956 resultados para Mathematical Techniques--Error Analysis
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Mode of access: Internet.
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Cover title.
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Vol. II translated by Earle Raymond Hedrick and Otto Dunkel, published in 2 parts.
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Two different slug test field methods are conducted in wells completed in a Puget Lowland aquifer and are examined for systematic error resulting from water column displacement techniques. Slug tests using the standard slug rod and the pneumatic method were repeated on the same wells and hydraulic conductivity estimates were calculated according to Bouwer & Rice and Hvorslev before using a non-parametric statistical test for analysis. Practical considerations of performing the tests in real life settings are also considered in the method comparison. Statistical analysis indicates that the slug rod method results in up to 90% larger hydraulic conductivity values than the pneumatic method, with at least a 95% certainty that the error is method related. This confirms the existence of a slug-rod bias in a real world scenario which has previously been demonstrated by others in synthetic aquifers. In addition to more accurate values, the pneumatic method requires less field labor, less decontamination, and provides the ability to control the magnitudes of the initial displacement, making it the superior slug test procedure.
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Thesis (Ph.D.)--University of Washington, 2016-06
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The aerated stirred reactor (ASR) has been widely used in biochemical and wastewater treatment processes. The information describing how the activated sludge properties and operation conditions affect the hydrodynamics and mass transfer coefficient is missing in the literature. The aim of this study was to investigate the influence of flow regime, superficial gas velocity (U-G), power consumption unit (P/V-L), sludge loading, and apparent viscosity (pap) of activated sludge fluid on the mixing time (t(m)), gas hold-up (epsilon), and volumetric mass transfer coefficient (kLa) in an activated sludge aerated stirred column reactor (ASCR). The activated sludge fluid performed a non-Newtonian rheological behavior. The sludge loading significantly affected the fluid hydrodynamics and mass transfer. With an increase in the UG and P/V-L, the epsilon and k(L)a increased, and the t(m), decreased. The E, kLa, and tm,were influenced dramatically as the flow regime changed from homogeneous to heterogeneous patterns. The proposed mathematical models predicted the experimental results well under experimental conditions, indicating that the U-G, P/V-L, and mu(ap) had significant impact on the t(m) epsilon, and k(L)a. These models were able to give the tm, F, and kLa values with an error around +/- 8%, and always less than +/- 10%. (c) 2005 Wiley Periodicals, Inc.
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QTL detection experiments in livestock species commonly use the half-sib design. Each male is mated to a number of females, each female producing a limited number of progeny. Analysis consists of attempting to detect associations between phenotype and genotype measured on the progeny. When family sizes are limiting experimenters may wish to incorporate as much information as possible into a single analysis. However, combining information across sires is problematic because of incomplete linkage disequilibrium between the markers and the QTL in the population. This study describes formulae for obtaining MLEs via the expectation maximization (EM) algorithm for use in a multiple-trait, multiple-family analysis. A model specifying a QTL with only two alleles, and a common within sire error variance is assumed. Compared to single-family analyses, power can be improved up to fourfold with multi-family analyses. The accuracy and precision of QTL location estimates are also substantially improved. With small family sizes, the multi-family, multi-trait analyses reduce substantially, but not totally remove, biases in QTL effect estimates. In situations where multiple QTL alleles are segregating the multi-family analysis will average out the effects of the different QTL alleles.
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Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.
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This letter presents an analytical model for evaluating the Bit Error Rate (BER) of a Direct Sequence Code Division Multiple Access (DS-CDMA) system, with M-ary orthogonal modulation and noncoherent detection, employing an array antenna operating in a Nakagami fading environment. An expression of the Signal to Interference plus Noise Ratio (SINR) at the output of the receiver is derived, which allows the BER to be evaluated using a closed form expression. The analytical model is validated by comparing the obtained results with simulation results.
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An analysis of thermal degradation products evolved during the melt processing of organo-layered silicates (OLS) was carried out via the use of a solid phase microextraction (SPME) technique. Two commerical OLSs and one produced in-house were prepared for comparision. The solid phase microextraction technique proved to be a very effective technique for investigating the degradation of the OLS at a specific processing temperature. The results showed that most available OLSs will degrade under typical conditions required for the melt processing of many polymers, including thermoplastic polyurethanes. It is suggested that these degradation products may lead to changes in the structure and properties of the final polymer, particularly in thermoplastic polyurethanes, which seem significantly succeptable to the presence of these products. It is also suggested that many commercially available OLSs are produced in such a way that results in an excess of unbound organic modifier, giving rise to a greater quantity of degradation products. All OLSs where compared and characterised by TGA and GC-MS. (c) 2004 Elsevier B.V. All rights reserved.
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This paper describes a number of techniques for facilitating reflective critical analysis as a means of eliciting in-depth reflections on practice. The authors have previously used similar techniques in the research context, to assist practitioners to identify and analyse the basis of their work with clients. The techniques presented in this paper have been adapted for use in social work education, including in class-based and field education contexts, and to professional supervision.
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This paper is an expanded and more detailed version of the work [1] in which the Operator Quantum Error Correction formalism was introduced. This is a new scheme for the error correction of quantum operations that incorporates the known techniques - i.e. the standard error correction model, the method of decoherence-free subspaces, and the noiseless subsystem method - as special cases, and relies on a generalized mathematical framework for noiseless subsystems that applies to arbitrary quantum operations. We also discuss a number of examples and introduce the notion of unitarily noiseless subsystems.
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We present an application of Mathematical Morphology (MM) for the classification of astronomical objects, both for star/galaxy differentiation and galaxy morphology classification. We demonstrate that, for CCD images, 99.3 +/- 3.8% of galaxies can be separated from stars using MM, with 19.4 +/- 7.9% of the stars being misclassified. We demonstrate that, for photographic plate images, the number of galaxies correctly separated from the stars can be increased using our MM diffraction spike tool, which allows 51.0 +/- 6.0% of the high-brightness galaxies that are inseparable in current techniques to be correctly classified, with only 1.4 +/- 0.5% of the high-brightness stars contaminating the population. We demonstrate that elliptical (E) and late-type spiral (Sc-Sd) galaxies can be classified using MM with an accuracy of 91.4 +/- 7.8%. It is a method involving fewer 'free parameters' than current techniques, especially automated machine learning algorithms. The limitation of MM galaxy morphology classification based on seeing and distance is also presented. We examine various star/galaxy differentiation and galaxy morphology classification techniques commonly used today, and show that our MM techniques compare very favourably.
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Using generalized collocation techniques based on fitting functions that are trigonometric (rather than algebraic as in classical integrators), we develop a new class of multistage, one-step, variable stepsize, and variable coefficients implicit Runge-Kutta methods to solve oscillatory ODE problems. The coefficients of the methods are functions of the frequency and the stepsize. We refer to this class as trigonometric implicit Runge-Kutta (TIRK) methods. They integrate an equation exactly if its solution is a trigonometric polynomial with a known frequency. We characterize the order and A-stability of the methods and establish results similar to that of classical algebraic collocation RK methods. (c) 2006 Elsevier B.V. All rights reserved.