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Submitted to ApJ The Broad-band Optical Properties of Galaxies with Redshifts 0.0 1.Motivation There are strong correlations among the measurable physical properties of galaxies.The classi?cation of galaxies along the visual morphological sequence described by Hubble(1936)correlates well with the dominance of their central bulge,their surface brightnesses,and their colors.These properties also correlate with other properties,such as metallicity,emission-line strength,luminosity in visual bands,neutral gas content,and the winding angle of the spiral structure(for a review,see Roberts&Haynes1994).The surface brightnesses of giant galaxies classi?ed morphologically as elliptical are known to be strongly correlated with their sizes(Kormendy1977).Galaxy colors(at least of morphologically elliptical galaxies)are known to be strongly correlated with galaxy luminosity(Baum1959;Faber1973;Visvanathan&Sandage1977;Terlevich et al.2001).The gravitational mass of a galaxy is closely related to the luminosity and other galaxy properties.These galaxy relations manifest themselves in the Tully-Fisher relation for spiral galaxies(Tully &Fisher1977;Burstein et al.1997)and the Fundamental Plane for morphologically elliptical galaxies(Faber &Jackson1976;Djorgovski&Davis1987;Dressler et al.1987;Burstein et al.1997);Strauss&Willick(1995) review these dynamical relations.Furthermore,the environment of a galaxy is related to its type,in the sense that early-type galaxies are found in denser regions than late-type galaxies,as?rst noted by Hubble (1936)and as found by numerous subsequent investigators(Oemler1974;Dressler1980;Davis&Geller 1976;Giovanelli et al.1986;Santiago&Strauss1992;Guzzo et al.1997;Hashimoto&Oemler1999;Blanton 2000).In short,di?erent physical properties of galaxies are closely related to each other. In order to understand theoretically how galaxies formed and acquired their current properties,it is?rst necessary to characterize the observed distribution of galaxy properties in a comprehensive manner.Such a characterization is a primary goal of the Sloan Digital Sky Survey(SDSS;York et al.2000).The SDSS has already created the largest sample to date(144,609galaxies)of luminous galaxies with well-measured photometric and spectroscopic properties.As a?rst step in understanding the joint distribution of galaxy properties,we present here a study of the number density and luminosity density distributions of galaxy colors,pro?les,luminosities,surface brightnesses,and local densities.Our purpose is to measure properties related to quantities that cosmological gasdynamical simulations(Nagamine et al.2001;Pearce et al.2001; Steinmetz&Navarro2002)or semi-analytic models(Somerville et al.2001;Mathis et al.2002)can predict or will soon be able to predict,such as galaxy ages,sizes,stellar masses,and degrees of concentration. The paper is organized as follows.Section2brie?y describes the spectroscopic sample of the SDSS. Section3describes how we measure the set of properties studied here.Section4shows the number density and luminosity density distributions of these properties of galaxies,and provides simple?ts for the dependence on luminosity of the properties of galaxies with nearly exponential radial pro?les and those with more concentrated radial pro?les.Section7summarizes and outlines future lines of research. 2.Data 2.1.Description of the Survey The SDSS(York et al.2000)is producing imaging and spectroscopic surveys overπsteradians in the Northern Galactic Cap.A dedicated wide-?eld2.5m telescope(Siegmund et al.,in preparation)at Apache Point Observatory,Sunspot,New Mexico,images the sky in?ve bands between3000and10000?A(u,g,r, i,z;Smith et al.2002)using a drift-scanning,mosaic CCD camera(Gunn et al.1998),detecting objects to a?ux limit of r～22.5mags.The photometric quality of the observations are tracked using an automatic photometricity monitor(Hogg et al.2001).An automatic image-processing pipeline known as photo(Lupton et al.in preparation)processes the images and produces a catalog.The astrometric calibration is also performed by an automatic pipeline(Pier et al.2002).One of the goals is to spectroscopically observe 900,000galaxies,(down to r lim≈17.77mags),100,000Luminous Red Galaxies(Eisenstein et al.2001),and 100,000quasars(Fan1999,Richards et al.2002).This spectroscopic follow up uses two digital spectrographs on the same telescope as the imaging camera.Many of the details of the galaxy survey are described in the galaxy target selection paper(Strauss et al.2002).Other aspects of the survey are described in the Early Data Release paper(EDR;Stoughton et al.2002).The survey has begun in earnest,and to date has obtained about30%of its intended data. The SDSS images are reduced and catalogs are produced by the SDSS pipeline photo,which measures the sky background and the seeing conditions,and detects and measures objects.The photometry used here for the bulk of these objects was the same as that used when the objects were targeted.However,for those objects which were in the EDR photometric catalog,we used the better calibrations and photometry from the EDR.The versions of the SDSS pipeline photo used for the reductions of the data used here ranged from v52.The treatment of relatively small galaxies,which account for most of our sample,did not change substantially throughout these versions.The astrometric calibration is also performed by an automatic pipeline which obtains absolute positions to better than0.1arcsec(Pier et al.2002). The magnitudes are calibrated to a standard star network(Smith et al.2002)approximately in the AB system.There are small di?erences between the system output by the SDSS pipelines and a true AB system,amounting to?m=?0.042,0.036,0.015,0.013,?0.002in the u,g,r,i,and z bands.Because these were discovered at a relatively late date in the preparation of this manuscript,we have not self-consistently included these shifts in our results(that is,by recalculating K-corrections based on the revised colors). Instead we have applied them a posteriori to our results in the0.1u,0.1g,0.1r,0.1i,and0.1z bands. Object?uxes are determined several di?erent ways by photo,as described in Stoughton et al.(2002). The primary measure of?ux used for galaxies is the SDSS Petrosian magnitude,a modi?ed version of the quantity proposed by Petrosian(1976).The essential feature of Petrosian magnitudes is that in the absence of seeing they measure a constant fraction of a galaxy’s light regardless of distance(or size).They are described in greater detail below(and also by Blanton et al.(2001)and Strauss et al.(2002)).As measures of the galaxy pro?le shape,photo also calculates the radius containing half the Petrosian?ux(r50)and that containing90%of the Petrosian?ux(r90). As described in Strauss et al.(2002),the bulk of the?bers in the SDSS are allocated to three samples: the Main Sample galaxies,the Luminous Red Galaxies,and quasars.Here we will concern ourselves solely with the Main Sample galaxies.There are three main criteria for their selection: r PSF?r model>s limit r petro μ50<μ50,limit.(1) Here,r PSF is an estimate of the magnitude using the local PSF as a weighted aperture,r model is an estimate of the magnitude using the better of a de Vaucouleurs and an exponential?t to the image(accounting for seeing),r petro is a modi?ed form of the magnitude described by Petrosian(1976),andμ50is the half-light surface brightness,de?ned as the average surface brightness within the radius which contains half of the Petrosian?ux.All of these quantities are reddening-corrected using the dust maps of Schlegel et al.(1998). In practice,the values of the target selection parameters vary across the survey in a well-understood way, but for the bulk of the area,they are:s limit=0.3,r limit=17.77mag,andμ50,limit=24.5mag in1arcsec2. Objects near the spectroscopic?ux limit are nearly?ve magnitudes brighter than the photometric limit; that is,the?uxes are measured at signal-to-noise ratio of a few hundred. Fibers are assigned to a set of circular tiles with a?eld of view1.49deg in radius by an automatic tiling pipeline(Blanton et al.2002b).The targets are observed by a multi-?ber spectrograph capable of taking640spectra simultaneously(48of the?bers are,in normal SDSS operations,devoted to sky ?bers and spectrophotometric standards).An automatic data processing pipeline spec2d wavelength-and ?ux-calibrates the spectra and outputs a one-dimensional spectrum for each object(Schlegel,et al.,in preparation).The resulting spectra have a resolution of R～2000,cover roughly3800?A to9000?A,and typically have signal-to-noise per pixel around10.This spectrum is then analyzed by another pipeline (spectro1d;SubbaRao,et al.,in preparation)which classi?es the spectrum and determines the redshift of the object.The redshifts used in the current analysis are determined independently using a separate pipeline originally designed speci?cally for bright stars(specBS;Schlegel,et al.,in preparation)whose results are nearly identical for the main galaxy sample to the“o?cial”SDSS redshift determination(over99%of the objects obtain nearly identical redshifts in each pipeline). As of April2002,the SDSS had imaged and targeted2,873deg2of sky and taken spectra of approximately 350,000objects over～2,000deg2of that area.The completeness over this area is roughly91%;most of the missing galaxies(7%of the total)are lost due to the fact that two?bers on the same tile cannot be placed more closely than55′′,about1%are lost because they are not able to be assigned a?ber,and about 1%cannot have a redshift determined.From these data,we created a well-de?ned sample for calculating large-scale structure and galaxy property statistics,known as Large-Scale Structure sample10.sample10 consists of all of the photometry for all of the targets over that area(as extracted from the internal SDSS operational database),all of the spectroscopic results(as output from specBS),and,most signi?cantly,a description of the angular window function of the survey and the?ux and surface brightness limits used for galaxies in each area of the sky.For most of the area,the same version of the analysis software used to create the target list was used in this sample.However,for the area covered by the Early Data Release(EDR; Stoughton et al.2002)we used the version of the analysis software used for that data release,since it was substantially better than the early versions of the software used to target that area.For photo,the most important piece of analysis software run on the data,the versions used for the photometry range from v5 2.The region covered by this sample is similar to,but not exactly,the region which will be released in the SDSS Data Release1(DR1),scheduled for January1,2003(which will use photo v5 brightnesses,and local densities. 3.1.Galaxy Colors and Luminosities To measure the galaxy?uxes,we rely on the SDSS Petrosian magnitudes.The essential feature of Petrosian magnitudes is that in the absence of seeing they measure a constant fraction of a galaxy’s light regardless of distance(or size).More speci?cally,we de?ne the“Petrosian ratio”R P at a radius r from the center of an object to be the ratio of the local surface brightness averaged over an annulus at r to the mean surface brightness within r: αhi rαlo r dr′2πr′I(r′) π(α2hi?α2lo)r2 R P(r)≡ 11,described by Blanton et al.(2002a), which uses a similar method to the method of Csabai et al.(2000)for calculating photometric redshifts.This method is demonstrably better than using the spectra(which su?er from the aperture biases associated with small-?ber spectroscopy,the inaccuracy of spectrophotometry,and the fact that we do not take spectra in the u and z bands;cf.Kochanek et al.2000)and better than simply interpolating the magnitudes between bands(which does not account well for the4000?A break). The essence of the method is that each set of observed magnitudes in the?ve bands is?t by a linear combination of four templates.The coe?cients in the linear combination,and the templates themselves, are optimized to reproduce the observed magnitudes.For each galaxy,we now have a model for the SED. We use this SED to calculate the correction from the observed band to?xed frame bandpasses with shapes corresponding the the restframe coverage of galaxies at z=0.1,the median redshift of the sample.The usual choice is to correct the colors to the z=0frame;however,this choice is not optimal,because most galaxies in our sample are not at z=0.Figure2shows the responses of the unshifted SDSS and the SDSS system shifted to0.1.We refer to bands in this system as0.1u,0.1g,0.1r,0.1i,and0.1z,following the nomenclature of Blanton et al.(2002a).We note in passing that the K-correction for a galaxy at redshift z=0.1to this system is?2.5log10(1.1)for all bands independent of galaxy SED. As a test of the validity of the colors,Figure3shows the distribution of galaxy colors as a function of redshift in a volume-limited sample in the range?22.5 We use these estimated Petrosian0.1(u?g),0.1(g?r),0.1(r?i)and0.1(i?z)colors as our measures of the SED shape for each galaxy.As a measure of luminosity,we use the i-band?ux converted to0.1i-band luminosity luminosity using the K-correction and the standard cosmological formulae for the distance modulus,assuming?m=0.3and?Λ=0.7(Hogg1999). 3.2.Galaxy Pro?le Shape and Surface Brightness One measure of the morphology of galaxies is the radial dependence of their surface brightness.For example,if one expresses this radial dependence in the form(suggested by S′e rsic1968): I(r)=A exp ?(r/r0)1/n ,(3) galaxies with nearly exponential pro?les(S′e rsic index n～1)tend to be considered“late-type,”while galaxies with nearly de Vaucouleurs pro?les(S′e rsic index n～4)tend to be considered“early-type.”For this reason, we use measures of the azimuthally-averaged radial pro?les of the galaxies in our analysis. Although Strateva et al.(2001)and Shimasaku et al.(2001)have shown that the Petrosian inverse concentration index1/c≡r50/r90is reasonably well correlated with eyeball morphological classi?cations for nearby,large galaxies,this parameter is too sensitive to the e?ects of seeing to be of great use for the bulk of the SDSS Main Sample.The top panel of Figure4shows the dependence of inverse concentration parameter on seeing for a volume-limited subsample,limited to galaxies with?21 photo?ts two-dimensional models to the galaxy images which account for the e?ects of seeing,but they are limited to the cases of n=1and n=4,and in addition(because they were designed to study faint galaxy colors)for the versions of photo used here(before v5 parameter as a function of seeing for the volume-limited subsample.Our measure of galaxy pro?le shape is less seeing-dependent than the raw estimate r50/r90.We have?t S′e rsic pro?les to the pro?les of all bands; the results of the g,r,i,and z pro?les are all fairly consistent,while the u band results tend to be much less concentrated than the others. It is worth examining how our axisymmetric S′e rsic?t is a?ected by the non-axisymmetry of galaxies, and how it is related to other measures of pro?le shape,such as the Petrosian concentration parameter and the bulge-to-total(B/T)ratio.In the top panels of Figure5,we investigate how our S′e rsic?ts behave for galaxies with S′e rsic pro?les and galaxies which consist of a de Vaucouleurs bulge plus an exponential disk. In each case we assume the galaxies are comparable in half-light radius to SDSS Main Sample galaxies.We use three di?erent axis ratios b/a(as listed in Figure5)by which we distort the galaxy image.We then project that image onto a grid of pixels of size0.396′′,the size of SDSS pixels,apply single gaussian seeing with a standard deviation of1.2′′,and then extract radial pro?les using a similar scheme as used by SDSS https://www.doczj.com/doc/b117677266.html,ing the single gaussian model for the seeing,we?t the S′e rsic models to the resulting galaxy pro?les.We perfectly reconstruct the case of b/a for the S′e rsic pro?les;for small axis ratios,we measure a higher concentration than we would for a circular image with the same pro?le as along the semi-major axis. For the bulge-plus-disk models,we?nd that the S′e rsic index n is monotonically related to the B/T ratio of the bulge?ux to the total?ux. The bottom panels of Figure5show the dependence of the Petrosian inverse concentration parameter r50/r90for galaxies of the same shapes as,but larger radii than,the galaxies in the top panels.This situation is meant to be similar to the conditions under which galaxies were observed by Shimasaku et al.(2001)and Strateva et al.(2001),who observed large,bright galaxies less a?ected by seeing than those in the SDSS Main sample.We?nd that r50/r90is closely related to S′e rsic index and B/T. We de?ne the half-light surface brightness r S,50to be the average surface brightness within the Petrosian half-light radius for the S′e rsic pro?le?t in the0.1i-band,in magnitudes in one arcsec2,K-corrected and corrected for cosmological surface brightness dimming: μi≡m S,0.1i+2.5log10(2πr2S,50)?10log10(1+z)?K0.1i(z).(4) This measure of surface brightness is thus slightly di?erent(in general,higher)than the raw estimate calculated from the Petrosian half-light radius output by the SDSS photometric pipeline,because it is corrected for seeing.Note that we have implicitly assumed that the K-correction of the observation of surface brightness remains una?ected by color gradients in the galaxy;because the K-correction from the observed i band to the0.1i band are expected to vary little with redshift and color(<0.2magnitudes)this approximation is not bad. 3.3.Local Galaxy Density For each object in the sample we estimate the local galaxy densityρas follows.First,we distribute a large number(2×106)of Poisson random points within the SDSS volume,distributed in redshift according to the selection function of the survey.The selection function is determined by an evolving0.1r-band Schechter function?t to the luminosity function of the following form(following Lin et al.1999): Φ(L)dL=φ? L where L?(z)=100.4Q(z?0.1)L?(z=0.1).(6) Expressed in magnitudes,this implies M?(z)=M?(z=0.1)?Q(z?0.1).If Q>0,these formulae mean galaxies are brighter in the past.We indeed?nd that in order to explain the dependence of number counts on redshift in the survey,it is necessary to include the possibility that galaxy luminosities evolve.We perform this?t in the0.1r band and?nd the parameters: φ?=3.22×10?2h3Mpc?3, M?=?20.50, α=?1.01, Q=1.97.(7) We do not give error bars for this?t,nor do we recommend this?t as our best estimate of the luminosity density or its evolution.We do not give error bars for this?t,nor do we recommend this?t as our best estimate of the luminosity density or its evolution.A more complete discussion of this topic is forthcoming in a separate paper.However,this?t is good enough to use to estimate local https://www.doczj.com/doc/b117677266.html,ing this?t,the magnitude limits as a function of position on the sky(m r,min(θ,φ)and m r,max(θ,φ)),and the spectroscopic sampling fraction as a function of position on the sky(f(θ,φ))we can calculate the selection function at any point on the sky. Given the galaxy catalog and the random catalog,we count the number of galaxies N g(including the galaxy in question)and the number of random points N r in a8h?1Mpc radius comoving sphere around each galaxy.We de?ne N g/ N g ρ≡ Nevertheless,one can still meaningfully consider the median density within bins of other galaxy proper-ties,because the increased noise for galaxies with higher mean distances does not a?ect the median density. As proof of this assertion,Figure6shows the density distribution as a function of redshift for several small ranges of absolute magnitude.The lines are the10%,50%,and90%quantiles of the distribution.Although at large distances the distribution widens(the10%and90%quantiles decrease and increase,respectively), the median remains remarkably constant,indicating that it measures the density around galaxies consistently at high redshift and at low redshift.This consistency means that even when we divide galaxies into di?erent groups which cover di?erent redshift ranges(such as dividing by luminosity)we can make a fair comparison among the median local densities of the groups.This consistency happens to be the justi?cation for the common practice of calculating the correlation function for galaxies of di?erent luminosities,in di?erent redshift ranges. 3.4.Contribution to Number Density To compute the global number and luminosity densities,it is necessary to compute the number-density contribution1/V max for each galaxy,where V max is the volume covered by the survey in which this galaxy could have been observed,accounting for the?ux,surface brightness,and redshift limits as a function of angle(Schmidt1968).This volume is calculated as follows: V max=1 dz ,(9) where f(θ,φ)is described above and z min(θ,φ)and z max(θ,φ)are de?ned by: z min(θ,φ)=max(z m,min(θ,φ),zμ,min(θ,φ),0.02) z max(θ,φ)=min(z m,max(θ,φ),zμ,max(θ,φ),0.22).(10) The?ux limits m r,min(θ,φ)(equal to14.5mag across the survey)and m r,max(θ,φ)(approximately17.77mag) implicitly set z m,{min max } (θ,φ)by: m r,{min max } (θ,φ)=M0.1r+DM(z m,{min max } (θ,φ)) +K0.1r(z m,{min max } (θ,φ)).(11) The surface brightness limitsμr,min(θ,φ)(in practice,too high surface brightness to matter for this sample) andμr,max(θ,φ)(approximately24.5mag but variable in a known way across the survey)implicitly set zμ,{min max } (θ,φ)by: μr,{min max } (θ,φ)=μ0.1r+10log10(1+zμ,{min max } (θ,φ)) +K0.1r(zμ,{min max } (θ,φ)).(12) Note that we have implicitly assumed that the K-correction of the observation of surface brightness remains una?ected by color gradients in the galaxy.In practice,the surface brightness limits only rarely a?ect the V max determination. The function f(θ,φ)is the SDSS sampling fraction of galaxies as a function of position on the sky.The total sampling rate of galaxies is computed separately for each region covered by a unique set of spectroscopic survey tiles.We adopt the nomenclature of the2dF(Percival et al.2001):each such region is a“sector”(which corresponds identically to the“overlap regions”de?ned in Blanton et al.2001).That is,in the case of two overlapping tiles,the sampling is calculated separately in three sectors:the sector covered only by the ?rst tile,the sector covered only by the second tile,and the sector covered by both.Each position on the sky is thereby assigned a sampling rate f(θ,φ)equal to the number of redshifts of galaxy targets obtained in the region divided by the number of galaxy targets in the region.In regions covered by a single tile,typically 0.85 With V max,we calculate the global number and luminosity density contributions of any group of galaxies as i1/V i,max and i L i/V i,max. 4.Results 4.1.Results for All Galaxies 4.1.1.Overview Our primary results on the distributions of colors,surface brightnesses,pro?le shapes,luminosities and local densities of galaxies are shown in Figures7–9.Each of these plots is a multi-panel?gure showing some form of the bivariate distribution of every pair of galaxy properties in the o?-diagonal plots.The plots on the diagonal show the univariate distribution of each quantity. Figure7shows the projected number density distribution for all pairs of seven of our parameters, excluding density(for reasons described above).The V max values calculated above are used to calculate the contribution of each galaxy to the total number density.The panels above and below the diagonal are mirror images of each other.The grey scale is proportional to the square-root of the projected density in that plane.Contours in the plot represent loci of constant projected density enclosing68%and95%of the density.Figure8shows the bivariate0.1i-band luminosity density distribution between all of our parameters. This?gure is similar to Figure7,only now each object contributes L/V max rather than1/V max to the density. Figure9shows the conditional number density distribution of all eight of our parameters relative to seven of them(only excluding density as an independent variable).That is,the greyscale represents the probability of a particular value of the quantity on the vertical axis,given the value of the quantity on the horizontal axis.This is equivalent to taking Figure7,and for each vertical column of pixels renormalizing by the total density in that column.This plot is useful when the dependence of one quantity on another is strong in the regime where there are few galaxies(such as at high density or high luminosity).The histograms along the diagonal are the number density distributions of each quantity,and are identical to the histograms in Figure7. Some basic results are visible in these plots,which we review in the following subsections.Many of these results have been noted in the literature in the past.We will concentrate mostly on Figure9,in which many of the relationships are easiest to see. 4.1.2.Dependence on Luminosity The?rst simple result to point out from Figure9is the histogram in the right column and the second row from the top,which shows the luminosity function in the0.1i-band.The function is close to a Schechter function,although because the vertical scale is linear,it may not appear familiar.Now consider the same panel in Figure8,which shows the luminosity density as a function of absolute magnitude.It is clear from this plot that the luminosity density is almost entirely contained within the range of absolute magnitudes shown (?23.5 the luminosity density as a function of surface brightness demonstrates that the luminosity density falls o? long before the surface brightness limit becomes important.An even clearer demonstration of this fact is the panel showing the luminosity density versus M0.1i andμ0.1i.This panel shows that even at the lowest luminosities in the sample,the surface brightness limit excludes very little luminosity density.These results are in agreement with Blanton et al.(2001)(using early SDSS data)and Cross et al.(2001)(using early 2dFGRS data).A more careful analysis of the luminosity function and its evolution from this sample is being carried out by Blanton et al.(in preparation). Next,let us follow the right column of Figure9down from the top.Each panel below the top panel shows the conditional dependence of a galaxy parameter on luminosity.These panels show many of the familiar relationships of galaxy properties to luminosity from previous observations.In sequence from top to bottom: 1.Local density is a strong function of luminosity.The most luminous galaxies exist preferentially in the densest regions of the universe.Density increases with luminosity for all absolute magnitudes above M0.1i=?18.At lower luminosities,the local density starts to increase again,which is potentially related to the existence of dwarf spheroidal galaxies in clusters.This result agrees with results from CfA(Hamilton1988),the Optical Redshift Survey(hermit96a),as well as the recent results of Zehavi et al.(2002)in the SDSS and Norberg et al.(2002)in the2dFGRS on the dependence of the correlation function amplitude on galaxy luminosity. 2.Highly luminous galaxies are more concentrated,and thus have higher S′e rsic indices,than lower lumi- nosity galaxies.This trend is consistent with high luminosity galaxies being preferentially“early-type.” However,even for the highest luminosity galaxies,the de Vaucouleurs pro?le(n～4)is always more concentrated than more than75%of the galaxies;the total number density of galaxies with n>3in our absolute magnitude range is～5%.Thus,either the number density fraction of“true ellipticals” is small in this absolute magnitude range or many“true ellipticals”are not de Vaucouleurs pro?le galaxies.That the S′e rsic pro?le in most cases provides a better?t to the surface brightness pro?les of morphologically-classi?ed elliptical galaxies than the de Vaucouleurs pro?le has been shown previ- ously by several groups(Prugniel&Simien1997;Caon et al.1993).We can try to compare to these results by dividing our sample by color.Figure10shows the dependence of n on absolute magnitude for three ranges of0.1(g?r).The S′e rsic index n is clearly independent of luminosity for very blue galaxies(which are close to exponential).For very red galaxies the S′e rsic index is a strong function of luminosity(paralleling the dependence of S′e rsic index on luminosity found by Prugniel&Simien1997 and Caon et al.1993for morphologically elliptical galaxies). 3.Luminosity and surface brightness are positively correlated at low luminosities,while at extremely high luminosity surface brightness declines with luminosity.As we will see below,this occurs because the surface brightnesses of highly concentrated galaxies,which tend to be highly luminous,decline with luminosity,while the surface brightnesses of exponential galaxies,which tend to be low luminosity, increase with luminosity.Note that constant size as a function of surface brightness would be a 45degree line on the luminosity-surface brightness plot.At all luminosities,the surface brightness increases more slowly than luminosity,indicating that the median size of galaxies increases strongly with luminosity,especially for the most luminous galaxies. 4.The colors of galaxies depend strongly on luminosity,in the sense that more luminous galaxies are redder.The color-magnitude relation is most obvious in0.1(g?r),where separate red and blue popu-lations are evident.This bimodality has also been noted in the observed u?r colors by Strateva et al. (2001)and in the D4000spectroscopic measurements of Kau?mann et al.(2002). In short,there are strong dependencies of all parameters on luminosity. 4.1.3.Dependence on Pro?le Shape The number density distribution of n,the S′e rsic index,shows a strong peak at n=1in Figures7and 9,indicating that exponential galaxies are extremely common in the universe.Note that there is no peak in the number density at n=4,which corresponds to a de Vaucouleurs pro?le,indicating that de Vaucouleurs galaxies lie along a continuum of galaxy concentration.Naturally,this result is subject to the limitations of our?t—which is based on an azimuthally averaged pro?le and uses a double gaussian approximation to the seeing.On the other hand,Figure5suggests that the e?ects of inclination are more likely to increase the observed S′e rsic indices.To test the e?ects of inclination more empirically,we can restrict our sample to galaxies which are nearly face on(axis ratio greater than0.8),according to the photo pipeline’s ab dev measurements of the best?t axis ratios assuming an exponential or de Vaucouleurs model, https://www.doczj.com/doc/b117677266.html,ing either ab dev as a measure of axis ratio does not lead the S′e rsic?ts to be closer to n=4or for any separate“concentrated”peak to appear in the distribution of S′e rsic index n. √ Similarly,if we restrict our sample to galaxies which haveχ2less than N d+ 4.The galaxies which are nearly exponential tend to be very blue in all colors(0.1(g?r)～0.4).For n>3, galaxies are extremely homogeneous:constant and red colors,constant and high surface brightness, and constant average density. 4.1.4.Dependence on Surface Brightness The third column from the right in Figure9shows the dependence of each quantity onμ0.1i; 1.Density is only a weak function of surface brightness on scales of8h?1Mpc.Note that Zehavi et al. (2002)investigate the dependence of the correlation function on surface brightness;they?nd little dependence on these scales,but a strong dependence on smaller scales. 2.Paralleling the results for the S′e rsic index,the luminosity function of high surface brightness objects is peaked at high luminosity,while at low surface brightness the luminosity function continues to increase until it passes our low luminosity limit.This result is similar to that found in SDSS commissioning data by Blanton et al.(2001)and in the2dFGRS by Cross et al.(2001). 3.The pro?les of high surface brightness galaxies are concentrated,while the pro?les of most galaxies at lower surface brightness thanμ0.1i～20are consistent with exponential. 4.The highest surface brightness objects are uniformly red,while fainter thanμ0.1i～20,surface bright- ness and color are positively correlated—the lower surface brightness a galaxy is,the bluer it tends to be. 4.1. 5.Dependence on Color The four left columns in Figures7–9show the relation between the four optical colors of galaxies and the other parameters.In Figure7,two distinct peaks in the number density distribution are visible,most notably in the relation between0.1(u?g)and0.1(g?r).This result corresponds closely to the work of Strateva et al.(2001),which found a double peaked distribution of(observer-frame)u?r color.The four left columns in Figure9show the dependence of all properties on the colors: 1.Density is a strong function of all colors.One expects this trend,based on the separate correlations of galaxy morphological classi?cation with color(Roberts&Haynes1994and references therein)and environment(for example,Dressler1980). 2.The luminosity function of galaxies which have0.1(g?r)～1.0and0.1(r?i)～0.45is peaked at high luminosity(M0.1i～?20.5).The luminosity function of bluer galaxies increases right up to our lower luminosity limit.Redder than0.1(g?r)～1.0or0.1(r?i)～0.45,the peak luminosity declines as well.This trend indicates that the reddest galaxies are a di?erent population than those right around 0.1(g?r)～1.0. 3.Blue galaxies have preferentially exponential pro?les.The concentration of galaxy pro?les is highest for galaxies with0.1(g?r)～1.0or0.1(r?i)～0.45(although,again,the median never reaches n～4) and declines at redder colors again.Again,this trend suggests that the reddest galaxies are a special population;in particular,the fact that they tend to have pro?les closer to exponential suggests that they are edge-on spiral galaxies reddened by dust.This speculation is supported by the fact that the axial ratios measured by photo for these objects are low(～0.2–0.3). 4.The half-light surface brightness increases for redder galaxies,again until0.1(g?r)～1.0or0.1(r?i)～ 0.45,at which point the surface brightness declines again.This trend may also be partly due to the fact that the reddest galaxies are probably dusty spirals;however,it is also due to the trend that at the higher luminosities,where galaxy colors are redder,galaxy half-light surface brightnesses tend to get lower with luminosity. Furthermore,the colors are clearly highly correlated among themselves.In particular,measuring0.1(g?r) constrains the other colors to very small ranges. 4.2.Dividing Galaxies by S′e rsic Index We have so far explored only two-dimensional projections of an eight-dimensional space of parameters. It is interesting to explore a third dimension of this space.To do so,we divide the galaxies into two groups according to their S′e rsic index.We choose an exponential group(n<1.5)and a concentrated group (n>3).(The results for the intermediate group look like a combination of the contributions from each group separately).Figures11and12show the conditional distribution of all our galaxy properties on all of the others for the exponential and concentrated groups,respectively.It is easy to identify which group of galaxies is considered from the panels involving the S′e rsic index—for example,there is clearly no data for n>1.5in Figure11. The diagonal panels in each plot(which can be inferred from Figure7)demonstrate that the S′e rsic index does indeed divide galaxies into red,high surface brightness,luminous galaxies and blue,low surface brightness,underluminous galaxies.In particular,the luminosity function of the exponential group clearly is rising steeply through our low luminosity limit,while the luminosity function of the concentrated group is peaked at high luminosity. Constrasting the conditional distributions of the exponential group in Figure11to those of the concen-trated group in Figure12reveals that the properties of these populations have very di?erent interrelation-ships.Most strikingly,the color-magnitude diagrams of these two classes separate extraordinarily well,into a tight red sequence(probably corresponding closely to the color–magnitude relation for morphologically elliptical galaxies Baum1959)and a less tight,but undeniable,blue sequence. Similarly,the dependence of surface brightness on luminosity di?ers markedly for the two classes.The surface brightness of exponential galaxies increases with luminosity(as does their size),while the surface brightness of concentrated galaxies decreases with their luminosity(while their size increases even more strongly).Another way of looking at the same issue is to consider the dependence of the physical half-light radius on absolute magnitude;Figure13shows this relationship for three ranges of S′e rsic index n. Again,this?gure shows that galaxy size closely correlates with luminosity,with a stronger dependence for de Vaucouleurs than for exponential galaxies.These results are comparable to those in Figure44of Bernardi et al.(2002);if one simply inverts the slope of the relationship between r50and L(which yields a correct ,close result if the relationship between the two is su?ciently linear and deterministic)we?nd L i∝r?1.67 i,50 to their result.These results also hold for the dependence of surface brightness on color;surface brightness correlates with color for exponential galaxies,but peaks at around0.1(g?r)～1.0for concentrated galaxies. The results for concentrated galaxies are in good agreement with the work of Binggeli et al.(1984)(based on morphologically elliptical galaxies). Some of the most interesting results from these?gures are the dependence of local density on galaxy properties for each type of galaxy pro?le taken separately.For example,the local density of exponential galax-ies does not depend on surface brightness;for de Vaucouleurs,local density decreases slightly with surface brightness,a fact probably related to the decrease of luminosity with surface brightness for concentrated galaxies.It is only that higher surface brightness galaxies are more concentrated,and that concentrated galaxies are on average in higher density regions,that keeps the dependence of density on surface brightness ?at for all galaxies. The dependence of the local density on luminosity has a similar shape for concentrated galaxies and exponential galaxies,in the range of luminosities in which there is data for both types(less luminous than about M0.1i～?22).However,concentrated galaxies are naturally in slightly denser regions at any given luminosity.Meanwhile,for both concentrated and exponential galaxies,density depends strongly on color. 5.Fitting Median Properties as a Function of Luminosity Figures11and12show that exponential and concentrated galaxies have colors,surface brightnesses, and local densities which are distinct from one another and which depend on galaxy luminosity.To quantify these relations,Figure14shows the1/V max-weighted median properties of both types as the boxes for exponential(thin lines)and concentrated(thick lines)galaxies(these lines are just the same as the median lines in Figures11and12).The statistical uncertainties in the median containing68%of the probability are shown as vertical lines(the width of the distribution is of course much larger). We have?t these median relations in the following manner.First,we add a minimum variance in quadrature to the uncertainties on the points,which is(0.02)2for the colors and surface brightness,and is(0.1)2for density.We do so in order that ourχ2minimization does not obsessively?t the few points with extremely tiny statistical uncertainties(who could easily be o?by a small amount due to systematic errors in the data and our processing).Next,we?t a cubic polynomial to the resulting set of points and uncertainties.To be explicit,for property p we?t the function p=p0+p1(M0.1i+21)+p2(M0.1i+21)2+p3(M0.1i+21)3,(13) where M0.1i is expressed in absolute magnitudes,the surface brightness is in magnitudes in1arcsec2,the colors are in magnitudes,and log10ρis unitless.We center the?t at M0.1i=?21because that is near the center of our luminosity range.The resulting curves are shown in Figure14as continuous lines.They are all reasonable?ts to the data over the range we have?t.The parameters p i are listed in Table1for each property and galaxy pro?le type.We also list the standard deviationsσde?ned by the distribution of galaxies around the best?t curves,both in Figure14and in Table1.However,as can be seen in Figures11 and12,gaussians of constant width are by no means adequate descriptions of the distributions around the median. These relations may be useful for comparison to theory or to other observations.They allow one to predict the median properties of an exponential or concentrated galaxy given only the absolute magnitude. We caution that because there is signi?cant scatter in all of these relations,one cannot reverse these?ts to predict absolute magnitude from other galaxy properties. 6.Accessing the Two-dimensional Distribution Data The two-dimensional projections of the number and luminosity density data used to create Figures7, 8,10,11,12,and13are available in FITS format from the electronic version of this article[NOTE TO EDITOR:These electronic tables will be required;for now these can be found at https://www.doczj.com/doc/b117677266.html,/manyd/paper/]. The FITS?le each have a single HDU containing three columns and one row.The?rst column contains M names(in string format)of the quantities listed in that?le(corresponding to the x and y axes of the ?gures).The second column contains the ranges over those M quantities included in the?gure(the?rst element of each range indicates the position of the left edge of the left-most pixel,and the second element of each range indicates the position of the right edge of the right-most pixel).The third column is a four dimensional array of dimensions(N,N,M,M)where M is the number of quantities,and N is the number of pixels on a side of each image.Each N×N subarray contains the data values used to create the greyscales in the corresponding?gure of this paper.The values in the subarray are per unit“quantity m1”per unit“quantity m2”,where m1and m2represent the position in the(M,M)sized dimensions in the four dimensional array. 7.Conclusions and Future Work In this paper,we have presented the number and luminosity density distributions of photometric galaxy properties as measured by the SDSS.Galaxy properties are highly correlated.Here are our main conclusions: 1.The most luminous galaxies comprise a homogeneous red,highly concentrated,high surface brightness population,and reside in locally dense regions.Underluminous galaxies are less homogeneous,but in general are bluer,less concentrated,lower surface brightness,and reside in less dense regions.These results are in qualitative agreement with previous work. 2.The relations among galaxy luminosities,surface brightnesses,and colors separate neatly once one has separated them into concentrated and unconcentrated(exponential)groups.Some of these relation-ships are well-known(such as the color–magnitude relation of concentrated galaxies;Baum1959)while others are less commonly discussed(such as the color–magnitude relation for exponential galaxies). 3.Local density is a strong function of luminosity,at least for the most luminous galaxies,and on color for galaxies of all colors.This dependence of clustering on galaxy type has been quanti?ed many times previously,early on in the work of Oemler(1974),later in the cluster studies of Dressler(1980),and also on larger scales by Davis&Geller(1976);Giovanelli et al.(1986);Santiago&Strauss(1992); Guzzo et al.(1997);Blanton(2000),and many others. In addition,we make several more minor observations about the distribution of galaxy properties: 1.Optical galaxy colors all correlate very strongly with0.1(g?r)color.The distribution of0.1(g?r)is double-peaked. 2.Most of the luminosity density in the universe is contained in galaxies within our range of galaxy luminosities(?23.5 3.The very reddest galaxies(which are small in number)are in optical colors exponential galaxies,not concentrated galaxies. 4.For all types of galaxies,size increases with luminosity.For concentrated galaxies,surface brightness decreases with luminosity for the most luminous galaxies.For exponential galaxies,surface brightness increases with luminosity. Simple quantitative expressions of some of these results are contained in the parametric?ts to the conditional distributions of Figure14and Table1as well as the two-dimensional projections given in the associated electronic tables.However,in future work we may characterize the distribution using a full seven-dimensional model of the distribution of properties. A potentially important e?ect to account for in future analyses is the passive evolution of galaxy lu-minosities.Measurements of this evolution in the SDSS suggest that galaxy luminosities are brighter in the past(a trend of4.2,2.0,1.6,1.6,and0.8magnitudes per unit redshift in the0.1u,0.1g,0.1r,0.1i,and 0.1z bands).While the di?erence in absolute magnitude over the whole redshift range is quite small(0.3 magnitudes)compared to the dynamic range in0.1i-band absolute magnitude,some of the colors may be a?ected signi?cantly.Because the survey is?ux limited,this e?ect can,in principle,alter the slope of our measurement of the color-magnitude relationship.For the trends quoted here,accounting for evolution would make the measured slope steeper,because the high luminosity galaxies are observed primarily at high redshift,and are observed to be bluer than they would be at the lower redshifts of the lower luminosity galaxies to which we are comparing them.For example,Bernardi et al.(2002)have found such evolution of the g?r color–magnitude diagram in their early-type galaxy SDSS sample(their Figure24).We note that one can detect the e?ect of not accounting for evolution by comparing the results of Blanton et al.(in preparation)for the evolution-corrected luminosity function at z=0.1to the1/V max-based estimate of the 0.1i-band luminosity function presented here(our luminosity function extends to slightly higher luminosities than that estimate). The scope of this paper has not allowed us to delve into a more detailed discussion of the individual relationships and their relation to galaxy formation theory.However,we could not do justice to these topics in the space and time available here,and leave this for the future,encouraging readers to use the data distributed here to perform similar work.We note that Kau?mann et al.(2002)have performed a similar analysis using?ts to stellar population models.They also see bimodality in the distribution of galaxy properties,particular in their measure of the4000?A break.In addition,they?nd that because their estimated mass-to-light ratio(in their case in the0.1z-band)is a strong(and increasing)function of mass or luminosity,the dependence of surface mass density in a galaxy on total stellar mass is monotonic,even though the dependence of surface brightness on luminosity shows a maximum surface brightness at around an absolute magnitude of M?.A particularly interesting application along these lines would be to consider the dependence of local density on stellar mass. Two important photometric parameters missing from this space are color gradients and axis ratios.As it turns out,the color gradients of galaxies in our sample are usually quite small,partly because the central bulges of our spiral galaxies are blurred with the disks by seeing.Nevertheless,using a restricted sample of nearby galaxies,one can characterize this distribution.The distribution of axis ratios is related to the three-dimensional shape of the galaxy,so understanding the conditional distribution of this quantity on other parameters will yield a better understanding of galaxy geometry.Preliminary tests have shown that the axis ratios of our red,concentrated objects tend to be close to unity,whereas the axis ratios of blue, unconcentrated objects tend to di?er signi?cantly from unity,agreeing with the common understanding of the nature of elliptical and spiral galaxies.Including a characterization of the two-dimensional shape of each galaxy would be an interesting way to expand the results found here. We began this paper by noting that many galaxy properties have been known for decades to be corre-lated,and in particular to be correlated with position on the Hubble Sequence.Many readers will ask how galaxies of di?erent morpholopgical classi?cations are distributed in this space,a question we could address using a nearby subsample of the galaxies studied here.We have not done so here because we believe that morphological classi?cation is not a su?ciently speci?ed measurement to be straightforwardly interpreted. The position along the Hubble Sequence is determined by most galaxy classi?ers from a consideration of a galaxy’s surface brightness,smoothness,concentration,axis ratio,the prominence of dust lanes,and spiral arm pitch angle.However,astronomy has not standardized the weights to be accorded by the classi?er to each of these qualities of a galaxy image when placing each galaxy along the one-dimensional Hubble Sequence.In addition,the decision about a galaxy’s classi?cation is highly dependent on the observing conditions,especially the distance of the galaxy from the observer,the dynamic range of the image,the passband of the observation,and the angle from which the galaxy is observed.Even when considering a single image and allowing only the classi?er to vary,the repeatability of classi?cation appears to be low (σ～2in units of Revised Hubble T type;Naim et al.1995).Even this level of repeatability is not clearly due to the fact that classi?ers all weight the various Hubble type criteria similarly,since the properties under consideration all correlate. Nevertheless,as reviewed by Roberts&Haynes(1994),morphological classi?cation is clearly important, since it does correlate with many physical properties of galaxies.Furthermore,we can specify certain aspects which determine morphological classi?cation.For example,many investigators have quanti?ed measures of surface brightness,concentration,smoothness/blobbiness(e.g.Naim et al.1997),and lopsidedness(e.g. Rudnick et al.2000),among others,and investigated the dependence of these measures on observing condi-tions.These e?orts,of which this paper is a part,are forming a new approach to quantitative morphology that we hope will have a more direct and better speci?ed connection to theoretical predictions. MB and DWH acknowledge NASA NAG5-11669for partial support.MB is grateful for the hospitality of the Department of Physics and Astronomy at the State University of New York at Stony Brook,who kindly provided computing facilities on his frequent visits there. Funding for the creation and distribution of the SDSS has been provided by the Alfred P.Sloan Foun-dation,the Participating Institutions,the National Aeronautics and Space Administration,the National Science Foundation,the U.S.Department of Energy,the Japanese Monbukagakusho,and the Max Planck Society.The SDSS Web site is https://www.doczj.com/doc/b117677266.html,/. The SDSS is managed by the Astrophysical Research Consortium(ARC)for the Participating Institu-tions.The Participating Institutions are The University of Chicago,Fermilab,the Institute for Advanced Study,the Japan Participation Group,The Johns Hopkins University,Los Alamos National Laboratory,the Max-Planck-Institute for Astronomy(MPIA),the Max-Planck-Institute for Astrophysics(MPA),New Mex-ico State University,University of Pittsburgh,Princeton University,the United States Naval Observatory, and the University of Washington. 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Prugniel,P.&Simien,F.1997,A&A,321,111 Richards,G.et al.2002,AJ,123,2945 Roberts,M.S.&Haynes,M.P.1994,ARA&A,32,115 Rudnick,G.,Rix,H.,&Kennicutt,R.C.2000,ApJ,538,569 Santiago,B.X.&Strauss,M.A.1992,ApJ,387,9 Schlegel,D.J.,Finkbeiner,D.P.,&Davis,M.1998,ApJ,500,525 S′e rsic,J.L.1968,Atlas de Galaxias Australes(Cordoba:Obs.Astronomico) 第六部分文件管理 1、文件系统的主要目的就是( )。 A、实现对文件的按名存取 B、实现虚拟存储 C、提供外存的读写速度 D、用于存储系统文件 2、文件系统就是指( )。 A、文件的集合 B、文件的目录集合 C、实现文件管理的一组软件 D、文件、管理文件的软件及数据结构的总体 3、文件管理实际上就是管理( )。 A、主存空间 B、辅助存储空间 C、逻辑地址空间 D、物理地址空间 4、下列文件的物理结构中,不利于文件长度动态增长的文件物理结构就是( )。 A、顺序文件 B、链接文件 C、索引文件 D、系统文件 5、下列描述不就是文件系统功能的就是( )。 A、建立文件目录 B、提供一组文件操作 C、实现对磁盘的驱动调度 D、实现从逻辑文件到物理文件间的转换 6、文件系统在创建一个文件时,为它建立一个( )。 A、文件目录 B、目录文件 C、逻辑结构 D、逻辑空间 7、索引式(随机)文件组织的一个主要优点就是( )。 A、不需要链接指针 B、能实现物理块的动态分配 C、回收实现比较简单 D、用户存取方便 8、面向用户的文件组织机构属于( )。 A、虚拟结构 B、实际结构 C、逻辑结构 D、物理结构 9、按文件用途来分,编译程序就是( )。 A、用户文件 B、档案文件 C、系统文件 D、库文件 10、将信息加工形成具有保留价值的文件就是( )。 A、库文件 B、档案文件 C、系统文件 D、临时文件 11、文件目录的主要作用就是( )。 A、按名存取 B、提高速度 C、节省空间 D、提高外存利用率 12、如果文件系统中有两个文件重名,不应采用( )。 A、一级目录结构 B、树型目录结构 C、二级目录结构 D、A与C 13、文件系统采用树型目录结构后,对于不同用户的文件,其文件名( )。 A、应该相同 B、应该不同 C、可以不同,也可以相同 D、受系统约束 14、文件系统采用二级文件目录可以( )。 A、缩短访问存储器的时间 B、实现文件共享 C、节省内存空间 D、解决不同用户间的文件命名冲突 精品文档 一.背景描述： 江南海岸总体规划和设计均体现了传统中国居家理想和现代生 活方式的有机融合，是依照21世纪人居标准精心打造的高级住宅小区。 整个小区无不营造一个舒适休闲的生活空间，是一所环境优雅，闹中 取静的花园式住宅小区，满足住户对高品质生活的追求。 二.工程说明： 江南海岸位于三明市列东区，由14栋高层住宅小区组成，总建 筑面积29.7627万平方米，其中包括4栋27层，6栋25层，4栋29 层，会所1间，负一层，一层。住户总数为1182户。 项目要求: 江南海岸，是集住宅、花园、会所于一体的高级住宅小区。小区智能化系统的工程建设具有投资大、工程复杂、专业性强等特点。小区要求建设成具有国内先进水平的，既具有自身特点，又具有时代潮流特色的高尚住宅楼宇。 整个工程规划、设计、实施上要求充分体现技术的先进性、系统的复杂性、严密的安防集控管理。注重整体功能强大，中心设备完善，系统配置科学合 理，真正体现高技术、高标准、高水平的现代化智能小区。 四.需求分析: 4.1分析与评估： 本方案以江南海岸小区住宅智能化管理及安全防范为设计目标为将力求建设成为高水平、高质量、高标准的信息化智能小区。我方提出以下见解，请发包方领导参考。 ①小区建设要求基于系统可靠、稳定、先进的基础上，既能满足用户住宅 的实际需求，同时又力求经济、实用、合理。 ②整个系统的结构要求清晰合理，小区实现全封闭管理，各个子系统既 相互关联又相对独立，形成一个全方位智能安防管理系统。 ③要求考虑未来系统扩展的需求，为小区以后系统功能的增加、升级，提 供良好的环境空间。 因此，考虑江南海岸属于大型的综合住宅小区，建筑规模庞大、结构复杂，小区各项功能模块齐备，因此在智能化建设方面，产品的集成度、统一化、高效管理方面尤为重要，同时，还必须考虑小区规模的不断扩大，智能化产品必须具备高度的扩展及冗余，顺应小区的发展。 我方进行多项分析与评估，结合小区建筑结构的分布特点、规模发展，以及对小区各功能模块的深层了解，建议江南海岸智能化系统工 /bin:是binary的缩写,这个目录是对Unix系统习惯的沿袭,存放着使用者最经常使用的命令。如:ls,cp,cat等。 /boot:这里存放的是启动Linux时使用的一些核心文档。 /dev:是device的缩写.这个目录下是任何Linux的外部设备,其功能类似Dos下的.sys 和Win下的.vxd。在Linux中设备和文档是用同种方法访问的。例如:/dev/hda代表第一个物理IDE硬盘。 /etc:这个目录用来存放任何的系统管理所需要的配置文档和子目录。 /home:用户主目录,比如说有个用户叫sina,那他的主目录就是/home/sina，说到这里打个岔.您现在应该明白，在我们访问一些个人网页。如:https://www.doczj.com/doc/b117677266.html,/sina的时候,sina就是表示访问 https://www.doczj.com/doc/b117677266.html, 站点中的用户sina的用户主目录.假如这个网站的操作系统是Linux,那就是表示/home/sina。 /lib:这个目录里存放着系统最基本的动态链接共享库,其作用类似于Windows里的.dll文档。几乎任何的应用程式都需要用到这些共享库。 /lost+found:这个目录平时是空的,当系统不正常关机后,这里就成了一些无家可归的文档的避难所。对了,有点类似于Dos下的.chk文档。 /mnt:这个目录是空的,系统提供这个目录是让用户临时挂载别的文档系统。 /proc:这个目录是个虚拟的目录,他是系统内存的映射,我们能够通过直接访问这个目录来获取系统信息。也就是说，这个目录的内容不在硬盘上而是在内存里啊。 /root:系统管理员,也叫终极权限者的用户主目录。当然系统的拥有者,总要有些特权啊。/sbin:s就是Super User的意思,也就是说这里存放的是一些系统管理员使用的系统管理程式。 /tmp:这个目录不用说,一定是用来存放一些临时文档的地方了。 /usr:这是个最庞大的目录,我们要用到的很多应用程式和文档几乎都存放在这个目录了。具体来说: /usr/X11R6:存放X-Windows的目录。 /usr/bin:存放着许多应用程式. /usr/sbin:给终极用户使用的一些管理程式就放在这. /usr/doc:这就是Linux文档的大本营. /usr/include:Linux下研发和编译应用程式需要的头文档在这里找. /usr/lib:存放一些常用的动态链接共享库和静态档案库. /usr/local:这是提供给一般用户的/usr目录,在这安装软件最适合. /usr/man:是帮助文档目录. /usr/src:Linux开放的源代码,就存在这个目录,爱好者们别放过哦! /var:这个目录中存放着那些不断在扩充着的东西,为了保持/usr的相对稳定,那些经常被修改的目录能够放在这个目录下,实际上许多系统管理员都是这样干的.顺便说一下,系统的日志文档就在/var/log目录中. /usr/local/bin 本地增加的命令 /usr/local/lib 本地增加的库根文件系统 通常情况下，根文件系统所占空间一般应该比较小，因为其中的绝大部分文件都不需要, 经常改动，而且包括严格的文件和一个小的不经常改变的文件系统不容易损坏。 除了可能的一个叫/ v m l i n u z标准的系统引导映像之外，根目录一般不含任何文件。所有其他文件在根文件系统的子目录中。 Documents and Settings是什么文件？ 答案： 是系统用户设置文件夹，包括各个用户的文档、收藏夹、上网浏览信息、配置文件等。补：这里面的东西不要随便删除，这保存着所有用户的文档和账户设置，如果删除就会重新启动不能登陆的情况，尤其是里面的default user、all users、administrator和以你当前登陆用户名的文件夹。 Favorites是什么文件？ 答案： 是收藏夹，存放你喜欢的网址。可以在其中放网址快捷方式和文件夹快捷方式，可以新建类别（文件夹）。 Program Files是什么文件？ 答案： 应用软件文件夹装软件的默认路径一般是这里！当然里面也有些系统自身的一些应用程序Common Files是什么文件？ 答案： Common Files. 这个文件夹中包含了应用程序用来共享的文件,很重要，不能乱删除Co mmon Files这个文件是操作系统包扩系统程序和应用程序Common Files是应用程序运行库文件数据库覆盖了大约1000多个最流行的应用程序的插件,补丁等等文件夹com mon files里很多都是系统文件，不能随意删除，除非确定知道是干什么用的，没用的可以删掉。不过就算删掉了有用的东西，也没大的关系，顶多是某些软件用不了，不会造成系统崩溃。 ComPlus Applications是什么文件？ 答案： ComPlus Applications：微软COM+ 组件使用的文件夹，删除后可能引起COM+ 组件不能运行 DIFX是什么文件？ 答案： 不可以删除，已有的XML数据索引方法从实现思想上可分为两类:结构归纳法和节点定位法.这两种方法都存在一定的问题,结构归纳法的缺点是索引规模较大而且难以有效支持较复杂的查询,而节点定位法的主要缺点是容易形成过多的连接操作.针对这些问题,提出了一种新的动态的XML索引体系DifX,它扩展了已有的动态索引方法,采用一种动态的Bisimil arity的概念,可以根据实际查询需求以及最优化的要求动态决定索引中保存的结构信息,以实现对各种形式的查询最有效的支持.实验结果证明DifX是一种有效而且高效的XML索引方法,其可以获得比已有的XML索引方法更高的查询执行效率. Internet Explorer是什么文件？ 答案： 不用说了，肯定不能删除，IE，浏览网页的！ Kaspersky Lab是什么文件？ 答案：卡巴斯基的文件包，这个是卡巴的报告，在C:\Documents and Settings\All Users\Application Data\Kaspersky Lab\AVP6\Report 的更新文件中有很多repor t文件很占地 智能化系统初步配置方案 一、设计原则 1、智能化系统设计，应综合考虑项目投资额度的可控性、设备选型的灵活性、工程施工的可行性、系统功能的可扩展性、系统运行维护的便利性和物业管理 的规范性等要求。 2、智能化系统的设计应参照本要求，其配置标准不得低于”初步配置方案” 的要求，同时应有一定的升级和扩展能力，并预留相应的接口。 3、智能化系统设计除了满足国家标准与规范的相关规定，以及本标准基本配 置要求外，还应满足建筑、结构以及与智能化系统存在设计相关联的其他专业 的设计规范和要求，特别需要注意符合项目当地现行有关标准和规范的特殊要求。 4、智能化系统设计与工程项目建设地点的实际情况相适应。 5、智能化系统所采用的设备及线路材料等均应符合国家现行的规定，并具有 产品合格证、质量检验证书和产品须通过国家的CCC认证。 二、智能化系统基本配置要求 1、安全防范系统 a、安全防范系统包括闭路电视监控、防盗报警系统、门禁系统、电子巡更系统及无线对讲系统。 b、闭路电视监控系统主要设置重要出入口，如公共场所、重要房间、楼梯通道、楼层电梯通道、电梯轿厢、室外主干道及交叉路口等处，电梯轿厢安装电 梯专用监控镜头并加装抗干扰器，户外监控镜头须带有红外夜视功能。所有的 监控镜头全部采用网络彩色摄像机，采用矩阵主机切换至电视墙，录像以全天 实时高清图像质量保存至少30天（具体保存时间可按照当地派出所要求确定）。 c、防盗报警系统主要设置在财务室、档案室等重要房间，并设置手动报警开关或脚挑报警开关，要求闭路电视监控系统同时显示出报警地点画面。 d、重要设备机房、财务室、档案室等重要房间设置门禁系统。 e、电子巡更系统要求采用离线式，设置于重要机房、楼层楼梯口、电梯厅。 f、无线对讲系统要求具有可进行信道改写和信道加密功能，满足内部通讯需要。 g、各系统设备品牌须选用技术成熟，性能稳定可靠的产品。 2、通讯网络系统 All Users文件夹： 『Win9x/ME』所有用户文件夹，里面里面包括系统缺省登录时的桌面文件和开始菜单的内容。『Win2000』在Win2000的系统目录中没有这个文件夹，Win2000将用户的信息放在根目录下的Documents and Settings文件夹中，每个用户对应一个目录，包括开始菜单、桌面、收藏夹、我的文档等等。 Application Data文件夹： 『Win9x/ME』应用程序数据任务栏中的快捷方式，输入法的一些文件等等。根据你系统中使用不同的软件，该目录中的内容也有所不同。 『Win2000』在Documents and Settings文件夹中，每个用户都对应一个Application Data 文件夹，同样每个用户由于使用的软件不同，目录内容也相同。 Applog文件夹： 『Win9x/ME』应用程序逻辑文件目录。逻辑文件是用来记录应用软件在运行时，需要调用的文件、使用的地址等信息的文件。要查看这些文件，用记事本打开即可。 Catroot文件夹： 『Win9x』计算机启动测试信息目录，目录中包括的文件大多是关于计算机启动时检测的硬软件信息。 『WinME』该文件夹位于系统目录的system目录中。 『Win2000』该文件夹位于系统目录的system32目录中。 Command文件夹： 『Win9x/ME』DOS命令目录。包括很多DOS下的外部命令，虽说都是些小工具，但真的很好用，特别是对于系统崩溃时。 『Win2000』这些DOS命令位于系统目录的system32目录中。 Config文件夹： 『Win9x/ME/2000』配置文件夹，目录中包括一些MIDI乐器的定义文件。 Cookies文件夹： 『Win9x/ME』Cookies又叫小甜饼，是你在浏览某些网站时，留在你硬盘上的一些资料，包括用户名、用户资料、网址等等。 『Win2000』每个用户都有一个Cookies文件夹，位于Documents and Settings文件夹的每个用户目录中。 Cursors文件夹： 『Win9x/ME/2000』鼠标动画文件夹。目录中包括鼠标在不同状态下的动画文件。 Desktop文件夹： 『Win9x/ME』桌面文件夹。包括桌面上的一些图标。 『Win2000』这个文件夹在系统目录中也存在，同时在Documents and Settings文件夹的每个用户目录中还有“桌面”文件夹。 linux下各文件夹的结构说明及用途介绍: /bin:二进制可执行命令。 /dev:设备特殊文件。 /etc:系统管理和配置文件。 /etc/rc.d:启动的配置文件和脚本。 /home:用户主目录的基点,比如用户user的主目录就是/home/user,可以用~user 表示。 /lib:标准程序设计库,又叫动态链接共享库,作用类似windows里的.dll文件。 /sbin:系统管理命令,这里存放的是系统管理员使用的管理程序。 /tmp:公用的临时文件存储点。 /root:系统管理员的主目录。 /mnt:系统提供这个目录是让用户临时挂载其他的文件系统。 /lost+found:这个目录平时是空的,系统非正常关机而留下“无家可归”的文件就在这里。 /proc:虚拟的目录,是系统内存的映射。可直接访问这个目录来获取系统信息。 /var:某些大文件的溢出区,比方说各种服务的日志文件。 /usr:最庞大的目录,要用到的应用程序和文件几乎都在这个目录。其中包含: /usr/x11r6:存放x window的目录。 /usr/bin:众多的应用程序。 /usr/sbin:超级用户的一些管理程序。 /usr/doc:linux文档。 /usr/include:linux下开发和编译应用程序所需要的头文件。 /usr/lib:常用的动态链接库和软件包的配置文件。 /usr/man:帮助文档。 /usr/src:源代码,linux内核的源代码就放在/usr/src/linux 里。 /usr/local/bin:本地增加的命令。 /usr/local/lib:本地增加的库根文件系统。 通常情况下,根文件系统所占空间一般应该比较小,因为其中的绝大部分文件都不需要经常改动,而且包括严格的文件和一个小的不经常改变的文件系统不容易损坏。除了可能的一个叫/vmlinuz标准的系统引导映像之外,根目录一般不含任何文件。所有其他文件在根文件系统的子目录中。 1. /bin目录 /bin目录包含了引导启动所需的命令或普通用户可能用的命令(可能在引导启动后。这些命令都是二进制文件的可执行程序(bin是binary的简称,多是系统中重要的系统文件。 2. /sbin目录 /sbin目录类似/bin,也用于存储二进制文件。因为其中的大部分文件多是系统管理员使用的基本的系统程序,所以虽然普通用户必要且允许时可以使用,但一般不给普通用户使用。 3. /etc目录 让你知道C盘的每个文件夹代表什么,其作用是什么 C:\Program Files文件夹介绍 列出常见的几个文件夹： 1、C:\Program Files\common files Common Files （存放软件会用到的公用库文件） 安装一些软件会在里面产生文件夹 比如visual studio symentec antivirus gtk lib 等 他是一些共享资源，这里的共享是指，一个公司所出的一系列软件都需要用这里的文件 比如：vb vc 要用里面visual studio 文件夹下的文件 norton fire wall norton antivirus 等要用里面symentec shared文件夹下的文件acrobat reader photoshop 要用里面adobe 文件夹下的文件 公有文件不能删除 正常的话会有： Microsoft Shared MSSoap ODBC SpeechEngines System Direct X Common Files这个文件是操作系统包扩系统程序和应用程序 Common Files是应用程序运行库文件 数据库覆盖了大约1000多个最流行的应用程序的插件,补丁等等 文件夹common files里很多都是系统文件，不能随意删除，除非确定知道是干什么用的，没用的可以删掉。不过就算删掉了有用的东西，也没大的关系，顶多是某些软件用不了，不会造成系统崩溃。 另外也有说各种软件的注册信息也在里面。 2、C:\Program Files\ComPlus Applications ComPlus Applications：微软COM+ 组件使用的文件夹，删除后可能引起COM+ 组件不能运行 显示名称：COM+ System Application ◎微软描述：管理基于COM+ 组件的配置和跟踪。如果服务停止，大多数基于 COM+ 组件将不能正常工作。如果本服务被禁用，任何明确依赖它的服务都将不能启动。 ◎补充描述：如果 COM+ Event System 是一台车，那么 COM+ System Application 就是司机，如事件检视器内显示的 DCOM 没有启用。一些 COM+软件需要，检查你的C:\Program Files\ComPlus 3、C:\Program Files\InstallShield Installation Information 发现这个网上一些解释的很含糊，说是用来存放部分软件安装信息的文件夹。比较准确的说 题目：请比较PageRank算法和HITS算法的优缺点，除此之外，请再介绍2种用于搜索引擎检索结果的排序算法，并举例说明。 答： 1998年，Sergey Brin和Lawrence Page[1]提出了PageRank算法。该算法基于“从许多优质的网页链接过来的网页，必定还是优质网页”的回归关系，来判定网页的重要性。该算法认为从网页A导向网页B的链接可以看作是页面A对页面B的支持投票，根据这个投票数来判断页面的重要性。当然，不仅仅只看投票数，还要对投票的页面进行重要性分析，越是重要的页面所投票的评价也就越高。根据这样的分析，得到了高评价的重要页面会被给予较高的PageRank值，在检索结果内的名次也会提高。PageRank是基于对“使用复杂的算法而得到的链接构造”的分析，从而得出的各网页本身的特性。 HITS 算法是由康奈尔大学( Cornell University ) 的JonKleinberg 博士于1998 年首先提出。Kleinberg认为既然搜索是开始于用户的检索提问，那么每个页面的重要性也就依赖于用户的检索提问。他将用户检索提问分为如下三种：特指主题检索提问（specific queries，也称窄主题检索提问）、泛指主题检索提问（Broad-topic queries，也称宽主题检索提问）和相似网页检索提问（Similar-page queries）。HITS 算法专注于改善泛指主题检索的结果。 Kleinberg将网页（或网站）分为两类，即hubs和authorities，而且每个页面也有两个级别，即hubs（中心级别）和authorities（权威级别）。Authorities 是具有较高价值的网页，依赖于指向它的页面；hubs为指向较多authorities的网页，依赖于它指向的页面。HITS算法的目标就是通过迭代计算得到针对某个检索提问的排名最高的authority的网页。 通常HITS算法是作用在一定范围的，例如一个以程序开发为主题的网页，指向另一个以程序开发为主题的网页，则另一个网页的重要性就可能比较高，但是指向另一个购物类的网页则不一定。在限定范围之后根据网页的出度和入度建立一个矩阵，通过矩阵的迭代运算和定义收敛的阈值不断对两个向量authority 和hub值进行更新直至收敛。 从上面的分析可见，PageRank算法和HITS算法都是基于链接分析的搜索引擎排序算法，并且在算法中两者都利用了特征向量作为理论基础和收敛性依据。 > 系统目录WINDOWS下主要文件夹简介 时间:2008-08-14 12:26来源:网络作者:未知点击:599次 WINDOWS系统目录下各个核心文件夹作用及用途介绍,详细完 ├—WINDOWS │ ├—system32（存放Windows的系统文件和硬件驱动程序） │ │ ├—config（用户配置信息和密码信息） │ │ │ └—systemprofile（系统配置信息，用于恢复系统） │ │ ├—drivers（用来存放硬件驱动文件，不建议删除） │ │ ├—spool（用来存放系统打印文件。包括打印的色彩、打印预存等） │ │ ├—wbem（存放WMI测试程序，用于查看和更改公共信息模型类、实例和方法等。请勿删除） │ │ ├—IME（用来存放系统输入法文件，类似WINDOWS下的IME文件夹） │ │ ├—CatRoot（计算机启动测试信息目录，包括了计算机启动时检测的硬软件信息） │ │ ├—Com（用来存放组件服务文件） │ │ ├—ReinstallBackups（电脑中硬件的驱动程序备份） │ │ ├—DllCache（用来存放系统缓存文件。当系统文件被替换时，文件保护机制会复制这个文件夹下的文件去覆盖非系统文件） │ │ ├—GroupPolicy（组策略文件夹） │ │ │ ├—system（系统文件夹，用来存放系统虚拟设备文件） │ ├—$NtUninstall$（每给系统打一个补丁，系统就会自动创建这样的一个目录，可删除） │ ├—security（系统安全文件夹，用来存放系统重要的数据文件） │ ├—srchasst（搜索助手文件夹，用来存放系统搜索助手文件，与msagent文件 Word目录编排功能应用 录是一个文档中不可缺少的一部分，目录的内容通常都是 由各级标题及其所在页的页码组成，目的在于方便阅读者直接查询有关内容的页码。 目录编排是档案编研工作中必不可少的一项工作。如果用手工完成一个文档目录的建立，将是一件机械而容易出错的工作，因为您必须一字不错的将每个标题的内容照抄到相应的目录中，而且要将该标题所在页的页号正确无误地记录下来。当文档中的标题被修改后，您又必须手工更新目录的内容，稍有不慎，生成的目录就会题文不符。笔者在工作中使用Word目录自动编排功能，觉得方便又准确，在此介绍给同行，以供参考。 Word提供了根据文档中标题样式段落的内容自动生成目录的功能。您可以通过控制创建目录的标题级别数来控制目录的级别数。但文档中的标题一定要使用相应的标题样式，否则，Word就不能按标题样式自动创建目录。 Word共提供了9级目录格式，它们一般为“目录1”、“目录2”、“目录3”、……“目录9”。“目录1”的内容一般为“标题1”的内容，如文章的每一章的标题，参考文献、附录、索引等标题，但整个文档的大题目、目录标题的“目录”、前言的标题“前言”等不能作为“目录1”中的内容出现在目录1中。“目录2 ”的内容为“标题2”的内容，“目录3”为“标题3”的内容，以此类推。文档的目录一般只需要3级，最多不超过4级或5级。 一、目录的生成 目录的生成一般都在文档写作完成后才进行。其生成过程就是目录域的插入。要插入目录，首先要选择目录的插入点，一般都选择在 文档正文之前，并将光标定位到该插入点。具体操作步骤如下： 1、 选择?插入(I)?菜单中的〖分隔 符(B)〗命令后，屏幕上显示“分隔符” 对话框，右图所示，点击〖分页符〗中 的“下一页”选项，然后再单击?确定? 按钮。（主要功能是将目录与正文的页 码断开，即确保目录和正文的页码都分 别从“1”开始。） 2、 将光标 定位在分节符 前，选择?插入 (I)?菜单中的〖索 引和目录(D)…〗 命令后，屏幕上 显示“索引和目 录”对话框，如右 上图所示。 3、 激活〖目录(C)〗选项后，在〖格式(T)〗选项下选择合适的目录格式，并在对话框中部查看其预览效果。 4、 在〖显示级别(L)〗选项下输入不同数字以改变目录中包含的标题样式级别数目。 5、 单击?确定?按钮，就会将 目录添加到文档中。右图所示的是用 Word 自动生成的《科研成果简介）》 目录。 二、目录的更新 当对文档的标题作了修改之后， 自然需要对新文档的目录进行更新。如果用手工更新目录的话，就要 linux下各文件夹的结构说明及用途介绍： /bin 二进制可执行命令 /dev 设备特殊文件 /etc 系统管理和配置文件 /etc/rc.d 启动的配置文件和脚本 /home 用户主目录的基点，比如用户user的主目录就是/home/user，可以用~user表示 /lib 标准程序设计库，又叫动态链接共享库，作用类似windows里的.dll文件 /sbin 系统管理命令，这里存放的是系统管理员使用的管理程序 /tmp 公用的临时文件存储点 /root 系统管理员的主目录（呵呵，特权阶级） /mnt 系统提供这个目录是让用户临时挂载其他的文件系统。 /lost+found 这个目录平时是空的，系统非正常关机而留下“无家可归”的文件（windows 下叫什么.chk）就在这里 /proc 虚拟的目录，是系统内存的映射。可直接访问这个目录来获取系统信息。 /var 某些大文件的溢出区，比方说各种服务的日志文件 /usr 最庞大的目录，要用到的应用程序和文件几乎都在这个目录。其中包含： /usr/x11r6 存放x window的目录 /usr/bin 众多的应用程序 /usr/sbin 超级用户的一些管理程序 /usr/doc linux文档 /usr/include linux下开发和编译应用程序所需要的头文件 /usr/lib 常用的动态链接库和软件包的配置文件 /usr/man 帮助文档 /usr/src 源代码，linux内核的源代码就放在/usr/src/linux里 /usr/local/bin 本地增加的命令 /usr/local/lib 本地增加的库根文件系统 通常情况下，根文件系统所占空间一般应该比较小，因为其中的绝大部分文件都不需要经常改动，而且包括严格的文件和一个小的不经常改变的文件系统不容易损坏。 除了可能的一个叫/ v m l i n u z标准的系统引导映像之外，根目录一般不含任何文件。所有其他文件在根文件系统的子目录中。 1. /bin目录 / b i n目录包含了引导启动所需的命令或普通用户可能用的命令(可能在引导启动后)。这些 命令都是二进制文件的可执行程序( b i n是b i n a r y - -二进制的简称)，多是系统中重要的系统文件。 2. /sbin目录 / s b i n目录类似/bin ，也用于存储二进制文件。因为其中的大部分文件多是系统管理员使用的基本的系统程序，所以虽然普通用户必要且允许时可以使用，但一般不给普通用户使用。 3. /etc目录 / e t c目录存放着各种系统配置文件，其中包括了用户信息文件/ e t c / p a s s w d，系统初始化文件/ e t c / r c等。l i n u x正是*这些文件才得以正常地运行。 4. /root目录 /root 目录是超级用户的目录。 Windows下各个文件夹的作用分别是什么 更新时间：2011-08-21 作者：来源：访问： ├—WINDOWS │├—system32(存放Windows的系统文件和硬件驱动程序) ││├—config(用户配置信息和密码信息) │││└—systemprofile(系统配置信息，用于恢复系统) ││├—drivers(用来存放硬件驱动文件，不建议删除) ││├—spool(用来存放系统打印文件。包括打印的色彩、打印预存等) ││├—wbem(存放WMI测试程序，用于查看和更改公共信息模型类、实例和方法等。请勿删除) ││├—IME(用来存放系统输入法文件，类似WINDOWS下的IME文件夹) ││├—CatRoot(计算机启动测试信息目录，包括了计算机启动时检测的硬软件信息) ││├—Com(用来存放组件服务文件) ││├—ReinstallBackups(电脑中硬件的驱动程序备份) ││├—DllCache(用来存放系统缓存文件。当系统文件被替换时，文件保护机制会复制这个文件夹下的文件去覆盖非系统文件) ││├—GroupPolicy(组策略文件夹) ││ │├—system(系统文件夹，用来存放系统虚拟设备文件) │├—$NtUninstall$(每给系统打一个补丁，系统就会自动创建这样的一个目录，可删除) │├—security(系统安全文件夹，用来存放系统重要的数据文件) │├—srchasst(搜索助手文件夹，用来存放系统搜索助手文件，与msagent文件夹类似) │├—repair(系统修复文件夹，用来存放修复系统时所需的配置文件) │├—Downloaded Program Files(下载程序文件夹，用来存放扩展IE功能的ActiveX等插件) │├—inf(用来存放INF文件。INF文件最常见的应用是为硬件设备提供驱动程序服务，不建议删除其中文件) │├—Help(Windows帮助文件) │├—Config(系统配置文件夹，用来存放系统的一些临时配置的文件) │├—msagent(微软助手文件夹，存放动态的卡通形象，协助你更好地使用系统。若觉的没有必要，可直接删除) │├—Cursors(鼠标指针文件夹) │├—Media(声音文件夹，开关机等wav文件存放于此) │├—Mui(多语言包文件夹，用来存放多国语言文件。简体中文系统中这个文件夹默认是空的，但不建议删除此文件夹) │├—java(存放java运行的组件及其程序文件。不建议删除其中文件) │├—Web ││├—Wall*****(存放桌面壁纸的文件夹) ││ │├—addins(系统附加文件夹，用来存放系统附加功能的文件) │├—Connection Wizard(连接向导文件夹，用来存放“Internet连接向导”的相关文件) │├—Driver Cache(驱动缓存文件夹，用来存放系统已知硬件的驱动文件) ││└—i386(Windows操作系统自带的已知硬件驱动文件，可删除以节省空间) ----------给你一个WINDOWS 文件夹内容的介绍,看了你就明白了. ├—WINDOWS │ ├—system32（存放Windows的系统文件和硬件驱动程序） │ │ ├—config（用户配置信息和密码信息） │ │ │ └—systemprofile（系统配置信息，用于恢复系统） │ │ ├—drivers（用来存放硬件驱动文件，不建议删除） │ │ ├—spool（用来存放系统打印文件。包括打印的色彩、打印预存等） │ │ ├—wbem（存放WMI测试程序，用于查看和更改公共信息模型类、实例和方法等。请勿删除） │ │ ├—IME（用来存放系统输入法文件，类似WINDOWS下的IME文件夹） │ │ ├—CatRoot（计算机启动测试信息目录，包括了计算机启动时检测的硬软件信息） │ │ ├—Com（用来存放组件服务文件） │ │ ├—ReinstallBackups（电脑中硬件的驱动程序备份） │ │ ├—DllCache（用来存放系统缓存文件。当系统文件被替换时，文件保护机制会复制这个文件夹下的文件去覆盖非系统文件） │ │ ├—GroupPolicy（组策略文件夹） │ │ │ ├—system（系统文件夹，用来存放系统虚拟设备文件） │ ├—$NtUninstall$（每给系统打一个补丁，系统就会自动创建这样的一个目录，可删除） │ ├—security（系统安全文件夹，用来存放系统重要的数据文件） │ ├—srchasst（搜索助手文件夹，用来存放系统搜索助手文件，与msagent文件夹类似） │ ├—repair（系统修复文件夹，用来存放修复系统时所需的配置文件） 多功能厅智能化系统 一、工程概况 多功能厅的智能化系统应是功能先进，设施完备，应当具有智能性、先进性、兼容性及可扩展性。该会议系统将高质量的音频、视频和诸多智能特性紧密结合在一起，通过数字信号处理、压缩编码技术和数据传输等新技术的融入，实现更多、更齐全的会议会话、视频传输、信息传输功能。方案设计要根据具体需求分析，针对性地展开设计，以满足各多功能厅的具体要求。 设计方案应集成当今世界多媒体集成系统的各种现代化设备，如使用会议同声传译发言表决系统，使会议进程具有高雅简洁的特点；大屏幕显示的应用可以提高大厅的规格档次，具有清晰明亮、色彩丰富、逼真的及动态视觉效果；选用专业化的音频处理设备，以最佳状态处理声音效果，使话音清晰均匀，演示效果明显；高清晰度及高精度云台摄像监视设备完全满足现代化监控的摄录及新闻发布的要求；所有的视听设备均可通过智能中央控制系统实现遥控操作，使系统具有完美的整体性和高度可靠性，操作平台可选用彩色液晶显触摸屏，控制涉及大厅系统的每个子系统，真正实现了智能化控制。 系统的设计思想是现代化、高起点、高质量、高可靠性、高完整性。既具备现代化大厅的各种使用功能，又可以完成高质量的视听节目播放等特殊使用要求，高度自动化的操控方式使其具备各种功能任意选用的特点。 二、系统设计依据 本项目中所指定系统之设计的依据和设计规范标准为： ●多功能厅的需求报告； ●多功能厅的相关技术图纸； ●《智能建筑设计规范》GBJ08-47-95； ●《智能建筑设计标准》GB/T 50314-2000 ； ●《30-1GHz声音和电视信号的电缆分配系统》GB6510-86； ●《建筑电气安装工程质量检验评定标准》GBJ-303-88； ●《民用建筑电气设计规范》JGJ/T16-92； ●《厅堂扩声系统声学特性指标》GYJ25-86； ●《声系统设备互连的优选配接值》GB14197-93； ●《厅堂混响时间测量规范》GBJ76-84 ●《厅堂扩声特性测量法》GB/T4959-1995； ●《客观评价厅堂语言可懂度的RASTI法》GB/T14476-93； ●《中华人民共和国国家行业标准》； PageRank算法实验报告 一、算法介绍 PageRank是Google专有的算法，用于衡量特定网页相对于搜索引擎索引中的其他网页而言的重要程度。它由Larry Page 和Sergey Brin在20世纪90年代后期发明。PageRank实现了将链接价值概念作为排名因素。 PageRank的核心思想有2点： 1.如果一个网页被很多其他网页链接到的话说明这个网页比较重要，也就是pagerank值会相对较高； 2.如果一个pagerank值很高的网页链接到一个其他的网页，那么被链接到的网页的pagerank值会相应地因此而提高。 若页面表示有向图的顶点，有向边表示链接，w(i,j)=1表示页面i存在指向页面j的超链接，否则w(i,j)=0。如果页面A存在指向其他页面的超链接，就将A 的PageRank的份额平均地分给其所指向的所有页面，一次类推。虽然PageRank 会一直传递，但总的来说PageRank的计算是收敛的。 实际应用中可以采用幂法来计算PageRank，假如总共有m个页面，计算如公式所示： r=A*x 其中A=d*P+（1-d)*(e*e'/m) r表示当前迭代后的PageRank，它是一个m行的列向量,x是所有页面的PageRank初始值。 P由有向图的邻接矩阵变化而来，P'为邻接矩阵的每个元素除以每行元素之和得到。 e是m行的元素都为1的列向量。 二、算法代码实现 三、心得体会 在完成算法的过程中，我有以下几点体会： 1、在动手实现的过程中，先将算法的思想和思路理解清楚，对于后续动手实现 有很大帮助。 2、在实现之前，对于每步要做什么要有概念，然后对于不会实现的部分代码先 查找相应的用法，在进行整体编写。 3、在实现算法后，在寻找数据验证算法的过程中比较困难。作为初学者，对于 数据量大的数据的处理存在难度，但数据量的数据很难寻找，所以难以进行实例分析。 目录树的主要部分有root（/）、/usr、/var、/home等等。下面是一个典型的linux 目录结构如下： / 根目录 /bin 存放必要的命令 /boot 存放内核以及启动所需的文件等 /dev 存放设备文件 /etc 存放系统的配置文件 /home 用户文件的主目录，用户数据存放在其主目录中 /lib 存放必要的运行库 /mnt 存放临时的映射文件系统，我们常把软驱和光驱挂装在这里的floppy和cdrom子目录下。 /proc 存放存储进程和系统信息 /root 超级用户的主目录 /sbin 存放系统管理程序 /tmp 存放临时文件的目录 /usr 包含了一般不需要修改的应用程序，命令程序文件、程序库、手册和其它文档。 /var 包含系统产生的经常变化的文件，例如打印机、邮件、新闻等假脱机目录、日志文件、格式化后的手册页以及一些应用程序的数据文件等等。建议单独的放在一个分区。[separator] 典型的/usr目录如下： /X11R6 存放X window系统 /bin 存放增加的用户程序 /dict 存放字典 /doc 存放追加的文档 /etc 存放设置文件 /games 存放游戏和教学文件 /include 存放C开发工具的头文件 /info 存放GNU信息文件 /lib 存放库文件 /local 存放本地产生的增加的应用程序 /man 存放在线帮助文件 /sbin 存放增加的管理程序 /share 存放结构独立的数据 /src 存放程序的源代码 由于/usr中的文件不和特定的计算机相关，也不会在通常使用中修改，因此可以通过网络共享这个目录（文件系统），这样，当管理员安装了新的软件之后，所有共享这一文件系统的计算机均可以使用新的软件。 Linux继承了unix操作系统结构清晰的特点。在linux下的文件结构非常有条理。但是，上述的优点只有在对linux相当熟悉时，才能体会到。现在，虫虫就把linux 下的目录结构简单介绍一下。 一、建筑概况 酒店位于广州市珠江新城猎德，酒店建筑面积50294平方米，建筑高度100m，建筑层数地上25层，地下4层，客房数约357间。 二、设计依据 1、甲方提供的建筑、结构、强电、给排水、暖通等相关专业提供的设计资料及要求。 2、希尔顿酒店管理公司《机电设计标准》（弱电系统部分） 3、国家现行的相关设计标准、规范： （1）《高层民用建筑设计防火规范》（GB50045-95）2005年版 （2）《民用建筑电气设计规范》（JGJ 16-2008） （3）《火灾自动报警系统设计规范》（GB50116-2013） （4）《智能建筑设计标准》（GB/T 50314-2006） （5）《综合布线系统工程设计规范》（GB 50311-2007） （6）《安全防范工程技术规范》（GB 50348-2004） （7）《入侵报警系统工程技术规范》（GB 50394-2007） （8）《视频安防监控系统工程技术规范》（GB 50395-2007） （9）《出入口控制系统工程技术规范》（GB 50396-2007） （10）《电子信息系统机房设计规范》（GB50174-2008） （11）《民用建筑设计通则》（GB50352-2005） （12）《低压配电设计规范》（GB50054-2011） （13）《供配电系统设计规范》（GB50052-2009） （14）《电力工程电缆设计规范》（GB50217-2007） （15）《通用用电设备配电设计规范》(GB50055-2011) （16）《建筑照明设计标准》（GB50034-2013） （17）《建筑物防雷设计规范》（GB50057-2010） （18）《建筑物电子信息系统防雷设计技术规范》（GB 50343-2012） （19）《电力装置的电测量仪表装置设计规范》(GB50063-90) （20）《公共建筑节能设计标准》（GB 50189－2005） （21）《公共建筑节能设计标准》广东省实施细则（DBJ 15－51－2007） 4、建设单位、物业管理单位对设计的意见及需求 如何理解网页和网页之间的关系，特别是怎么从这些关系中提取网页中除文字以外的其他特性。这部分的一些核心算法曾是提高搜索引擎质量的重要推进力量。另外，我们这周要分享的算法也适用于其他能够把信息用结点与结点关系来表达的信息网络。 今天，我们先看一看用图来表达网页与网页之间的关系，并且计算网页重要性的经典算法：PageRank。 PageRank 的简要历史 时至今日，谢尔盖·布林（Sergey Brin）和拉里·佩奇（Larry Page）作为Google 这一雄厚科技帝国的创始人，已经耳熟能详。但在1995 年，他们两人还都是在斯坦福大学计算机系苦读的博士生。那个年代，互联网方兴未艾。雅虎作为信息时代的第一代巨人诞生了，布林和佩奇都希望能够创立属于自己的搜索引擎。1998 年夏天，两个人都暂时离开斯坦福大学的博士生项目，转而全职投入到Google 的研发工作中。他们把整个项目的一个总结发表在了1998 年的万维网国际会议上（WWW7，the seventh international conference on World Wide Web）（见参考文献[1]）。这是PageRank 算法的第一次完整表述。 PageRank 一经提出就在学术界引起了很大反响，各类变形以及对PageRank 的各种解释和分析层出不穷。在这之后很长的一段时间里，PageRank 几乎成了网页链接分析的代名词。给你推荐一篇参考文献[2]，作为进一步深入了解的阅读资料。 PageRank 的基本原理 我在这里先介绍一下PageRank 的最基本形式，这也是布林和佩奇最早发表PageRank 时的思路。 首先，我们来看一下每一个网页的周边结构。每一个网页都有一个“输出链接”（Outlink）的集合。这里，输出链接指的是从当前网页出发所指向的其他页面。比如，从页面A 有一个链接到页面B。那么B 就是A 的输出链接。根据这个定义，可以同样定义“输入链接”（Inlink），指的就是指向当前页面的其他页面。比如，页面C 指向页面A，那么C 就是A 的输入链接。 有了输入链接和输出链接的概念后，下面我们来定义一个页面的PageRank。我们假定每一个页面都有一个值，叫作PageRank，来衡量这个页面的重要程度。这个值是这么定义的，当前页面I 的PageRank 值，是I 的所有输入链接PageRank 值的加权和。 那么，权重是多少呢？对于I 的某一个输入链接J，假设其有N 个输出链接，那么这个权重就是N 分之一。也就是说，J 把自己的PageRank 的N 分之一分给I。从这个意义上来看，I 的PageRank，就是其所有输入链接把他们自身的PageRank 按照他们各自输出链接的比例分配给I。谁的输出链接多，谁分配的就少一些；反之，谁的输出链接少，谁分配的就多一些。这是一个非常形象直观的定义。操作系统文件管理_答案
智能化系统建设方案
linux下各目录作用和功能
告诉你C盘里面每个文件夹是什么作用
智能化系统配置项目及要求
文件夹的作用
linux下各文件夹的结构说明及用途详细介绍解析
让你知道C盘的每个文件夹代表什么,其作用是什么
比较PageRank算法和HITS算法的优缺点
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