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At least 55 records · Page 3

Estimating regression coefficients from clustered samples: Sampling errors and optimum sample allocation

A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.

Kalton, G.↗

HEAO A-2 observations of non-Abell Zwicky clusters containing extended radio sources

Burns and Owen's sample of 25 4C radio sources which coincide with Zwicky clusters of galaxies has been searched for X-ray emission using the HEAO 1 A-2 experiment. X-ray emission was detected from five sources at the 3sigma level, two of which exceeded 5sigma. Positions and 90% X-ray error boxes are given for each of the five sources detected. The clusters surveyed are non-Abell clusters which would generally not be expected to be detectable with the A-2 experiment. The search for X-ray emission was prompted by the knowledge of the existence of extended radio sources in the clusters. The relaxed structure of these sources implies the presence of a relatively dense intracluster medium which is expected to produce thermal bremsstrahlung X-ray emission.

Holman, G. D.↗

Using arcs in clusters of galaxies as probes to cluster potentials and the faint blue galaxies

An extensive study of the statistical properties of highly magnified gravitational images of background galaxies produced by clusters of galaxies (generally known as arcs) is undertaken. After summarizing the results obtained from present observations (described in detail elsewhere), techniques are described for observations to improve upon these results, and to obtain further information on the potentials of the lensing clusters and the faint blue galaxies acting as sources. The most important information that can be obtained from the arcs is, with respect to the clusters, on the total density profile and the substructure at the small scales probed by the arcs, and, with respect to the background sources, on their angular sizes and redshifts.

Miralda-Escude, Jordi↗

Mapping of terrain by computer clustering techniques using multispectral scanner data and using color aerial film

Two clustering techniques were used for terrain mapping by computer of test sites in Yellowstone National Park. One test was made with multispectral scanner data using a composite technique which consists of (1) a strictly sequential statistical clustering which is a sequential variance analysis, and (2) a generalized K-means clustering. In this composite technique, the output of (1) is a first approximation of the cluster centers. This is the input to (2) which consists of steps to improve the determination of cluster centers by iterative procedures. Another test was made using the three emulsion layers of color-infrared aerial film as a three-band spectrometer. Relative film densities were analyzed using a simple clustering technique in three-color space. Important advantages of the clustering technique over conventional supervised computer programs are (1) human intervention, preparation time, and manipulation of data are reduced, (2) the computer map, gives unbiased indication of where best to select the reference ground control data, (3) use of easy to obtain inexpensive film, and (4) the geometric distortions can be easily rectified by simple standard photogrammetric techniques.

Smedes, H. W.↗

Neutron stars and millisecond pulsars from accretion-induced collapse in globular clusters

This paper examines the limits on the number of millisecond pulsars which could be formed in globular clusters by the generally accepted scenario (in which a neutron star is created by the supernova of an initially massive star and subsequently captures a companion to form a low-mass X-ray binary which eventually becomes a millisecond pulsar). It is found that, while the number of observed low-mass X-ray binaries can be adequately explained in this way, the reasonable assumption that the pulsar luminosity function in clusters extends below the current observational limits down to the luminosity of the faintest millisecond pulsars in the field suggests a cluster population of millisecond pulsars which is substantially larger than the standard model can produce. Alleviating this problem by postulating much shorter lifetimes for the X-ray binaries requires massive star populations sufficiently large that the mass loss resulting from their evolution would be likely to unbind the cluster. It is argued that neutron star formation in globular clusters by accretion-induced collapse of white dwarfs may resolve the discrepancy in birthrates.

Bailyn, Charles D.↗

The composite sequential clustering technique for analysis of multispectral scanner data

The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

Su, M. Y.↗

Unsupervised classification of earth resources data.

A new clustering technique is presented. It consists of two parts: (a) a sequential statistical clustering which is essentially a sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by existing supervised maximum liklihood classification technique.

Su, M. Y.↗

An unsupervised classification technique for multispectral remote sensing data.

Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.

Su, M. Y.↗

Dynamic Simulation and Stability Analysis

Dynamic Interaction Simulation of Controls and Structure (DISCOS) program developed for dynamic simulation and stability analysis of passive and actively controlled spacecraft. In use of DISCOS, physical system undergoing analysis generally described as cluster of contiguous flexible structures (bodies) that comprise mechanical system, such as spacecraft.

Frisch, H. P.↗

Unsupervised classification of remote multispectral sensing data

The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.

Su, M. Y.↗

A hill-sliding strategy for initialization of Gaussian clusters in the multidimensional space

A hill sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimate of sample data for the first step of iterative unsupervised classification. Each cluster was assumed to posses a unimodal normal distribution. A clustering function proposed distinguished elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with LANDSAT multispectral scanner data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill sliding clustering technique developed herein has the potential applicability to decomposition any multivariate mixture distribution into a number of unimodal distributions when an appropriate distribution function to the data set is employed.

Park, J. K.↗

The Gas Distribution in Galaxy Cluster Outer Regions

Aims. We present the analysis of a local (z = 0.04 - 0.2) sample of 31 galaxy clusters with the aim of measuring the density of the X-ray emitting gas in cluster outskirts. We compare our results with numerical simulations to set constraints on the azimuthal symmetry and gas clumping in the outer regions of galaxy clusters. Methods. We exploit the large field-of-view and low instrumental background of ROSAT/PSPC to trace the density of the intracluster gas out to the virial radius. We perform a stacking of the density profiles to detect a signal beyond r200 and measure the typical density and scatter in cluster outskirts. We also compute the azimuthal scatter of the profiles with respect to the mean value to look for deviations from spherical symmetry. Finally, we compare our average density and scatter profiles with the results of numerical simulations. Results. As opposed to some recent Suzaku results, and confirming previous evidence from ROSAT and Chandra, we observe a steepening of the density profiles beyond approximately r(sub 500). Comparing our density profiles with simulations, we find that non-radiative runs predict too steep density profiles, whereas runs including additional physics and/or treating gas clumping are in better agreement with the observed gas distribution. We report for the first time the high-confidence detection of a systematic difference between cool-core and non-cool core clusters beyond 0.3r(sub 200), which we explain by a different distribution of the gas in the two classes. Beyond r(sub 500), galaxy clusters deviate significantly from spherical symmetry, with only little differences between relaxed and disturbed systems. We find good agreement between the observed and predicted scatter profiles, but only when the 1% densest clumps are filtered out in the simulations. Conclusions. Comparing our results with numerical simulations, we find that non-radiative simulations fail to reproduce the gas distribution, even well outside cluster cores. Although their general behavior is in better agreement with the observations, simulations including cooling and star formation convert a large amount of gas into stars, which results in a low gas fraction with respect to the observations. Consequently, a detailed treatment of gas cooling, star formation, AGN feedback, and taking into account gas clumping is required to construct realistic models of cluster outer regions.

Eckert, D.↗

Uneven Inflation Load Share Trends in Clusters

The use of parachute clusters for payload recovery is still seeing widespread use ever since the early days of WWII. By involving the (near) simultaneous deployment of several smaller and identical canopies connected to the payload, cluster systems offer flexibility in tailoring to needed descent rates and load management, as well as providing robustness against individual canopy deployment or opening failure. Their downside, of course, resides in the possibility of differing inflation rates by each cluster member as caused by deployment variability, canopy-to-canopy interference, etc. Such variability leads to the lead-lag phenomenon, which causes uneven loading among the parachutes, often times leaving a single canopy to take up a significant portion of the system’s inflation loads. Herein we investigate how serious such an effect can be in terms of the number N of cluster members, underinflation drag of the lagging canopies, inflation swiftness of the leader in comparison to the laggards’, disreefing cutter activation staggering and pre-disreefing drag area. Two new metrics are used to highlight load share unevenness, namely, the peak and average leader canopy riser load in comparison to the leader’s drag during no-lead-lag; and leader peak load, as compared to total peak load. Results are calculated from data collected in NASA’s Orion/CPAS test program, as well as from simple algebraic expressions informing leader drag as sustained in different cluster systems (i.e., of different N) and varying leader inflation time relative to the laggards. Generally, using large-N cluster systems confers better load sharing among canopies. However, and in deployments where significant lead-lag occur, large-N systems may feature greater leader overload excursions, oftentimes in excess of 50% the no-lead-lag levels. These excursions are particularly made worse when cutter activation among the members are far from simultaneous and the laggards’ pre-disreefing drag area is small in comparison to the leader’s.

parachutes↗

The Gas Distribution in the Outer Regions of Galaxy Clusters

Aims. We present our analysis of a local (z = 0.04 - 0.2) sample of 31 galaxy clusters with the aim of measuring the density of the X-ray emitting gas in cluster outskirts. We compare our results with numerical simulations to set constraints on the azimuthal symmetry and gas clumping in the outer regions of galaxy clusters. Methods. We have exploited the large field-of-view and low instrumental background of ROSAT/PSPC to trace the density of the intracluster gas out to the virial radius, We stacked the density profiles to detect a signal beyond T200 and measured the typical density and scatter in cluster outskirts. We also computed the azimuthal scatter of the profiles with respect to the mean value to look for deviations from spherical symmetry. Finally, we compared our average density and scatter profiles with the results of numerical simulations. Results. As opposed to some recent Suzaku results, and confirming previous evidence from ROSAT and Chandra, we observe a steepening of the density profiles beyond approximately r(sub 500). Comparing our density profiles with simulations, we find that non-radiative runs predict density profiles that are too steep, whereas runs including additional physics and/ or treating gas clumping agree better with the observed gas distribution. We report high-confidence detection of a systematic difference between cool-core and non cool-core clusters beyond approximately 0.3r(sub 200), which we explain by a different distribution of the gas in the two classes. Beyond approximately r(sub 500), galaxy clusters deviate significantly from spherical symmetry, with only small differences between relaxed and disturbed systems. We find good agreement between the observed and predicted scatter profiles, but only when the 1% densest clumps are filtered out in the ENZO simulations. Conclusions. Comparing our results with numerical simulations, we find that non-radiative simulations fail to reproduce the gas distribution, even well outside cluster cores. Although their general behavior agrees more closely with the observations, simulations including cooling and star formation convert a large amount of gas into stars, which results in a low gas fraction with respect to the observations. Consequently, a detailed treatment of gas cooling, star formation, AGN feedback, and consideration of gas clumping is required to construct realistic models of the outer regions of clusters.

Eckert, D.↗

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to < 0.01 Mbp, is modeled using computer simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

DNA/radiation effects↗

Globular cluster origin of X-ray bursters

X-ray bursters and galactic bulge X-ray sources, or the most luminous X-ray sources in the Galaxy, are reasonably well constrained in their basic nature but not in their origin. It is suggested they may all have been produced by tidal capture in high density cores of globular clusters, which have now largely been disrupted by tidal stripping and shocking in the galactic plane. General arguments are presented for cluster disruption by the possible ring of giant molecular clouds in the Galaxy. Tests of the cluster disruption hypothesis are in progress and preliminary results are summarized here. The G-K star 'companions' previously noted for at least four bursters have spectra (in the two cases observed) consistent with metal-rich cluster giants. Several possibilities are discussed, including the formation of hierarchical triples in the dissolving cluster or in the galactic plane.

Grindlay, J. E.↗

Technical support for creating an artificial intelligence system for feature extraction and experimental design

Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied.

Glick, B. J.↗