Reduction methods

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  • This paper describes a simple method to reduce the radon background component applied in environmental studies using gamma spectrometry. The radon component can be reduced by introducing either nitrogen gas or clean air into the detector chamber in order to create a positive pressure and further minimize radon intrusion from outside.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Amplitude PDF Analysis of OFDM Signal Using Probabilistic PAPR Reduction Method

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  • This book presents and develops major numerical methods currently used for solving problems arising in quantitative finance. Our presentation splits into two parts. Part I is methodological, and offers a comprehensive toolkit on numerical methods and algorithms. This includes Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula functions, transform-based methods and quadrature techniques. Part II is practical, and features a number of self-contained cases.

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  • Bringing together the recent and relevant contributions of over 125 scientists from industry, government, and academia in North America and Western Europe, Alternative Toxicological Methods explores the development and validation of replacement, reduction, and refinement alternatives (the 3Rs) to animal testing. Internationally recognized scientists present what has been accomplished thus far in developing acceptable alternatives to traditional animal toxicological assessment and provide potentially new initiatives.

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  • In this paper, we propose a novel method of reducing the size of translation model for hierarchical phrase-based machine translation systems. Previous approaches try to prune infrequent entries or unreliable entries based on statistics, but cause a problem of reducing the translation coverage.

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  • Statistical machine learning methods are employed to train a Named Entity Recognizer from annotated data. Methods like Maximum Entropy and Conditional Random Fields make use of features for the training purpose. These methods tend to overfit when the available training corpus is limited especially if the number of features is large or the number of values for a feature is large. To overcome this we proposed two techniques for feature reduction based on word clustering and selection.

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  • This paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual bootstrapping, which are referred to, in a general term, as ‘collaborative bootstrapping’. The paper indicates that uncertainty reduction is an important factor for enhancing the performance of collaborative bootstrapping.

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  • (BQ) Part 2 book "Data reduction and error analysis for the physical sciences" has contents: Least-Squares fit to an arbitrary function, fitting composite curves, direct application of the maximum likelihood method, testing the fit.

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  • Lecture Research methods - Chapter 11 "Introduction to qualitative data analysis" presents the following content: What is qualitative data? goal(s) of qualitative data analysis, data reduction, content analysis, coding.

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  • Signal processing is concerned with the modelling, detection, identification and utilisation of patterns and structures in a signal process. Applications of signal processing methods include audio hifi, digital TV and radio, cellular mobile phones, voice recognition, vision, radar, sonar, geophysical exploration, medical electronics, and in general any system that is concerned with the communication or processing of information.

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  • Over the last two decades, we have witnessed an explosive growth in both the diversity of techniques and the range of applications of image processing. However, the area of color image processing is still sporadically covered, despite having become commonplace, with consumers choosing the convenience of color imaging over traditional grayscale imaging.

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  • CHAPTER 20 RELIABILITY IN MECHANICAL DESIGN B. S. Dhillon Department of Mechanical Engineering University of Ottawa Ottawa, Ontario, Canada 20.1 20.2 INTRODUCTION BASICRELIABILITY NETWORKS 20.2.1 Series Network 20.2.2 Parallel Network 20.2.3 k-out-of-n Unit Network 20.2.4 Standby System MECHANICALFAILURE MODES AND CAUSES RELIABILITY-BASED DESIGN 487 488 488 488 489 490 20.6 491 20.7 491 20.5.3 Failure Rate Modeling and Parts Count Method 20.5.4 Stress-Strength Interference Theory Approach 20.5.5 Network Reduction Method 20.5.6 Markov Modeling 20.5.

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  • State-Space Kalman Filters 7.2 Sample-Adaptive Filters y(m) e(m) µ α w(m) α z –1 wk(m+1) ADAPTIVE FILTERS 7.3 Recursive Least Square (RLS) Adaptive Filters 7.4 The Steepest-Descent Method 7.5 The LMS Filter 7.6 Summary A daptive filters are used for non-stationary signals and environments, or in applications where a sample-by-sample adaptation of a process or a low processing delay is required.

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  • A wide range of issues related to analysis of gas turbines and their engineering applications are considered in the book. Analytical and experimental methods are employed to identify failures and quantify operating conditions and efficiency of gas turbines. Gas turbine engine defect diagnostic and condition monitoring systems, operating conditions of open gas turbines, reduction of jet mixing noise, recovery of exhaust heat from gas turbines, appropriate materials and coatings, ultra micro gas turbines and applications of gas turbines are discussed.

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  • 1.1 Signals and Information 1.2 Signal Processing Methods 1.3 Applications of Digital Signal Processing 1.4 Sampling and Analog−to−Digital Conversion H E LL O ignal processing is concerned with the modelling, detection, identification and utilisation of patterns and structures in a signal process.

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  • POWER SPECTRUM AND CORRELATION Power Spectrum and Correlation Fourier Series: Representation of Periodic Signals Fourier Transform: Representation of Aperiodic Signals Non-Parametric Power Spectral Estimation Model-Based Power Spectral Estimation High Resolution Spectral Estimation Based on Subspace Eigen-Analysis Summary T he power spectrum reveals the existence, or the absence, of repetitive patterns and correlation structures in a signal process.

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  • This book proposes a framework for a national-scale program to monitor changes in mercury concentrations in the environment following the reduction of atmospheric mercury emissions. The book is the product of efforts initiated at a workshop held in Pensacola, Florida, in September 2003, involving more than 30 experts in the fields of atmospheric mercury transport and deposition, mercury cycling in terrestrial and aquatic ecosystems, and mercury bioaccumulation in aquatic food webs and wildlife.

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  • Bayesian Estimation Theory: Basic Definitions Bayesian Estimation The Estimate–Maximise Method Cramer–Rao Bound on the Minimum Estimator Variance Design of Mixture Gaussian Models Bayesian Classification Modeling the Space of a Random Process Summary B ayesian estimation is a framework for the formulation of statistical inference problems. In the prediction or estimation of a random process from a related observation signal, the Bayesian philosophy is based on combining the evidence contained in the signal with prior knowledge of the probability distribution of the process.

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  • Amines are extremely important intermediates and end products of the chemical industry and are often obtained by hydrogenation of the corresponding nitro compounds or imines. A search of the literature reveals, that hydrogenation of nitro compounds catalyzed by well-defined molecular complexes in aqueous solutions is rare.

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  • Tham khảo sách 'principal component analysis – engineering applications', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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