In this work a simple implementation of fundamental frequency estimation is presented. The algorithm is based on a frequency-domain approach. It was mainly developed for tonal sounds and it was used in Canary birdsong...
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In this work a simple implementation of fundamental frequency estimation is presented. The algorithm is based on a frequency-domain approach. It was mainly developed for tonal sounds and it was used in Canary birdsong analysis. The method was implemented but not restricted for this kind of data. It could be easily adapted for other sounds. python libraries were used to develop a code with a simple algorithm to obtain fundamental frequency. An open source code is provided in the local university repository and Github. The algorithm and the implementation are very simple and cover a set of potential applications for signal analysis. code implementation is written in python, very easy to use and modify. Present method is proposed to analyze data from sounds of Serinus canaria. (C) 2018 The Author(s). Published by Elsevier B.V.
The optimum use of safety and economy in deep excavation design is possible with the selection of the appropriate system, and modelling of the selected system and soil properties properly. Therefore, soil parameters s...
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The optimum use of safety and economy in deep excavation design is possible with the selection of the appropriate system, and modelling of the selected system and soil properties properly. Therefore, soil parameters selection has a crucial effect in deep excavation analyses. The realistic estimation of the displacements with the finite element software is only possible by using the realistic deformation modulus values during analyses. However, in stiff clays for which undisturbed sampling is very difficult, displacements calculated with laboratory deformation modulus parameters may be higher than the measured values. Objective of this study is to determine the constant that shows linear relationship between SPT-N and deformation modulus parameter of Ankara clay by using three constitutive soil model of Plaxis-2D, namely Mohr-Coulomb (MC), hardening soil model (HS) and hardening soil model with small strain stiffness (HSsmall). For this purpose, back analysis of a 25 m deep excavation was performed by using inclinometer measurement results. To be more precise in numerical analysis, instead of using the idealized soil profile the soil is divided into layers according to SPT-N 60 measurements. Additionally, each displacement measured by the inclinometer along the depth is compared with the analysis results to minimize the error. In case trial-error method is used in the study, time loss and the possibility of not reaching the correct result were taken into consideration; therefore, the analysis was done by writing a python code. As a result of analyses, the soil models were compared with each other and it is concluded that displacements curves obtained from the MC model could not converge to the real displacements. HSsmall model results are closest to the real displacements. Moreover, displacement curves obtained from HS and HSsmall models are very close to each other, and the linear correlation formula is determined as E 50 ref =780xN 60 kPa for this excavation of the
An algorithm for solving steady-state heat conduction problems in arbitrarily complex composite walls is presented. Per se, steady-state heat conduction across a wall can easily be solved by hand. Yet, in practical ap...
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An algorithm for solving steady-state heat conduction problems in arbitrarily complex composite walls is presented. Per se, steady-state heat conduction across a wall can easily be solved by hand. Yet, in practical applications the wall structure is often complex enough to deter such an approach if a finer yet simple analysis of the thermal bridges is of interest. Moreover, if high-temperature applications are involved, the additional complexity of including time-dependent thermal conductivity must be considered. Thus, a general methodology for solving arbitrary topology walls, involving any kind of thermal resistances in series and in parallel is discussed. While such a problem is formally simple to solve for a given wall following the theory, its algorithmic generalization is not. A method is provided, involving a program written in python language. The focus of the work is mainly on the algorithmic point of view: a simple way for the assessment of the wall topology and for the resolution of the heat conduction problem originating is sought. Temperature-dependent thermal conductivity of the materials is addressed, resulting in the need of evaluating the heat fluxes and the average temperature at each thermal resistance. (C) 2017 Elsevier Ltd. All rights reserved.
Transfer nodes are essential elements of public transport systems which provide door-to-door transport services for passengers. Parameters of the technological processes in public transport systems are stochastic vari...
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Transfer nodes are essential elements of public transport systems which provide door-to-door transport services for passengers. Parameters of the technological processes in public transport systems are stochastic variables, thus, computer simulations are usually used for solving optimization problems of public transport. There is a number of simulation tools supporting decision-making in public transportation, but they don't provide the flexibility for solving the transfer nodes optimization problems. Authors present a library of classes implemented in python, which could be used for computer simulations of public transfer nodes. The proposed software allows researchers to change technological parameters during simulation procedures and makes possible automatization of simulation experiments in the field of passengers' transportation. (C) 2017 The Authors. Published by Elsevier Ltd.
Transfer nodes are essential elements of public transport systems which provide door-to-door transport services for passengers. Parameters of the technological processes in public transport systems are stochastic vari...
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Transfer nodes are essential elements of public transport systems which provide door-to-door transport services for passengers. Parameters of the technological processes in public transport systems are stochastic variables, thus, computer simulations are usually used for solving optimization problems of public transport. There is a number of simulation tools supporting decision-making in public transportation, but they don’t provide the flexibility for solving the transfer nodes optimization problems. Authors present a library of classes implemented in python, which could be used for computer simulations of public transfer nodes. The proposed software allows researchers to change technological parameters during simulation procedures and makes possible automatization of simulation experiments in the field of passengers’ transportation.
We discuss a few simple modifications to time-dependent density matrix renormalization group (DMRG) algorithms which allow to access larger time scales. We specifically aim at beginners and present practical aspects o...
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We discuss a few simple modifications to time-dependent density matrix renormalization group (DMRG) algorithms which allow to access larger time scales. We specifically aim at beginners and present practical aspects of how to implement these modifications within any standard matrix product state (MPS) based formulation of the method. Most importantly, we show how to 'combine' the Schrodinger and Heisenberg time evolutions of arbitrary pure states vertical bar psi > and operators A in the evaluation of < A >(psi)(t) = . This includes quantum quenches. The generalization to (non-)thermal mixed state dynamics < A >(rho),(t) = Tr[rho A(t)] induced by an initial density matrix rho is straightforward. In the context of linear response (ground state or finite temperature T > 0) correlation functions, one can extend the simulation time by a factor of two by 'exploiting time translation invariance', which is efficiently implementable within MPS DMRG. We present a simple analytic argument for why a recently-introduced disentangler succeeds in reducing the effort of time-dependent simulations at T > 0. Finally, we advocate the python programming language as an elegant option for beginners to set up a DMRG code. (C) 2015 Elsevier B.V. All rights reserved.
As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of ce...
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ISBN:
(数字)9781400848911
ISBN:
(纸本)9780691151687
As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. The book presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest.
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