-4 and 59 pairs had e-values less than 1 X 10-4. The set of 1991 ORF pairs had a mean pairwise correlation coefficient of 0.036, whereas the set of 59 ORF pairs with lower e-values had a mean pairwise correlation coefficient of 0.419.Figure 4. Pairwise correlation coefficients of the expression pattern across 62 experiments of yeast ORFS. Red comparisons are highly similar pairs. Note the relative rarity of cross homology and the relative high degree of co-expression amongst the highly similar ORFs.Despite the small numbers, it appears that ORF pairs with a higher degree of sequence similarity are also more likely to exhibit a higher degree of correlation between their expression profiles. The e-value indicates, but does not prove cross-hybridization. It is also possible that genes with high sequence similarity may have similar function and therefore may be authentically co-expressed. Cross-hybridization and the degree to which this may confound results from genome-wide microarray experiments should definitely be considered in the design of future microarrays by printing gene specific probes used wherever possible.Calculating the WeightsWeights were assigned to only those genes, which passed two criterion. The pairwise expression with another ORF had to exceed 1e-4 and their BLASTN score had to exceed 100. The BLASTN score was the morestringent criterion, resulting in no expression correlation below 1e-21. 782 pairwise comparisons passed this cut off, representing some 678 ORF's. Weights were firstcalculated for each pairwise comparison. If an ORF was part of more than one pairwisecomparison then the weights were multiplied. Weights were calculated as 0.5 +0.5(minimum exp/exp). In this case the minimum exponent of the data set was -21. If a given pairwise correlation value was 1e-42 then the weight would be 0.5 + 0.5(-21/-42) = 0.75. The maximum weight was 1 and the minimum weight was 0.16. This method is one of many that should give a reasonable approximation of the range of cross-hybridization. However, a method based on empirical evidence of cross-hybridization would be preferable.Figure 5. Histogram of the weights applied to potentially cross-hybridizing genes. Note that most weights were nearly 0.5, and only a small sub-population is weighted less than 0.25.Determining Best PracticeBest practice of microarray data analysis is directly tied to the application of the data. If the arrays are to be used as rapid screening tools, then sophisticated normalization and analysis may not be necessary. If, however, the object of the experiment is to model subtle biological patterns across gradients of time or treatment, much more complex analysis is required. Furthermore, while statistical measures are useful in measuring and correcting some errors, the most accurate means of determining gene transcript behavior will require empirical evidence. When DNA microarrays are designed to assist the statistical analysis, best practice can be achieved. For example, the replicate printing of a core control group of elements in several locations throughout an array would greatly simplify detection of spatial bias. Also, doping controls that consist of a class of non-homologous RNA transcripts could serve as independent verification of normalization methods. Finally, the correlation of expression amongst elements with high degrees of cross-homology does not prove cross-hybridization. In summary, thecombination of careful array design, empirical verification and accurate mathematical models of error will result in the best practice of microarray data analysis. AcknowledgementsWe would like to acknowledge the National Science Foundation (NSF) for providing functional genomics funding (grant number 9872638) and through it’s postdoctoral fellowship program in bioinformatics (David Finkelstein).References1. Kruskal WH, Wallis WA: The use of ranks in one-criterion variance analysis.Journal of the American Statistical Association 47: 583-621 (1952).2. Neter J, Wasserman W, Kutner MH: Applied Linear Statistical Models. Irwin,Homewood, Ill (1990).3. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO,Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9: 3273-97. (1998).4. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied tothe ionizing radiation response. Proc Natl Acad Sci U S A 98: 5116-21. (2001). 5. Yang YH, Dudoit S, Luu P, Speed TP: Normalization for cDNA Microarray DataUC Berkeley Tech Report (2000).。