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A context-sensitive generalization of ICA

A context-sensitive generalization of ICA
A context-sensitive generalization of ICA

A Context-Sensitive Generalization of ICA

Barak A.Pearlmutter Lucas C.Parra

Dept.of Cog.Sci.,UCSD,La Jolla,California,USA,barak.pearlmutter@https://www.wendangku.net/doc/8f17596907.html,

Siemens Corporate Research,Princeton,New Jersey,USA,lucas@https://www.wendangku.net/doc/8f17596907.html,

—Source separation arises in a surprising number of signal processing applications,from speech recognition to EEG analysis.In the square linear blind source separation problem without time delays, one must?nd an unmixing matrix which can detangle the result of mixing unknown independent sources through an unknown mixing matrix.The recently introduced ICA blind source separation algorithm (Baram and Roth1994;Bell and Sejnowski1995)is a powerful and surprisingly simple technique for solving this problem.ICA is all the more remarkable for performing so well despite making absolutely no use of the temporal structure of its input!This paper presents a new algorithm,contextual ICA,which derives from a maximum likelihood density estimation formulation of the problem.cICA can incorporate arbitrarily com-plex adaptive history-sensitive source models,and thereby make use of the temporal structure of its input. This allows it to separate in a number of situations where standard ICA cannot,including sources with low kurtosis,colored gaussian sources,and sources which have gaussian histograms.Since ICA is a special case of cICA,the MLE derivation provides as a corollary a rigorous derivation of classic ICA.

1The ICA algorithm

In the blind source separation problem,one is given the output of a number of microphones,each of which records a mixture of a number of sources.The task is to recover the sources.In the blind linear square case,there are the same number of microphones as sources,and the mixing is linear.In the absence of time delays or echos,the mixing is characterized by an matrix,so if is a vector of the sources at time then is a vector of the signals received by the microphones at time.Naturally we will assume that is full rank.

In the absence of noise,which is the case we consider,the solution to this problem is to?nd a full rank matrix which has the property that has exactly one nonzero element in each row and each column.We denote the result of the unmixing process as,and note that.If we have found an appropriate then the product will be equal to the product of a diagonal matrix with a permutation matrix, and the elements of will be the same as the elements of,but shuf?ed and scaled.

With no prior information about or the source signals,the problem might sound impossible.However, for non-gaussian distributions,it is not.An algorithm called independent components analysis was introduced by Comon(1994).This version of the algorithm approximates some distributions by their?rst few moments,which is both approximate and computationally burdensome.Single coordinate higher order cumulants are used in a some-what simpler algorithm by Obradovic and Deco(1995).A surprisingly simple,but inexpensive and exact,variant of the Comon(1994)algorithm was recently introduced(Baram and Roth1994;Bell and Sejnowski1995).In a now standard abuse of notation,this new algorithm will be refered to as ICA.This simpler ICA algorithm takes each component of the vector and passes it though a saturating monotonic nonlinearity,giving a vector. Gradient descent is used to modify the components of the matrix and the bias terms of the nonlinearities in order to increase the entropy of the distribution of induced by the input distribution.ICA was motivated by consid-erations of biological optimality,which?ow from experiments showing that,when presented with natural stimuli, many neurons appear to make good use of their available axonal channel capacity(Bialek et al.1991).

The ICA algorithm,in various con?gurations,has been applied to a surprising number of problems,from separation of digitally mixed speech signals(Bell and Sejnowski1995),to separating the componenets of electroencephalo-graphic data(Makeig et al.1996),to blind deconvolution(Bell and Sejnowski1995),to?nding the higher-order structure of a natural sound(Bell and Sejnowski1996b),and even to?nancial forecasting(Baram and Roth1995) and image processing(Bell and Sejnowski1996a).There have been attempts to generalize the algorithm,the most notable being extensions to tolerate time delays and echos introduced by Torkkola(1996a,1996b).

The usual intuition for why ICA tends to separate sources runs roughly as follows:if the output entropy is maxi-mized,then the components of the output vector must be statistically independent.If so,then the signals must also be statistically independent prior to the nonlinearity.That being the case,the sources must be separated. However,there are problematic cases which ICA cannot separate.For instance,a mixture of two uniform distri-butions,or more generally two low-kurtosis distributions,is not properly separated.(Although separation in this case might be achieved by using a special nonlinearity chosen for the problem.)Since a two-dimensional gaussian distribution is rotationally symmetric,a mixture of white gaussian sources is inherently impossible to separate.Any

independent

of generative model

Figure1:The ICA algorithm?ts this parameterized generative model to data.

algorithm that makes no use of the temporal structure of its inputs can by de?nition make use of only the cumula-tive histograms of its inputs.If these histograms are gaussian,then such an algorithm will be in principle unable to separate.Since ICA makes no use of the temporal structure of its inputs,it is in principle unable to separate sources whose histograms are gaussian.This includes,for example,colored gaussian sources,speech or music which hap-pen to have gaussian histograms,etc.It is sometimes speculated that any mixture of sources with high-kurtosis histograms is separable by ICA—but there is as yet no proof of this.

We shall now proceed to derive an ICA-like algorithm that can make use of temporal context.We do this by refor-mulating the blind source separation problem in a maximum likelihood framework.

2Source separation and maximum likelihood density estimation

Consider the abstract problem of density estimation from samples.One desires to estimate some true distribu-tion over a space from which samples have been drawn.The maximum likelihood approach (Mendel and Burrus1990)is to use a density estimator of some parametric form,say.Given a setting of the parameter vector,this will constitute the estimated probability density.In order to set appropriately,we ?nd a value for it that minimizes a measure of the difference between and.An appropriate difference measure is the asymmetric divergence

independent input Figure 2:The contextual ICA (cICA)algorithm uses conditional densities which are not memoryless.

formulas for the two different sorts of parameters involved,

(3)(4)

where expr

denotes the column vector whose elements are expr

expr

.

This is precisely the ICA algorithm,where our

is the derivative of the Bell and Sejnowski (1995)satu-rating monotonic nonlinearity ,and our parameter vector holds the th component of their vector of bias terms,In our formulation no squashing nonlinearity is ever calculated,except perhaps as a common subexpression in the computation of the derivatives of the densities.However,the output of the squashing nonlinearity is never actually used for anything in classic ICA.

3Generalizing ICA

Under this MLE formulation of source separation,there is no restriction on the form of the distributions .The density function can have complex structure,and can be conditioned on other information—such as its recent history (as shown in ?gure 2),or even information from other modalities.All that is required is that the components of be conditionally independent.In general,can be of the form

other information

We call this algorithm contextual ICA or cICA.To give a vivid example,if the sources were different people speak-ing,then the “other information”might be lip position measured using a visual modality,and would be pri-marily conditioned on the recent history of that source itself,,but there might also be some small in?uence from other speakers.Although can in principle be made arbitrarily complex,there is no practical reason to make it more complex than is necessary to permit proper separation of the sources.

Of course we must still calculate as per equation 4.In doing so,the history

of source is treated as constant with respect to changes in .This is correct,because the unmixing depends only on the matrix and not the parameters of the individual source distributions.On the other hand,changing changes the estimated recent history of source ,which in turn has an in?uence on .However we use equation 3without adding these extra terms.The approximation of dropping these cross terms is ubiquitous in time series analysis,and in this case the successful results of our simulations leads us to believe that it is benign.

?15

?10?5051015

?15?10

?5

5

10

15

Filtered and Mixed Uniform Distributions

Figure 3:cICA using a history of one to 5,000samples of a mixture of two one-dimensional uniform distributions each ?ltered by convolution with a decaying exponential of time constant of 99.5.Shown is a scatterplot of the data input to the algorithm,along with the true source axes (left),the estimated residual probability density (center),and a scatterplot of the residuals of the data transformed into the estimated source space coordinates (right).The product of the true mixing matrix and the estimated unmixing matrix deviates from a scaling and permutation matrix by about 3%.

4Experiments

In our simulations we chose to make

a weighted sum of logistic density functions with variable means and scales,and make these means linear functions of the recent history of source .This allowed us to revert to classic ICA by setting the amount of temporal context to zero and the number of logistic densities in the sum to one.This density estimator,and the corresponding derivatives,are described in detail in appendix A.

Here we experiment with two distributions that conventional ICA is unable to separate.The ?rst is an extremely simple two-dimensional distribution with no temporal context:both and are chosen iid from a uniform dis-tribution.Conventional ICA incorrectly rotates the distribution 45degrees,for reasons explained very well by Bell and Sejnowski (1995)in their discussion of this problematic case.The cICA algorithm successfully separates the sources.To make the problem more challenging,we then ?ltered each source through low-pass ?lter.The resulting time series has very gaussian histograms,but as shown in ?gure 3,cICA again correctly separates the sources.

The second experiment is somewhat more involved.Ten acoustic sources,which include the six used by Bell and Sejnowski (1995),were obtained,courtesty of Dr.Tony Bell.As shown in ?gure 4,the cumulative density of each source was measured and used to construct a monotonically increasing normalizer which,when applied to each sample from a source,gave the time series a gaussian histogram.These preprocessed time series were mixed using a random matrix.As shown in ?gures 5and 6,ICA was unable to separate the resulting babble,but cICA separates properly,even when using only a very small amount of temporal context.

5Discussion

In deriving cICA we have seen that ICA can regarded as a gradient method for performing maximum likelihood density estimation using a linear historyless factorial model and rigid source densities.The resulting error measure is naturally the same as in the Bell and Sejnowski (1995)derivation,but taking an MLE viewpoint allows a number of generalizations,which allow cICA to to separate a wider variety of sources.

A weakness this method shares with other blind source separation techniques is that it not robust to modulation of the dimensionality.In other words,it is not designed for a non-square mixing matrix.If and is -dimensional but is -dimensional,then in the case that the algorithm presented here can make no good use of the extra information but to imagine that a few extra Gaussian sources were mixed into the signal.This may perhaps be solved by using a matrix of a special form.In the case that no linear unmixing can separate the sources,and it seems that a strong prior will be necessary to distinguish a single complex one-dimensional source from the one-dimensional sum of two simple independent one-dimensional sources,and a nonlinear unmixing process will be necessary to separate them.

formation applied to the data (center),histogram of transformed data (right).

Influence of Filter Size

filter size

sources (left),and unmixing error of cICA (us-ing linear predictive sources with a single logistic)as a function of the length of the history used in the predictive ?lter (right).The zero history case corresponds to classic ICA,which fail to separate due to the gaussian histograms.(The parallelogram-shaped boundary and the stripes in the scatterplot on the left are artifacts of the signal quanti-zation and the digital mixing.)

each row normalized to make the largest element equal to one,and the rows permuted to place large elements along the diagonal.If the unmixing is perfect,the result will be a ridge along the diagonal with all off-diagonal elements equal to zero.The ten sources mixed are acoustic sources (courtesy of Tony Bell)which have had a monotonic non-linearity applied to them to make their histograms exhibit gaussian statistics (see ?gure 4.)These are mixed using a random mixing matrix,and cICA with linear predictive sources and a single logistic density is used to estimate the unmixing matrix.The length of the history used is varied from zero,which corresponds to conventional ICA (left),to one (center),to two (right).

Finally,we would like to compare ICA with PCA.The principal components algorithm (Hotelling 1933)?ts a linear mixture of one-dimensional Gaussian sources of minimal variance to samples from a high-dimensional distribution.ICA performs a similar action,but instead uses a linear mixture of potentially non-Gaussian distributions.As such,ICA might be viewed as a linear but non-Gaussian generalization of PCA—except that without PCA’s minimum variance constraint,if Gaussian distributions are used for the distributions of ICA,the unmixing matrix has a great deal of freedom.It need not be orthogonal,and the coordinate system it embodies need have no special status.A challenge that remains with us is to ?nd a sensible nonlinear analogue of PCA.One algorithm was proposed

for this purpose by Parra,Deco,and Miesbach(1995),who replaced the orthogonal linear mixture of PCA by a symplectic mixing function while retaining PCA’s minimal variance Gaussian source model.Unfortunately the symplectic map has a great deal of undesired freedom,so again the coordinate system it produces need have no special status.

6Future work

Our current work concentrates on combining source separation with deconvolution,to enable the system to both tolerate and cancel the effects of echos and time delays between the sources and the microphones.An inherent ambiguity is introduced,which amounts to a freedom of one?lter per source.We hope to resolve this ambiguity in a more symmetric fashion than in Torkkola(1996a),where identity?lters are placed along the diagonal of the matrix of deconvolution?lters.We are also exploring the incorporation of microphone nonlinearities,and microphone noise of known distribution,into the model.

Acknowledgments

Thanks are due to Dr.Tony Bell for provocative discussions and for generously sharing his data.Portions of this work were performed while BAP was visiting the Sloan Center for Theoretical Neurobiology at the Salk Institute. References

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Also1846,20,1.

A Linear predictive source distributions

In the simulations of section4the distribution used is a mixture of logistic densities,

(5) where is a scale parameter for logistic density of source and is an element of,and the mixing coef?cients are elements of and are constrained by.The component means are taken to be linear functions of the recent values of that source,

(6) where the linear prediction coef?cients and bias are elements of.

To perform stochastic gradient descent it is necessary to calculate the derivative.We ac-complish this using the following equations.For conciseness,when we below refer to,,and their simple derivatives,,we leave off the arguments,which are the same as the corresponding argu-ments above.The logistic density function and its cumulative distribution function are as in footnote1.

(7)

(8)

(9)(10)

(11)

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∴10kV母线上的三相短路电流为:Id=100000/0.60875*√3*10.5,冲击电流:I sh=2.55I =23032.875A。 d 2动稳定校验 (1)10kV母线桥的动稳定校验: 进行母线桥动稳定校验应注意以下两点: ①电动力的计算,经过对外边相所受的力,中间相所受的力以及三相和二相电动力进行比较,三相短路时中间相所受的力最大,所以计算时必须以此为依据。 ②母线及其支架都具有弹性和质量,组成一弹性系统,所以应计算应力系数,计及共振的影响。根据以上两点,校验过程如下: 已知母线桥为8×80mm2的铝排,相间中心线间距离为210mm,先计算应力系数: ∵频率系数N f=3.56,弹性模量E=7×10.7 Pa,单位长度铝排质量M=1.568kg/m,绝缘子间跨距2m,则一阶固有频率: f’=(N f/L2)*√(EI/M)=110Hz 查表可得动态应力系数β=1.3。 ∴单位长度铝排所受的电动力为: f ph=1.73×10-7I sh2/a×β=568.1N/m ∵三相铝排水平布置,∴截面系数W=bh2/6=85333mm3,根据铝排的最大应力可确定绝缘子间允许的最大跨距为: L MAX=√10*σal*W/ f ph=3.24m ∵胡店变主变母线桥绝缘子间最大跨距为2m,小于绝缘子间的最大允许跨距。

大学物理学(第三版上) 课后习题1答案详解

习题1 1.1选择题 (1) 一运动质点在某瞬时位于矢径),(y x r 的端点处,其速度大小为 (A)dt dr (B)dt r d (C)dt r d | | (D) 22)()(dt dy dt dx + [答案:D] (2) 一质点作直线运动,某时刻的瞬时速度s m v /2=,瞬时加速度2 /2s m a -=,则一秒钟后质点的速度 (A)等于零 (B)等于-2m/s (C)等于2m/s (D)不能确定。 [答案:D] (3) 一质点沿半径为R 的圆周作匀速率运动,每t 秒转一圈,在2t 时间间隔中,其平均速度大小和平均速率大小分别为 (A) t R t R ππ2, 2 (B) t R π2,0 (C) 0,0 (D) 0,2t R π [答案:B] 1.2填空题 (1) 一质点,以1 -?s m π的匀速率作半径为5m 的圆周运动,则该质点在5s 内,位移的大小是 ;经过的路程是 。 [答案: 10m ; 5πm] (2) 一质点沿x 方向运动,其加速度随时间的变化关系为a=3+2t (SI),如果初始时刻质点的速度v 0为5m·s -1,则当t 为3s 时,质点的速度v= 。 [答案: 23m·s -1 ] (3) 轮船在水上以相对于水的速度1V 航行,水流速度为2V ,一人相对于甲板以速度3V 行走。如人相对于岸静止,则1V 、2V 和3V 的关系是 。 [答案: 0321=++V V V ]

1.3 一个物体能否被看作质点,你认为主要由以下三个因素中哪个因素决定: (1) 物体的大小和形状; (2) 物体的内部结构; (3) 所研究问题的性质。 解:只有当物体的尺寸远小于其运动范围时才可忽略其大小的影响,因此主要由所研究问题的性质决定。 1.4 下面几个质点运动学方程,哪个是匀变速直线运动? (1)x=4t-3;(2)x=-4t 3+3t 2+6;(3)x=-2t 2+8t+4;(4)x=2/t 2-4/t 。 给出这个匀变速直线运动在t=3s 时的速度和加速度,并说明该时刻运动是加速的还是减速的。(x 单位为m ,t 单位为s ) 解:匀变速直线运动即加速度为不等于零的常数时的运动。加速度又是位移对时间的两阶导数。于是可得(3)为匀变速直线运动。 其速度和加速度表达式分别为 2 2484 dx v t dt d x a dt = =+== t=3s 时的速度和加速度分别为v =20m/s ,a =4m/s 2。因加速度为正所以是加速的。 1.5 在以下几种运动中,质点的切向加速度、法向加速度以及加速度哪些为零哪些不为零? (1) 匀速直线运动;(2) 匀速曲线运动;(3) 变速直线运动;(4) 变速曲线运动。 解:(1) 质点作匀速直线运动时,其切向加速度、法向加速度及加速度均为零; (2) 质点作匀速曲线运动时,其切向加速度为零,法向加速度和加速度均不为零; (3) 质点作变速直线运动时,其法向加速度为零,切向加速度和加速度均不为零; (4) 质点作变速曲线运动时,其切向加速度、法向加速度及加速度均不为零。 1.6 |r ?|与r ? 有无不同?t d d r 和d d r t 有无不同? t d d v 和t d d v 有无不同?其不同在哪里?试举例说明. 解:(1)r ?是位移的模,?r 是位矢的模的增量,即r ?12r r -=,12r r r -=?; (2) t d d r 是速度的模,即t d d r ==v t s d d . t r d d 只是速度在径向上的分量. ∵有r r ?r =(式中r ?叫做单位矢),则 t ?r ?t r t d d d d d d r r r += 式中 t r d d 就是速度在径向上的分量,

母线电动力及动热稳定性计算

母线电动力及动热稳定性计算 1 目的和范围 本文档为电气产品的母线电动力、动稳定、热稳定计算指导文件,作为产品结构设计安全指导文件的方案设计阶段指导文件,用于母线电动力、动稳定性、热稳定性计算的选型指导。 2 参加文件 表1 3 术语和缩略语 表2 4 母线电动力、动稳定、热稳定计算 4.1 载流导体的电动力计算 4.1.1 同一平面内圆细导体上的电动力计算

? 当同一平面内导体1l 和2l 分别流过1I 和2I 电流时(见图1),导体1l 上的电动力计 算 h F K I I 4210 π μ= 式中 F ——导体1l 上的电动力(N ) 0μ——真空磁导率,m H 60104.0-?=πμ; 1I 、2I ——流过导体1l 和2l 的电流(A ); h K ——回路系数,见表1。 图1 圆细导体上的电动力 表1 回路系数h K 表 两导体相互位置及示意图 h K 平 行 21l l = ∞=1l 时,a l K h 2= ∞≠1l 时,?? ? ???-+=l a l a a l K h 2)(12 21l l ≠ 22 2) ()(1l a m l a l a K h ++-+= 22)()1(l a m +-- l a m =

? 当导体1l 和2l 分别流过1I 和2I 电流时,沿1l 导体任意单位长度上各点的电动力计 算 f 124K f I I d μ= π 式中 f ——1l 导体任意单位长度上的电动力(m N ); f K ——与同一平面内两导体的长度和相互位置有关的系数,见表2。 表2 f K 系数表

4.1.2 两平行矩形截面导体上的电动力计算 两矩形导体(母线)在b <<a ,且b >>h 的情况下,其单位长度上的电动力F 的 计算见表3。 当矩形导体的b 与a 和h 的尺寸相比不可忽略时,可按下式计算 712 210x L F I I K a -=? 式中 F -两导体相互作用的电动力,N ; L -母线支承点间的距离,m ; a -导体间距,m ; 1I 、2I -流过两个矩形母线的电流,A ; x K -导体截面形状系数; 表3 两矩形导体单位长度上的电动力 4.1.3 三相母线短路时的电动力计算

高压电缆热稳定校验计算书

筠连县分水岭煤业有限责任公司 井 下 高 压 电 缆 热 稳 定 性 校 验 计 算 书 巡司二煤矿 编制:机电科 筠连县分水岭煤业有限责任公司

井下高压电缆热稳定校验计算书 一、概述: 根据《煤矿安全规程》第453条及456条之规定,对我矿入井高压电缆进行热稳定校验。 二、确定供电方式 我矿高压供电采用分列运行供电方式,地面变电所、井下变电所均采用单母线分段分列供电方式运行,各种主要负荷分接于不同母线段。 三、井下高压电缆明细: 矿上有两趟主进线,引至巡司变电站不同母线段,一趟931线,另一趟925线。井下中央变电所由地面配电房10KV输入。 入井一回路:MYJV22-8.7/10KV 3*50mm2--800m(10KV) 入井二回路:MYJV22-8.7/10KV 3*50mm2--800m(10KV) 四、校验计算 1、井下入井回路高压电缆热稳定性校验 已知条件:该条高压电缆型号为,MYJV22-8.7/10KV 3*50mm2 ,800m,电缆长度为800m=0.8km。 (1)计算电网阻抗 查附表一,短路电流的周期分量稳定性为 电抗:X=0.072*0.8=0.0576Ω; 电阻:R=0.407*0.8=0.3256 Ω; (2)三相短路电流的计算

A Z I 5.174693305 .0310000 3v 3=?== ∞ (3)电缆热稳定校验 由于断路器的燃弧时间及固有动作时间之和约为t=0.05S; 查附表二得热稳定计算系数取K=142; 故电缆最小热值稳定截面为 23mm 51.2705.0142/5.17469t )/(min ===∞)(K I S Smin<50mm 2 故选用 MYJV 22 -8.7/10KV 3*50 电缆热稳定校验合格,符合要求。 附表一:三相电缆在工作温度时的阻抗值(Ω/Km ) 电缆截面S (mm 2 ) 4 6 10 16 2 5 35 50 70 95 120 150 185 240 交联聚乙烯 R 4.988 3.325 2.035 1.272 0.814 0.581 0.407 0.291 0.214 0.169 0.136 0.11 0.085 X 0.093 0.093 0.087 0.082 0.075 0.072 0.072 0.069 0.069 0.069 0.07 0.07 0.07 附表二 不同绝缘导体的热稳定计算系数 绝缘材料 芯线起始温度(° C ) 芯线最高允许温度(°C ) 系数K 聚氯乙烯 70 160 115(114) 普通橡胶 75 200 131 乙丙橡胶 90 250 143(142) 油浸纸绝缘 80 160 107 交联聚乙烯 90 250 142

大学物理学(第三版)课后习题参考答案

习题1 1.1选择题 (1) 一运动质点在某瞬时位于矢径),(y x r 的端点处,其速度大小为 (A)dt dr (B)dt r d (C)dt r d | | (D) 22)()(dt dy dt dx + [答案:D] (2) 一质点作直线运动,某时刻的瞬时速度s m v /2=,瞬时加速度2 /2s m a -=,则 一秒钟后质点的速度 (A)等于零 (B)等于-2m/s (C)等于2m/s (D)不能确定。 [答案:D] (3) 一质点沿半径为R 的圆周作匀速率运动,每t 秒转一圈,在2t 时间间隔中,其平均速度大小和平均速率大小分别为 (A) t R t R ππ2, 2 (B) t R π2,0 (C) 0,0 (D) 0,2t R π [答案:B] 1.2填空题 (1) 一质点,以1 -?s m π的匀速率作半径为5m 的圆周运动,则该质点在5s 内,位移的大小是 ;经过的路程是 。 [答案: 10m ; 5πm] (2) 一质点沿x 方向运动,其加速度随时间的变化关系为a=3+2t (SI),如果初始时刻质点的 速度v 0为5m ·s -1 ,则当t 为3s 时,质点的速度v= 。 [答案: 23m ·s -1 ] (3) 轮船在水上以相对于水的速度1V 航行,水流速度为2V ,一人相对于甲板以速度3V 行走。如人相对于岸静止,则1V 、2V 和3V 的关系是 。 [答案: 0321=++V V V ]

1.3 一个物体能否被看作质点,你认为主要由以下三个因素中哪个因素决定: (1) 物体的大小和形状; (2) 物体的内部结构; (3) 所研究问题的性质。 解:只有当物体的尺寸远小于其运动范围时才可忽略其大小的影响,因此主要由所研究问题的性质决定。 1.4 下面几个质点运动学方程,哪个是匀变速直线运动? (1)x=4t-3;(2)x=-4t 3+3t 2+6;(3)x=-2t 2+8t+4;(4)x=2/t 2 -4/t 。 给出这个匀变速直线运动在t=3s 时的速度和加速度,并说明该时刻运动是加速的还是减速的。(x 单位为m ,t 单位为s ) 解:匀变速直线运动即加速度为不等于零的常数时的运动。加速度又是位移对时间的两阶导数。于是可得(3)为匀变速直线运动。 其速度和加速度表达式分别为 2 2484 dx v t dt d x a dt = =+== t=3s 时的速度和加速度分别为v =20m/s ,a =4m/s 2 。因加速度为正所以是加速的。 1.5 在以下几种运动中,质点的切向加速度、法向加速度以及加速度哪些为零哪些不为零? (1) 匀速直线运动;(2) 匀速曲线运动;(3) 变速直线运动;(4) 变速曲线运动。 解:(1) 质点作匀速直线运动时,其切向加速度、法向加速度及加速度均为零; (2) 质点作匀速曲线运动时,其切向加速度为零,法向加速度和加速度均不为零; (3) 质点作变速直线运动时,其法向加速度为零,切向加速度和加速度均不为零; (4) 质点作变速曲线运动时,其切向加速度、法向加速度及加速度均不为零。 1.6 |r ?|与r ? 有无不同?t d d r 和d d r t 有无不同? t d d v 和t d d v 有无不同?其不同在哪里?试举例说明. 解:(1)r ?是位移的模,?r 是位矢的模的增量,即r ?12r r -=,12r r r -=?; (2) t d d r 是速度的模,即t d d r ==v t s d d . t r d d 只是速度在径向上的分量. ∵有r r ?r =(式中r ?叫做单位矢),则 t ?r ?t r t d d d d d d r r r += 式中 t r d d 就是速度在径向上的分量,

翻译中的归化与异化

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翻译的归化与异化 作者:熊启煦 作者单位:西南民族大学,四川,成都,610041 刊名: 西南民族大学学报(人文社科版) 英文刊名:JOURNAL OF SOUTHWEST UNIVERSITY FOR NATIONALITIES(HUMANITIES AND SOCIAL SCIENCE) 年,卷(期):2005,26(8) 被引用次数:14次 参考文献(3条) 1.鲁迅且介亭杂文二集·题未定草 2.刘英凯归化--翻译的歧路 3.钱钟书林纾的翻译 引证文献(15条) 1.郭锋一小议英语翻译当中的信达雅[期刊论文]-青春岁月 2011(4) 2.许丽红论汉英语言中的文化差异与翻译策略[期刊论文]-考试周刊 2010(7) 3.王笑东浅谈汉英语言中的差异与翻译方法[期刊论文]-中国校外教育(理论) 2010(6) 4.王宁中西语言中的文化差异与翻译[期刊论文]-中国科技纵横 2010(12) 5.鲍勤.陈利平英语隐喻类型及翻译策略[期刊论文]-云南农业大学学报(社会科学版) 2010(2) 6.罗琴.宋海林浅谈汉英语言中的文化差异及翻译策略[期刊论文]-内江师范学院学报 2010(z2) 7.白蓝跨文化视野下文学作品的英译策略[期刊论文]-湖南社会科学 2009(5) 8.王梦颖探析汉英语言中的文化差异与翻译策略[期刊论文]-中国校外教育(理论) 2009(8) 9.常晖英汉成语跨文化翻译策略[期刊论文]-河北理工大学学报(社会科学版) 2009(1) 10.常晖对翻译文化建构的几点思考[期刊论文]-牡丹江师范学院学报(哲学社会科学版) 2009(4) 11.常晖认知——功能视角下隐喻的汉译策略[期刊论文]-外语与外语教学 2008(11) 12.赵勇刚汉英语言中的文化差异与翻译策略[期刊论文]-时代文学 2008(6) 13.常晖.胡渝镛从文化角度看文学作品的翻译[期刊论文]-重庆工学院学报(社会科学版) 2008(7) 14.曾凤英从文化认知的视角谈英语隐喻的翻译[期刊论文]-各界 2007(6) 15.罗琴.宋海林浅谈汉英语言中的文化差异及翻译策略[期刊论文]-内江师范学院学报 2010(z2) 本文链接:https://www.wendangku.net/doc/8f17596907.html,/Periodical_xnmzxyxb-zxshkxb200508090.aspx

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