Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

The Spiral Structure of the Milky Way, Cosmic Rays, and Ice Age Epochs on Earth

The short term variability of the Galactic cosmic ray flux (CRF) reaching Earth has been previously associated with variations in the global low altitude cloud cover. This CRF variability arises from changes in the solar wind strength. However, cosmic ray variability also arises intrinsically from variable activity of and motion through the Milky Way. Thus, if indeed the CRF climate connection is real, the increased CRF witnessed while crossing the spiral arms could be responsible for a larger global cloud cover and a reduced temperature, thereby facilitating the occurrences of ice ages. This picture has been recently shown to be supported by various data (Shaviv, 2001). In particular, the variable CRF recorded in Iron meteorites appears to vary synchronously with the appearance ice ages. Here we expand upon the original treatment with a more thorough analysis and more supporting evidence. In particular, we discuss the cosmic ray diffusion model which considers the motion of the Galactic spiral arms. We also elaborate on the structure and dynamics of the Milky Way's spiral arms. In particular, we bring forth new argumentation using HI observations which imply that the galactic spiral arm pattern speed appears to be that which fits the glaciation period and the cosmic-ray flux record extracted from Iron meteorites. In addition, we show that apparent peaks in the star formation rate history, as deduced by several authors, coincides with particularly icy epochs, while the long period of 1 to 2 Gyr before present, during which no glaciations are known to have occurred, coincides with a significant paucity in the past star formation rate.Comment: 33 pages, 11 figures. To Appear in New Astronom

The Giant Gemini GMOS survey of zem > 4.4 quasars – I. Measuring the mean free path across cosmic time

We have obtained spectra of 163 quasars at zem > 4.4 with the Gemini Multi Object Spectrometers, the largest publicly available sample of high-quality, low-resolution spectra at these redshifts. From this data set, we generated stacked quasar spectra in three redshift intervals at z ∼ 5 to model the average rest-frame Lyman continuum flux and to assess the mean free path λ912mfp of the intergalactic medium to H I-ionizing radiation. At mean redshifts zq = (4.56, 4.86, 5.16), we measure λ912mfp=(22.2±2.3,15.1±1.8,10.3±1.6)h−170 proper Mpc with uncertainties dominated by sample variance. Combining our results with measurements from lower redshifts, the data are well modelled by a power law λ912mfp=A[(1+z)/5]η with A=(37±2)h−170 Mpc and η = −5.4 ± 0.4 at 2.3 < z < 5.5. This rapid evolution requires a physical mechanism – beyond cosmological expansion – which reduces the effective Lyman limit opacity. We speculate that the majority of H I Lyman limit opacity manifests in gas outside galactic dark matter haloes, tracing large-scale structures (e.g. filaments) whose average density and neutral fraction decreases with cosmic time. Our measurements of the mean free path shortly after H I reionization serve as a valuable boundary condition for numerical models thereof. Our measured λ912mfp≈10 Mpc at z = 5.2 confirms that the intergalactic medium is highly ionized without evidence for a break that would indicate a recent end to H I reionization

Evaluation and improvement of energy flexibility and performance of building heating, ventilation, and air-conditioning systems

The foreseen reduction of available fossil fuels, the continued increase in global energy demand, and the irrefutable evidence of climate change, along with the implementation of a global commitment to achieve a net-zero emissions target, have greatly sharpened commercial interest in using renewable energy resources (RER). However, the high penetration of RER-based stochastic power generation systems has resulted in a significant requirement for increased flexibility on the demand side that can allow buildings to adapt to increasingly dynamic energy supply conditions to support power grid operation and optimization. Failure to adapt may carry serious electrical blackouts and can compromise the safety of the supply side. The building sector accounts for a substantial amount of global energy usage and offers great opportunities for energy flexibility. Building energy flexibility is an important and emerging concept in the modern energy landscape, which can support the sustainable transition of the power sector. Building heating, ventilation, and air-conditioning (HVAC) systems are one of the leading energy consumers in buildings, which can be used as a key flexible source. The HVAC systems with integrated thermal energy storage (TES) can further enhance building energy flexibility. This thesis contributes to the evolving field of demand flexibility and introduces methodologies to evaluate and improve energy flexibility and performance of building HVAC systems

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection